The use of robots in various industries is evolving from mechanization to intelligence and precision. These systems often comprise parts made of different materials and thus require accurate and comprehensive target identification. While humans perceive the world through a highly diverse perceptual system and can rapidly identify deformable objects through vision and touch to prevent slipping or excessive deformation during grasping, robot recognition technology mainly relies on visual sensors, which lack critical information such as object material, leading to incomplete cognition. Therefore, multimodal information fusion is believed to be key to the development of robot recognition. Firstly, a method of converting tactile sequences to images is proposed to deal with the obstacles of information exchange between different modalities for vision and touch, which overcomes the problems of the noise and instability of tactile data. Subsequently, a visual-tactile fusion network framework based on an adaptive dropout algorithm is constructed, together with an optimal joint mechanism between visual information and tactile information established, to solve the problem of mutual exclusion or unbalanced fusion in traditional fusion methods. Finally, experiments show that the proposed method effectively improves robot recognition ability, and the classification accuracy is as high as 99.3%. The growing individualization of products demands facilities that can manufacture small batch sizes with little effort. Autonomous robots can help increase the required flexibility. The reliable task execution is required for similar but different product variants. The Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) has developed an autonomous robot assembly system for flexible manufacturing . Here, n refers to the number of tactile sensors, while Representative time sequence recognition networks such as RNN and LSTM mainly focus on natural language processing and speech recognition, but there is no common 1D recognition network framework for object recognition. Therefore, we propose a method to convert the 1D tactile sequences from the tactile sensor array on a multi-fingered dexterous hand into a 2D image model.An image is typically represented as a matrix in a computer, with the pixel being the most basic unit for digital image processing. Therefore, the elements that constitute an image are its size and pixel values. By considering the number of sensors in the tactile sensor array as the image's width, the number of consecutive acquisitions as its height, and the feedback value collected from each sensor as the pixel value, the tactile image can be expressed using Formula (2).Where, m refers to the acquisition frequency, while n refers to the number of tactile sensors. The tactile sensor array we have built consists of 10 tactile sensors, which means the width of the tactile image is ten. The acquisition frequency of the tactile sensor is 20 ms. Taking 50 consecutive groups of tactile sensor data and treating them as a group, the image height is set to 50. The processed image is visualized as shown in We have collected pressure feedback values from the tactile sensors on the fingers while grasping an object with the dexterous hand. For a given object, the variation of tactile data should ideally lie within a specific range. However, external factors such as unstable finger output force and object sliding can introduce noise into the tactile data. To mitigate the effects of such noise on the data, it is necessary to apply a filtering technique.While conventional median filters use a fixed window size, they are often unable to balance between preserving image details and denoising efficiently. To address this limitation, we propose an adaptive median filter that utilizes the sample entropy factor, as illustrated in The sample entropy is a method for measuring the complexity of time sequences first proposed by Richman et al. . It can The image matrix is divided into columns to obtain n column vectors, as shown in formula (3).Each column vector corresponds to the time sequences collected by a single sensor, as shown in formula (4).Next, the sample entropy is calculated for each column vector. For any column vector, a threshold r representing the similarity comparison is specified, and a metric m for dividing the length of the subsequences is determined. (N–m+1) subsequences can be obtained by reconstructing the original sequences, as shown in (5).Each subsequence is represented by uv between any two reconstructed vectors The distance DThen, formula (8) is used to count the number of vectors that meet the following conditions.Formula (9) is used to calculate the ratio , representing the similarity between the subsequences and the original sequences.The similarity at m+1 is calculated according to steps (5–9) above, and the sample entropy is calculated according to formula (10).Repeating steps (5–10) above to calculate the sample entropy of the time sequences for all column vectors, we finally obtain the sample entropy vector shown in (11).The above is the calculation progress of sample entropy, in which m is usually set as 2; r is usually set as 0.25 × std, where std refers to the standard deviation of the original time sequences  is shown in (13).Where, The final denoised time sequences are shown in (14). The processing of the data filtering can more accurately reflect the object characteristics and improve the recognition accuracy.In this section, we discuss our visual and tactile fusion framework in detail. While visual images are useful for representing global information such as object shape, size, and contour, touch provides local information related to properties such as softness, hardness, and material. To ensure that both global and local details are taken into consideration when performing feature expression, we propose a comprehensive visual-tactile fusion network model based on the adaptive dropout algorithm. This approach helps to preserve the structural information of visual images while effectively capturing fine-grained features with discrimination and adjusting the weight of visual and tactile information based on sample quality, leading to significantly enhanced object recognition accuracy.To achieve feature fusion, we propose a visual-tactile feature fusion network, as illustrated in The multi-branch CNN feature extraction module consists of two different sub-modules. The first sub-module is the visual feature network, which takes visual images collected by the camera as input. This sub-module has a network structure consisting of three convolutional layers and three pooling layers. The second sub-module is the tactile feature network, which has a simpler network structure due to the small size of the tactile image. It comprises two convolutional layers and two pooling layers, with network composition and parameters shown in the accompanying table. The output layer features of the two sub-modules are then concatenated to obtain visual-tactile fusion features, which are processed through the Softmax layer to produce the classification results.The dropout layer is included as part of the fusion layer, which is described in the next section. A fusion algorithm is designed to combine visual features and tactile features in the fusion layer, resulting in the VT features. The fusion formula is shown in (15).Iv refers to the input visual image, and It refers to the input tactile image. V refers to the visual neural network mapping function, and the visual features are obtained by feature extraction from the visual image. T refers to the visual neural network mapping function, and the tactile features are obtained by feature extraction from the tactile image. ⊕ refers to the feature splicing operation, and the fused VT feature is obtained by splicing the visual and tactile features. The visual and tactile features need to be regularized separately before fusion.Where Under the influence of factors such as the quality of the collected data and the object material, visual data and tactile data reflect the true attributes of the object differently. For instance, visual samples can more effectively express the characteristics of objects with prominent colors, while tactile data is more representative of deformable objects. Additionally, visual data reliability decreases when objects are partially occluded, and tactile data is susceptible to noise when grasping is unstable. Therefore, it is essential to control the degree to which visual and tactile data impact the recognition results during the process of visual-tactile fusion. To achieve this, we propose an adaptive dropout model based on sample quality, illustrated in i(t) refers to the connection strength of a single neuron in the network, with the calculation formula shown in (17). wij(t) refers to the weight between neurons i and j in the t-th iteration, and πij(t) refers to the activation state of any neuron connected to neuron i in the network, with the value set as 0 or 1, 1 for active, and 0 for not active.The dropout parameter p is determined by discriminating network output and neuron connection strength with the formula (16). Where, p. The dropout algorithm flow is depicted in The Bernoulli method -dimensional vector to a C-dimensional vector through three fully connected layers. The C-dimensional vector outputs the probability of the corresponding category by the Softmax classification layer. The entire network is a complete end-to-end system with all parameters jointly trained by backpropagation, and our network is trained by standard backpropagation.The Xavier Kumar,  initialiThe training process is as follows:First, the visual sample evaluation network and the tactile sample evaluation network are trained.The CNN network weights trained in step (1) are shared and the fusion network is trained on this basis.The experimental platform consists of a three-fingered dexterous hand, an array of tactile sensor, a Kinect2 camera, and a robot arm of the UR5 robot. The UR5 robot arm is placed on the left side of the table, on which we have assembled the three-fingered dexterous hand with 10 tactile sensors. The dexterous hand is JODELL's JQ3-5, and tactile sensors are the thick-film piezoresistive sensors. There is a Kinect2 camera on the right side of the table to capture the object images. The recognition procedure of the robot is as follows: Firstly, the visual sensor detects the grasping posture , medium (H = 0), and hard (H = 1). There are 27 different types of objects. Extensive experiments were conducted to collect visual and tactile data on each object, with 50 images captured for each object under varying shooting conditions, including different occlusion areas. The tactile sensor sequences obtained during the stable grasping stage were chosen as the tactile data. Each object was recorded 50 times, and each tactile sequence was divided into 50 sample points. Then, 40 visual images and 40 tactile sequences were randomly selected from each object for training, with the remaining samples reserved for testing. All samples were obtained under successful grasping conditions. In addition to conventional category labels, the image qualities of visual and tactile samples were classified into three levels: good (Q = 1), medium (Q = 0), and poor (Q = −1). The tactile data was also divided into three levels based on time entropy: good (Q = 1), medium (Q = 0), and poor (Q = −1).Some visual and tactile samples are shown in The first challenge in achieving the fusion of visual and tactile data is the inconsistency between the modalities of these two types of data. To tackle this issue, this paper proposes a modality conversion method that converts tactile data into tactile images. To verify the feasibility of this approach, we compared the recognition results of an LSTM network that used the original tactile data and a CNN network that used the modality-converted tactile images. The CNN network comprises the tactile CNN part and the fully connected layer (excluding the VT layer) shown in Although the network frameworks of the two groups of experiments were quite different, the purpose was not to verify network quality but to evaluate the effectiveness of tactile images. According to the experimental results in It was also found that both networks fluctuated during training due to considerable interference in the tactile data acquisition process and noise in the data. To verify the denoising ability of the filtering algorithm proposed in this paper for tactile data, we conducted a comparative experiment on two different modality frameworks after filtering the original tactile data, as shown in It is evident from the data in Once the feasibility of the tactile modality conversion was verified, experiments on visual-tactile fusion were conducted to evaluate the performance of our visual-tactile fusion network. Three groups of comparative experiments were conducted, including visual recognition, tactile recognition, and visual-tactile fusion recognition. The visual network comprises the visual CNN part and the last three layers of the fully connected layer of the fusion network. Similarly, the tactile network refers to the tactile CNN part and the last three layers of the fully connected layer. Three indicators, including class accuracy, material property accuracy, and model size, were used to evaluate the performance of different algorithms. For example, the class of a ripe banana is “banana,” and the material property is “soft.” The data used in the three network models were all from our visual and tactile data sets. It was expected that the recognition algorithm could recognize more detailed material attributes of objects  simultaneously as it recognizes the object category.The experimental results in To further demonstrate the advantages of visual-tactile fusion, experiments were conducted to test the recognition ability of different algorithms in different object visibility scenarios by occluding water bottles. A total of 300 groups of experimental data were collected and tested, and the recognition rate of the object material attributes was evaluated with different algorithms using the samples shown in The occlusion experiments showed that object occlusion had the most significant impact on the visual recognition algorithm, and the recognition accuracy of the visual algorithm dropped to 86.7 and 61.7%, respectively. It was challenging for the visual algorithms to extract complete and effective object features when the object was occluded, leading to recognition failures. However, the tactile algorithm only relied on the force feedback after contact between the finger and the object to identify the object, so occlusion did not affect the tactile algorithm's performance. Notably, occlusion had no noticeable impact on the visual-tactile fusion algorithm. There was no sharp drop in the accuracy of the visual-tactile fusion algorithm as observed with the visual algorithm. The visual-tactile fusion algorithm was equipped with a dropout mechanism, which allocated weights of the two types of data during fusion. When there was an occlusion in the visual image, the visual image quality discrimination network in the fusion algorithm could detect the image's low quality. The dropout probability of the fully connected layer of the visual part in the fusion layer was relatively high, which reduced the weight in the recognition, thus maintaining the recognition algorithm's accuracy at a high level.In the water quantity attribute recognition experiment, the performance of the visual detection algorithm was found to be inadequate, with poor accuracy. However, the tactile detection algorithm demonstrated an impressive accuracy of 96.7%. Moreover, our visual-tactile fusion algorithm achieved an accuracy of 95.0%, which was not significantly affected by incomplete or unreliable visual data. These results demonstrate that our fusion algorithm is capable of effectively integrating visual and tactile features, leveraging the strengths of each modality and surpassing the performance of any standalone model. When faced with incomplete or unreliable feature information, the fusion algorithm adapts intelligently by adjusting the weights of the features and optimizing the classifier.In order to validate the effectiveness of the proposed tactile image filtering algorithm and dropout algorithm in improving algorithm performance, we conducted four groups of comparative experiments by introducing each algorithm sequentially into the fusion algorithm with variable controls. The results of the tests are presented in From the experimental results, it is evident that both the tactile image filtering algorithm and the dropout algorithm positively impact the model's recognition performance. Comparison between the first and second groups of experiments shows that the filtering algorithm improves the model's material recognition performance. This is because the filtering algorithm enhances the quality of the tactile data, which is crucial for recognizing material properties that rely heavily on tactile features. In contrast, the experiments comparing the first and third groups demonstrate that the dropout algorithm significantly improves the model's overall performance, with a more pronounced improvement effect than the filtering algorithm.Overall, the results of the comparative experiments confirm the effectiveness of the proposed visual-tactile fusion algorithm in robot object recognition tasks. Additionally, comparing the visual class accuracy rate of 93.0%, visual material property accuracy rate of 70.4%, and tactile class accuracy rate of 82.6% in However, the material property accuracy rate of 91.5% is lower than the tactile accuracy rate of 94.8%, demonstrating the potential for two different information types to impact each other during the fusion process. The proposed dropout mechanism effectively reduces the negative impact of poor-quality samples on recognition results.This paper proposes a visual-tactile fusion perception object recognition network VTN for robot recognition and grasping. The establishment of the filtering tactile images and the adaptive dropout algorithm provides a new optimization scheme for visual-tactile fusion perception tasks. Furthermore, it is demonstrated that tactile and visual signals are complementary, and combining data from two modalities can improve performance. The proposed model realizes the correlation between different modality information of vision and touch and provides an intelligent fusion mechanism for the fusion network. This paper has established data sets for training and testing with many recognition experiments, and the experimental results demonstrate the effectiveness and high accuracy of the proposed model. In the future, our model can be improved with more diverse data sets, and richer data will allow our network to identify more refined object attributes.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.ZD and GC conceived the study and put forward the methodology. ZD and ZW performed the data collection and pre-processing. GC and LS reviewed and edited the manuscript. All authors read and agreed to the published version of the manuscript.


The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms. However, there are many variants of the ICA methods and it is not clear which one is the most suitable for this task. The goal of this study is to test and objectively evaluate 11 variants of ICA methods combined with an adaptive fast transversal filter (FTF) for the purpose of extracting the NI-fECG. The methods were tested on two datasets, Labour dataset and Pregnancy dataset, which contained real records obtained during clinical practice. The efficiency of the methods was evaluated from the perspective of determining the accuracy of detection of QRS complexes through the parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). The best results were achieved with a combination of FastICA and FTF, which yielded mean values of ACC = 83.72%, SE = 92.13%, PPV = 90.16%, and F1 = 91.14%. Time of calculation was also taken into consideration in the methods. Although FastICA was ranked to be the sixth fastest with its mean computation time of 0.452 s, it had the best ratio of performance and speed. The combination of FastICA and adaptive FTF filter turned out to be very promising. In addition, such device would require signals acquired from the abdominal area only; no need to acquire reference signal from the mother’s chest. Fetal electrocardiography (fECG) is a method based on sensing electrical activity of fetal heart in the form of electric potentials. The fECG recording provides clinically important information about the fetus’ medical condition and can be used timely to identify congenital heart defects , but most importantly to timely identify fetal hypoxia , 2.Reduced oxygen and blood supply to fetal brain can cause hypoxic-ischemic encephalopathy (HIE), resulting in long-term neurological disorders like mental impairment and cerebral palsy . AccuratOne of these methods is fECG, which is able to provide additional information about the fetal health state besides fHR, which is determined using the most prominent peaks of the ECG waveform, the R waves . With fEMonitoring with fECG can be done by invasive sensing with a scalp electrode or by non-invasive sensing on the surface of the mother’s abdomen. The invasive variant yields fECG signal with very high quality, there is however a risk of introducing an infection into the uterus, and the variant as such may be carried out only during delivery, after the rupture of membranes. The non-invasive variant is much safer compared to the invasive fECG and may be used both during pregnancy and during delivery , 5. Moren denotes the number of samples of each vector.The disadvantage of the non-invasive variant is that the ECG acquired through abdomen (aECG) is a mix of useful signals, fECG, maternal ECG (mECG), and noise  , see Eq In addition, the mECG amplitude is usually higher than the fECG amplitude and the mECG signal has almost identical range from the perspective of time and frequency as the fECG and its elimination requires advanced extraction algorithms or combinations thereof , 16.hybrid systems. The aim of this article is to identify and test the most promising methods for fECG extraction in terms of the performance, implementability and computational speed.The following section includes a review of the state-of-the-art methods for fECG extraction, with particular focus on the Various algorithms for fECG extraction were tested in the past, among which are techniques based on blind source separation (BSS) , waveletIn general, there are two approaches to acquiring aECG and mECG signal in a non-invasive manner. The first approach is based on sensing aECG signal from abdominal area of the mother, which is used as the primary input, while a reference mECG signal is acquired from the mother’s chest area. However, the mECG signal acquired from the chest has different morphology than the maternal component of the aECG signal and is usually of poor quality and the extraction using the adaptive algorithm is therefore less effective. The measurement process is also less comfortable for the mother and reduces her mobility.Therefore, we used the second approach to acquire the aECG and mECG input signals. It is based on sensing aECG signal only and on BSS algorithm, which is able to reliably estimate mECG and aECG signal with enhanced fetal component (marked aECG*) from a mix of abdominal signals , 16. BotSince the performance of the adaptive algorithm depends on the quality of input signals, selecting a suitable BSS algorithm is important and has a direct effect on the quality of the estimated aECG* and mECG. Most BSS methods use second- and higher-order statistics, while the source signals and the mixing process are unknown. There are various BSS-based approaches which were tested in the past for fECG extraction. Among those are the most commonly used independent component analysis (ICA) , 22–25, A comparison of effectiveness of the kurtosis-based FastICA method, negentropy-based FastICA, joint approximate diagonalization of eigenmatrix (JADE) algorithm and PCA for extracting fECG were presented in . The resPeriodic component analysis has been designed and tested for fECG extraction in . No statIn , the autA comparison of ICA-based methods: second-order blind identification (SOBI), algorithm for extraction of multiple unknown sources (AMUSE) and eigenvalue decomposition was carried out in . Real reA combination of ICA and PCA method was tested on real records in . The ICAThe NMF method was designed for fECG extraction in  and testNMF was tested in combination with the EMD method by the authors in . The EMDBased on the conducted review of the state-of-the-art BSS methods used in fetal ECG extraction, the ICA-based methods appeared to be the most prevalent and also most promising for NI-fECG processing, at least as one of its steps. Often, these algorithms are combined with other methods, such adaptive algorithms , 16. HowThe goal of this study is therefore to test and objectively evaluate eleven ICA methods in terms of their performance and computational speed. The tests will include following ICA variants: AMUSE algorithm, equivariant robust ICA algorithm (ERICA), FastICA algorithm, flexible ICA algorithm (FlexICA), logistic infomax ICA algorithm (Infomax), JADE, kernel ICA algorithm (KICA), robust accurate direct ICA algorithm (RADICAL), robust ICA algorithm (RobustICA), simultaneous blind signal extraction using cumulants (SIMBEC), SOBI algorithm.This chapter provides basic description of the tested ICA-based method and adaptive FTF algorithm. The real datasets used for objective evaluation and validation of function of the applied algorithms will be described later in this chapter.Independent component analysis is a method that aims to find linear representation of non-Gaussian data so that the components are statistically independent or as independent as possible. Such representation captures the basic structure of data in many applications, including the extraction of functions and signal separation .Before applying the ICA method to aECG input signals, it is often wise to pre-process the signals by centering and whitening. After centering, input signals have zero mean value, whereas whitening makes them discorrelated with the unit variance .A is unknown; therefore, the goal of ICA method is to estimate the inverse of demixing matrix W. W. Each ICA-based method differs mainly in the manner of making the matrix estimate [For basic motivation, consider a signal sensed from an abdominal area of a pregnant woman (aECG) with multiple signal sources, such as fECG, mECG, electrical activity of the uterus and other muscles (manifesting as electrohysterogram (EHG) and electromyogram (EMG), respectively) and noise caused by the ambient environment. For further description, these sources are defined as estimate .x→(t)=AThere are many different ICA-based methods. In this study, the methods described below were used for the experiment :AMUSE algorithm is using eigenvalue value decomposition. This is an algorithm of blind separation of second-order sources, which uses the structure in data to find non-correlating components. To do so, it decomposes singular values on a shifted cross-covariance matrix. The shift is selected so that the auto-correlation of sources in these shifts are non-zero and as different from each other as possible. The AMUSE algorithm consists of several steps. In the first step, data are gathered and a covariance matrix is estimated. Then follows a decomposition of singular values of the covariance matrix, estimation of number of sources, and dispersion of noise. Data are then transformed. The next step is eigenvalue/eigenvector decomposition. The source signal is estimated, followed by estimating the actual channel parameter matrix. More information and mathematical description could be found in [found in .ERICA algorithm separates signals with non-zero kurtosis from mixed signals in the present of Gaussian noise. This algorithm is a quasi-Newton iteration, which will converge to saddle point with local isotropic convergence, regardless of source distribution. Whitening is not necessary for the algorithm to converge. The algorithm uses an iteration algorithm using a mixing matrix estimate, learning rate, and a matrix of fourth-order cross-cumulants for identifying the unknown of the mixing matrix. Demixing matrix is then computed as a pseudo-inverted matrix A. The iteration process continues until the selected convergence rate is achieved. More information and mathematical description can be found in [found in .FastICA algorithm is currently the most commonly used type of the ICA method. It is built on a fixed iteration plan for finding the maximum data values that do not originate from normal data distribution. This can be achieved by using the approximation of Newton iteration. Pre-processing by centering and whitening must be done before applying the FastICA algorithm. Convergence criterion and maximum number of iterations must be set. The goal of the convergence is to achieve practically zero scalar product between the old and new vector values. First, random normed initial weights of the vector are created. A new vector is then calculated using the kurtosis and negentropy. This is followed by norming and checking whether the scalar product of the new vector and the initial weights vector is lower than the selected convergence criterion. If that is not the case, the second and third step of the FastICA method is repeated until the convergence criterion condition is satisfied or the selected number of iterations is exceeded [exceeded .FlexICA algorithm is a learning algorithm with flexible linearity. This algorithm relies on hypothesized density functions since the probability density functions of source signals are unknown. It uses generalized Gaussian density, allowing approximation of all unimodal distributions. Simply speaking, FlexICA uses a combination of kurtosis and Gaussian exponentials to select the correct value of Gaussian exponentials. It can be defined as follows. As the first step, dimension is reduced by whitening. Then follows a search for orthogonality factor for minimization of common information in the whitened vector. This factor is then used to estimate the sought matrix W. More information and mathematical description can be found in [found in .Infomax algorithm is based on maximizing the entropy and represents a natural gradient form for computing independent components. It can be considered as a neural network learning method. Infomax algorithm uses annealing based on weight changes to automate the separation process. The algorithm basically maps input values to output values so that the mean information between the input and output values is maximized. Learning rate function and function related to the nature of distribution are selected during the computation (i.e. super-Gaussian or sub-Gaussian). The output is a demixing matrix that is inverted mixing matrix. More information and mathematical description can be found in [found in .JADE algorithm is based on common diagonalization of cumulation matrices. This algorithm is highly effective for separating low number of sources. Output components are separated by exploiting fourth-order moments, where orthogonal rotation of input signal is searched for the purpose of estimating source signals with high kurtosis. Firstly, the JADE algorithm estimates a whitening matrix. This is followed by estimating a maximum set of cumulation matrices. Orthogonal contrast is optimized by finding a rotation matrix so that the cumulation matrices are as diagonal as possible. Finally, the mixing matrix or direct output components  are estimated. More information and mathematical description can be found in [found in .KICA algorithm is based on maximizing the kurtosis or, to be more precise, on optimization of kurtosis-based cost function. This cost function is identical to the function applied in the FastICA algorithm. The first step is centering and whitening of data. This is followed by initialization of a separation matrix and setting the required parameters. A rotation matrix is then created, followed by a computation of estimate component output and update of the demixing matrix. The components are estimated and the demixing matrix is updated by iterations. More information and mathematical description can be found in [found in .RADICAL algorithm solves the ICA problem in arbitrary dimension. The RADICAL algorithm extracts independent signal sources using a differential entropy estimate based on spacing estimator. It is a consistent, asymptotically efficient, and computationally undemanding algorithm. The goal of the RADICAL algorithm is to minimize the contrast function using the Jacobean matrix, incorporated into the rotation matrix. The resulting rotation matrix contains all Jacobi rotations for all rotated pairs. This process is done for all sources estimated using the demixing matrix. The data must be whitened before applying the RADICAL. The advantage is its direct optimization of the statistic independence rate, absence of estimate of probability density and that it carries out one-dimensional entropy estimation, which converges quickly and avoids remote values. More information and mathematical description can be found in [found in .Robust ICA algorithm is based on exact line search of optimal kurtosis. In this algorithm, the step size is optimized for each iteration in order to achieve kurtosis maximization and reduction of computational complexity. It is a simple modification of FastICA algorithm, where exact line search is in contract of the kurtosis. Several steps are taken during each iteration of optimal step-size optimization. Optimal step-size polynomial coefficients are computed in the first step, followed by extraction of step-size polynomial roots. In the next step, root leading to the absolute maximum is selected. The demixing matrix is then updated and normalization is performed. More information and mathematical description can be found in [found in .SIMBEC algorithm uses simultaneous extraction of independent components that are present in the given data. The algorithm optimizes the maximum credibility criterion using a gradient algorithm on a Stiefel model. It uses natural gradient rise of Stiefel model to concurrently extract sources using a contrast function based on higher-order cumulants with a rate of learning that provides quick convergence. The algorithm is described by the following procedure. First, a separation matrix is computed. This is followed by a computation of cross-cumulant matrix. Finally, the searched source signals are estimated. More information and mathematical description can be found in [found in .SOBI algorithm is based on second-order statistics for use of time-correlation structure to estimate of the original signals. The primary concept of SOBI is an assumption of diagonal form of delayed correlation matrices. This allows approximating the original signals. The input signals are whitened first, followed by sample covariance using input data and diagonalization. Noise power is then estimated by averaging the smallest eigenvalues in whitening matrix. The next step is to estimate unitary matrix in the joint diagonalization. Finally, the resulting matrix A is computed and source signals are estimated. More information and mathematical description can be found in [found in .The FTF is an adaptive filter that automatically modified filter coefficients so that the filter converges to optimal condition. This condition is achieved by minimizing the error signal between the adaptive filter output and the required signal . In thisIn case of FTF algorithm , the error function is optimized by a deterministic approach. The main benefit of the FTF filter is its comparable performance with the recursive least squares method, while having shorter computation times . This is achieved by implementing four filters  operating together on a single task. However, this algorithm is burdened with instability in a finite precision environment. Description of this algorithm is rather comprehensive; see  for moreAll records of publicly available datasets (Labour and Pregnancy) were used for the experiments. The study protocol was approved by the Ethical Committee of the Silesian Medical University, Katowice, Poland (NN-013–345/02). Subjects read the approved consent form and gave written informed consent to participate in the study. We decided not to include other datasets because there are currently no sufficiently high-quality datasets on which it would be possible to objectively evaluate the quality of fECG extraction. Another reason is the need for expert-verified reference annotations so that the data can be properly validated. The selection of datasets was conditioned by the following criteria:Dataset including real data—the use of synthetic data for the experiments is not suitable because the algorithms often tend to work effectively, but when it is applied to real data, the algorithm often fails. Synthetically generated signals are therefore only suitable for initial experiments, not for the validation phase.Availability of the fQRS annotations—for an objective evaluation of the effectiveness of the algorithm, it is necessary to evaluate the correctness of determining the positions of fQRS complexes and estimating fHR. Without reference positions verified by experts, it is not possible to carry out objective assessment , and the effectiveness of the filtration can then only be evaluated subjectively, which is not very accurate.Sufficient length and diversity of the recordings—it is also important to use sufficiently long recordings (at least five minutes long) for testing. Testing only a short section of the signal can lead to distorted results. It is also important to include recordings of fetuses of different gestational ages, different ratios of mQRS:fQRS amplitudes and with different types and levels of interference. In addition to physiological records, it would be appropriate to use pathological records as well.figshare repository. All dataset information were acquired from [Although the availability of just such datasets is generally the biggest obstacle when testing algorithms for fECG extraction, the Labour and Pregnancy datasets come closest to the aforementioned requirements. Other publicly available datasets either contain synthetic signals, do not contain the reference positions of fQRS complexes, or contain very short and low-quality signals. All signals from Labour and Pregnancy dataset were acquired under clinical condition as part of research studies at the Department of Obstetrics and Gynaecology of the Medical University of Silesia in Katowice, Poland. The research was approved by the competent University Bioethics Committee and by participating patients. Records of both datasets are publicly available at red from .pubic symphysis, and the electrode with active-ground signal was placed on the mother’s left leg. Direct fECG signal was sensed from the fetus’ head with a sterile spiral electrode. The signals were converted to a digital format with 16-bit resolution and sampling frequency of 500 Hz for aECG signals and of 1000 Hz for direct fECG. All sensed aECG signals have been pre-processed using a simple filter with multiple notches, located every 50 Hz. Low-frequency interference has been eliminated by setting the first frequency threshold to approx. 5 Hz; top-band between 45 Hz and 55 Hz was set to remove power-line interference. In direct fECG, the noise was suppressed in a comparable manner, but with modified filter parameters according to the sampling frequency [In both datasets aECG signals have been sensed from the mother’s abdomen by means of four conventional Ag/AgCl electrodes and recorded using he KOMPOREL system consisting of signal recorder module and a portable computer. A conductive paste was applied on the upper epidermis layer before attaching the electrodes. The electrodes were attached around the mother’s navel line, common reference was placed above the requency .Labour dataset—the first used dataset contains twelve 5-minute long records of women between their 38th and 42nd week of pregnancy sensed in an advanced stage of labor. Each recording contains four aECG signals and includes a direct fECG signal sensed concurrently from the fetus’ head using a scalp electrode. Direct fECG gives the most reliable information about fHR and was therefore considered as reference or ‘gold standard’. In addition, the dataset includes annotation with precise positions of fQRS complexes determined by the authors to automatically detect R-peaks in direct fECG signal, correctness of which has been validated by clinical experts.Pregnancy dataset—the second used dataset contains ten 20-minute long records of women between their 32nd and 42nd week of pregnancy. Each recording also contains four aECG signals; in this case however, no reference direct fECG was sensed as the measurement using a scalp electrode can be done after the rupture of amniotic membrane. Nevertheless, in order to be able to use the signals for testing and evaluation the extraction algorithms, the authors processed and analyzed aECG signals and provided annotations with reference positions of mQRS and fQRS complexes. The correctness of fQRS complex position was again manually checked and corrected by clinical experts. Each fQRS complex was assigned with a reliability flag. Flag 1 means that the fQRS complex position was verified by a clinical expert, while flag 0 means that the position of the fQRS complex could not be verified by a clinical expert due to high levels of signal interference . The authors recommend excluding these unreliable fQRS complex positions when evaluating the detection accuracy.The datasets used contain records acquired both during pregnancy and during childbirth. Therefore, they contain diverse signals varying in their quality, magnitudes of maternal and fetal components, as well as the interferences present. In the Labour dataset, the signals were recorded with a more prominent fetal component, while in the Pregnancy dataset, the fetal component was significantly lower compared to the maternal one. For example, for the Labour dataset, the mQRS:fQRS ratio was on average equal to 2 whereas for the Pregnancy dataset, the average mQRS:fQRS ratio was 3.5. Different levels and types of noise were present in both datasets. These were mainly power line interference, but also low-frequency interferences caused by movements  or uterine contractions, as well as by impedance changes between the electrodes and maternal skin. Since the quality of individual records varied and cannot be generalized for the entire dataset, we present four parameters characterizing its quality for each record:Parametr WM—determining the mECG signal level in relation to interferences.Parametr WF—determining the fECG signal level in relation to interferences.Parametr WEM—characterizing energy changes of mQRS complexes in aECG signals.Parametr WEF—characterizing energy changes of fQRS complexes in aECG signals.The WM and WF parameters assess the relationships of the amplitudes of the mECG and fECG signals in relation to the interferences in dB units. As for the WEM and WEF parameters, if there are no changes in the amplitude or shape of the individual mQRS/fQRS complexes, the values of the WEM and WEF parameters are equal to zero. The values of the WEM and WEF indices are also affected by the interference level. If the interference amplitude is comparable to the mQRS/fQRS amplitude, the WEF and WEF values take it into account. Detailed information about these parameters, including mathematical formulas for their calculation, can be found in the original publication by Matonia et al. . The corIt is highly desirable to maintain the same process of objective evaluation of extracted signals in order to be able to compare individual studies without having to repeat a certain experiment using already tested methods. Therefore, objective evaluation in this study is based on computation of R-peaks detection accuracy (ACC). This parameter is very often determined in various publications addressing fECG signal extraction and determination of R-peak positions, for example , 20. OurIn order to be able to compute the ACC parameter, the fECG signal must be first extracted and the position of R-peaks in it must be estimated. Additionally, the tested dataset must contain reference annotation of correct positions of R-peaks determined by the experts. The next step is to determine the true positive (TP), false positive (FP) and false negative (FN) parameters. Detected R-peaks in the extracted signal, which ranges within the interval of ±50 ms from the reference annotations, are marked as TP. Detected R-peaks in the extracted signal, which fall outside the mentioned interval, are marked as FP. And finally, any emitted R-peaks that should have been detected in the said interval, but were missing, are marked as FN. After the TP, FP, and FN parameters are determined, Since ICA-based methods are multi-channel, at least two aECG signals were needed on the input side. It was found out based on our previous research , 16 thatPre-processing of aECG signals using a FIR filter to eliminate isoline fluctuations. Threshold frequencies were set to 5 through 50 Hz, the filter order was 500. The lower cutoff frequency of 5 Hz is chosen because the authors of the dataset removed frequencies below 5 Hz. The upper cutoff frequency was chosen at 50 Hz because the focus in this work is on fetal QRS complexes, which lie predominantly at frequencies 10–15 Hz [Estimate of individual components  from a mix of aECG signals using ICA-based methods. The number of output components was set to match the number of aECG input signals for the specific recording.Automatic choice of aECG* and mECG together with amplitude/time centering. After receiving the output components from the ICA method, QRS complexes are detected in all output components. Based on the amplitudes of the QRS complexes, the polarity of the signals is adjusted. Subsequently, the one with the smallest number of QRS complexes and meeting the condition of 60—100 bpm is selected as the mECG signal. The remaining two output components are compared with each other using the SNR, where the signal with the higher SNR is designated as aECG*. Time centering is then performed, i.e. synchronization of the mECG and aECG* based on the positions of the detected QRS complexes. Finally, based on the amplitudes of the detected QRS complexes, the signals are equalized in amplitude. Both centerings are applied to increase the efficiency of the FTF algorithm.Application of FTF algorithm used to modify the mECG signal in order for it to correspond to the mECG component in the aECG* signal as much as possible. The modified mECG signal was subtracted from aECG*, leading to fECG extraction.Missing R-peak is added if the current RR interval > 1.3 times the median of all RR intervals. In such case, the missing R-peak is added right in the middle between adjacent R-peaks.Incorrectly detected R-peak is removed if the current RR interval < 0.7 times the median of all RR intervals. In such case, the excess R-peak is removed.i interval < 0.9 times the median of RR intervals and concurrently RRi+1 interval > 1.1 times the median of RR intervals. This goes vice versa—the RRi interval > 1.1 times the median of RR intervals and concurrently RRi+1 interval < 0.9 times the median of RR intervals. In such case, the incorrect position of QRS complex shifted right in the middle between adjacent R-peaks.R-peak is determined as incorrectly positioned if the RRDetection of R-peak in the resulting fECG signal using CWT detector . The lasThe example of detected R-peak in recording r6 of the Labour dataset compared to direct fECG with highlighted reference positions of R-peaks by annotations is shown in Determination of TP, FP, and FN with estimated R-peak position and reference R-peak positions in annotations. Computation of ACC, SE, PPV, and F1 parameters based on the determined TP, FP, and FN values.This section summarizes the results of fQRS complex detection achieved by each ICA-based method and subsequent application of an adaptive FTF filter. The ICA-based methods were also compared in terms of time complexity. In this section, a statistical analysis is presented to determine whether the differences between the tested algorithms are statistically significant, both in terms of the quality of filtration they are providing, and in terms of their time consumption. All data used and analysed are available as supporting material to this study.The resulting ACC values acquired with each ICA-based method and the follow-up application of FTF are summarized for Labour dataset in As for the average values, in all cases exceeded 80% , and thus all tested algorithms achieved effective extraction in this dataset. When assessing the effectivity of the algorithms based on the mean of the ACC values over all recordings, the most effective algorithm is the RADICAL (91.41%), followed by FlexICA, SOBI and KICA methods that reached mean accuracy of over 90% . As the least effective methods, we can consider the methods with the mean accuracy below 90%, i.e. ERICA, Robust ICA, and SIMBEC, reaching 84.95%, 85%, and 86.93%, respectively.For a detailed statistic results  achieved by the most effective RADICAL method for each recording, see The ACC values acquired for the Pregnancy dataset with each ICA-based method and the follow-up application of FTF are summarized in The extraction accuracy was generally lower in this dataset, as the mean ACC values ranged between 77.34%–84.14%, and 6 variants of the ICA method  did not exceed the mean ACC of 80%. JADE and SOBI methods were not the most accurate in fQRS complex detection in none of the recordings and had mean values of ACC = 80.59% and ACC = 77.35%, respectively. AMUSE, ERICA, FlexICA, Infomax, and SIMBEC algorithms achieved the most accurate detection of fQRS complexes with 1 recording with mean values of ACC = 77.34%, ACC = 80.65%, ACC = 79.18%, ACC = 79%, and ACC = 78.84%, respectively. The most effective methods were KICA, RADICAL, and RobustICA, which achieved the most accurate detection of fQRS complexes with 2 recordings with mean values of ACC = 82.15%, ACC = 78.70% and ACC = 80.54%, respectively. FastICA turned out to be the most suitable for Pregnancy dataset; even though it achieved the most accurate detection of fQRS complexes with 1 recording only, it had the highest mean value of ACC (ACC = 84.14%).Even in this dataset, detailed statistical results with all recordings achieved with the most effective method, FastICA, are provided; see The main reason for the detection of the fQRS complexes is their use to determine fHR, which is then used by the clinicians to determine fetal health state. Therefore, we provide an example of a fHR trace created from signals extracted with the FastICA-FTF compared to the reference fHR trace for all recordings of the Labour dataset  and PregIn addition to evaluation of algorithms from the perspective of extraction quality, we compared each ICA-based method from the perspective of computation time; see According to the recorded computation times shown in R Core Team. In all cases, statistical significance was set as p < 0.05.In order to find out whether the differences between the tested algorithms are statistically significant, we performed a) a statistical analysis of the results achieved for all used parameters evaluating the quality of filtration  and b) a statistical analysis of the time consumption of individual algorithms. Statistical analysis was performed using First, the normality of the data was tested using the Shapiro-Wilk test for all parameters evaluating the quality of filtration, as well as for the time requirements for each algorithm. In some cases, statistically significant deviations from normality were detected, and therefore non-parametric methods were chosen for data description and subsequent analysis. The median and interquartile range (IQR) were used to describe the analyzed variables. A detailed analysis of the filtration quality results was performed for individual evaluation parameters and individual datasets. However, the results did not show significant differences between the given results and for this reason only the summary analysis for both datasets and one selected parameter (ACC) is presented. A deeper analysis, separately for the Labour dataset and separately for the Pregnancy dataset, is then devoted to the analysis of the time requirements of the tested algorithms, where statistically significant differences were found.To statistically evaluate the differences between the tested algorithms in terms of their filtration outputs and the computational demands, we used the Friedman test, which is the non-parametric alternative to the one-way ANOVA with repeated measures, supplemented with the Kendall concordance coefficient. The Kendall concordance coefficient expresses the simultaneous association (relatedness) between k sets of rankings . The range of Kendall concordance is from 0 to +1. Values close to zero represent lack of agreement in the rankings of the algorithms among records, while values close to 1 represent perfect agreement in the rankings of the algorithms among records. In case of the Friedman test detecting a statistically significant difference between individual algorithms, a Conover post-hoc analysis with Benjamini & Yekutieli correction was performed to calculate dusted p-values for the detection of homogeneous subgroups of algorithms.Detection accuracy resultsWhen comparing parameters evaluating the quality of filtering records  across both datasets, no statistically significant differences between the tested algorithms were found , see Time consumption analysis resultsIn the next step, we analyzed the time consumption of the tested algorithms. For each algorithm, the time needed to create ICA components was measured with different numbers of inputs . This methodology was selected because the number of inputs significantly affects the quality of the extraction and the computational complexity. The results showed a statistically significant difference between the calculation times for the individual algorithms for all tested scenarios , see Tables μ was practically at 0 and there was a low value of 1.96σ, which indicates the minimum difference between the reference and filtered fHR curves. For bad results in μ deviating from the value 0 and a high value of 1.96σ indicating a large difference between the reference and filtered fHR trace. For a better overview, the μ and 1.96σ values obtained for FastICA-FTF method on all data  can be seen in As part of the statistical analysis, we created the Bland-Altman plots, where two measurement results are compared. In our case, it is a comparison of the estimated fHR trace against the reference fHR trace. Herein, we present examples for the FastICA-FTF method, where The results presented in the previous section showed that ICA-based methods combined with an adaptive FTF algorithm are able to effectively extract non-invasively sensed fECG. However, there are visible differences in accuracy when detecting fQRS complexes, namely 1) between individual ICA-based methods, 2) between individual recordings, and 3) between mean results of both datasets.Influence of algorithm selection—the results of the statistical analysis, showed no statistically significant difference between the individual algorithms in terms of the accuracy they provide, only in the time needed for their calculations. However, there are visible differences between individual algorithms in some records. To show why each algorithm achieved different results with the same recording, we decided to compare the waveforms of the extracted fECG signals. An example of extracted signals for recording r7 of the Labour dataset is shown in Nevertheless, in the case of recording r4 in the Pregnancy dataset see , mQRS coIt should be noted that the low average accuracy of the RADICAL method on the Pregnancy dataset is caused mainly due to one extremely low outcome on record r6, where the dropped below 20% (ACC = 14.47%) and further low accuracy outcome of the record r8 (ACC = 44.56%). On the other hand, for the other records, this method worked similarly as the other algorithms. We can therefore conclude that the RADICAL method was the least robust algorithm in the experiments on low-quality data.Finally, as proved by the results of the statistical analysis, the choice of the algorithm does not significantly influence the results. However, what really counts is the setting of the algorithms used. For ICA methods, the key parameters to be set are the number of components, convergence criterion, number of iterations and so on (depending on the type of ICA-based method). For adaptive algorithms, these are mainly the filter order, convergence constant or forgetting factor , 16. We Influence of input signal quality—the differences in extraction accuracy with each recording is caused primarily by the quality of aECG input signals. Earlier studies [ studies  already Influence of dataset used—comparison of the results of both datasets show that the mean accuracy achieved during fQRS complex detection in recordings from the Labour dataset was higher than in case of the Pregnancy dataset recordings (ACC = 84.14%). Based on the information from [ion from , in whicThe first obvious reason for such differences is that in Labour dataset, the gestation age of the fetuses monitored was higher 38–42 weeks) in comparison to those in Pregnancy dataset (32–42 weeks), which generally means that the amplitude of the fetal ECG is higher and thus easier to extract , 45–48. –42 weeksIn , the autSince not all ICA-based methods worked well for various signals, we decided to create a summarizing evaluation of the algorithms, see Another problem may be the randomness effect, which causes different output components each time the ICA-based method is run. This means that an ICA-based method would have to be run several times to eliminate this randomness effect. The data used in this study was of good/medium quality, so this randomness effect was minimal, which is why we neglected it after the initial experiments. In these initial experiments, we tried to run individual algorithms repeatedly on the same recording and the same combinations of electrodes, and the randomness effect did not manifest itself significantly. In the case of low-quality signals, of course, the efficiency of the algorithms would drop sharply and the randomness effect could have a big influence. With low quality signals, where the fECG is almost invisible, it may even happen that the ICA-based method will not be able to extract the necessary output components at all, even with repeated execution. The problem of randomness effect was for example addressed in a study . The futIn order to make the fQRS complex detection even more accurate, post-processing algorithms (e.g. WT or EMD) could be tested in the future as they might improve the extraction quality. The future research should also focus on testing the proposed algorithm from the perspective of morphological analysis feasibility , which is very important for clinical practice. The ST segment analysis is a particularly important indicator of fetus’ health condition and can be used to detect fetal hypoxia very accurately . The robustness of the algorithm should be determined by means of testing pathological recordings and abnormality recordings in addition to physiology recordings.This study provided a comparison of available ICA algorithms for the fECG signal processing. The results on two tested datasets, Pregnancy dataset and Labour dataset, showed superior results of the FastICA algorithm and the follow-up FTF application in terms of accuracy . Moreover, the computation time needed was comparable with the other tested methods (mean of 0.452 s), allowing the algorithm implementation for the real-time applications. These results provide a significant first step towards creating a suitable hybrid method for NI-fECG extraction.S1 File(ZIP)Click here for additional data file.S2 File(ZIP)Click here for additional data file.S3 File(ZIP)Click here for additional data file.


Attenuated and/or genetically modified oncolytic viruses (OV) gain increasing interest as a promising approach for cancer therapy. Beside the assessment of subject safety, quality and efficacy aspects of medicinal products for human use, genetically modified viruses are also governed by EU regulatory frameworks requiring an environmental risk assessment (ERA). An important element to be assessed as part of the ERA is the incidence of exposure to OV of individuals, other than the trial subjects, and the environment. The evidence-based evaluation of shedding data is considered to be decisive in that context, as it may impact the OV capacity to be transmitted. This is particularly true for OV still able to  replicate as opposed to replication-defective viral vectors commonly used in gene therapy or vaccination. To our knowledge, this article presents the most extensive and up-to-date review of shedding data reported with OV employed in clinics. Besides the identification of a topical need for improving the collection of shedding data, this article aims at providing an aid to the design of an appropriate shedding study, thereby relying on and further complementing principles described in existing guidelines issued by European and international institutions. Oncolytic viruses (OV) are  replication competent viruses (with low pathogenicity) that are designed to be able to selectively replicate in tumor cells, leading to their destruction by the direct lysis of host tumor cells, while sparing normal cells. Accumulating evidence in oncovirotherapy demonstrates that OV infection can also trigger specific antitumor immune effects. Cellular proteins released from OV-lysed tumor cells elicit an interaction with the innate immune system through the activation of dendritic cells, which in turn stimulate adaptive immunity .OV can be naturally occurring viruses that have natural tropism to neoplastic cells, such as Reoviruses , Newcast®® , an oncolytic Herpes Simplex Virus (HSV), for the treatment of advanced melanoma can be considered as a milestone in the development of OV therapy, it should be noticed that three other OV have been approved for commercialization. A wild-type Enteric Cytopathogenic Human Orphan type 7 (ECHO-7) Picornavirus, with trade name Rigvir®®, was first approved in 2004 in Latvia for the treatment of melanoma [®®) received conditional approval in Japan for the treatment of glioblastoma [While the approval in the US and Europe in 2015 ,8,9  as part of the regulatory application for clinical trials or marketing applications of medicinal products containing genetically modified organisms (GMO).The evaluation of exposure pathways through which GM viruses and viral vectors with their inserted DNA sequence may interact with humans , or the environment, is of major importance in the ERA. Pathways of exposure include leakage upon administration (spreading) of the investigational product, accidental release during its administration, and shedding of the product by the trial subject. Shedding corresponds to the dissemination of viral (vector) particles in any form into the environment via excreta ), skin  and blood from the treated subject . Unlike While several guidelines are recommending to examine shedding as early as possible in the clinical development (see hereunder), in practice, rather limited attention is given to shedding data compared to the relatively extensive collection and assessment of data supporting the safety, quality and efficacy assessment of the OV.This paper presents a review of shedding data collected during the clinical development of OV and aims at providing an aid in the design of an appropriate shedding study.Based on general reviews on OV recently published in PubMed ,20, a fiThis final list counts 165 manuscripts representing 165 different clinical trials and each trial was evaluated for multiple variables. Variables assessed included the phase of the clinical trial, the number of subjects treated, the type of virus used, the nature of the viral backbone , the transgene, the type of cancer treated, the use of single-agent or combination regimens and viral genome shedding data. With respect to the viral genome shedding, the authors assessed for each of the studies whether viral genome shedding was assessed and reported, and more particularly, which body tissues and/or fluids were assessed for viral particles, which detection test was used, whether viral particles were detected and for how long, and whether shed particles contained infectious viral particles. This final list of 165 manuscripts can be found in online In addition to the regulatory requirements common to all  medicinal products, the use of GM viruses for pre-clinical investigation, clinical trials as well as their marketing as medicinal product is covered by the legislation regulating the use of GMO, which encompasses an ERA.The ERA relies on a well-defined methodology and should be conducted on a case-by-case basis. It starts with the identification and characterization of potential hazards associated with the GMO on human health, with focus on individuals other than subjects or those vaccinated, and on the environment at large, including animals, plants and microorganisms. Concurrently and as part of a second risk component, the probability of occurrence of potential hazards under the conditions of use is assessed. Both components led to an estimation of the risk to human health and the environment posed by each identified hazard of the GMO by combining the probability of its occurrence and the magnitude of its consequences. An overall risk is then determined by combining all of the individual risks ,22. The Numerous viruses have been used to design and develop OV therapy. The identification of potential hazards, the first step in the ERA, should take into account the characteristics of the OV and the properties of the inserted gene sequences and the gene products. This implies that due consideration of aspects such as the extent of attenuation, the replication capacity, tropism, biodistribution, genetic stability and the capacity to recombine with other wild-type viruses should be evaluated on a case-by-case basis. The properties of the parental viruses from which OV are derived may provide valuable starting information, taking into account that, in general, OV developed for therapeutic application are less virulent or infectious.The development of different oncolytic viral systems for cancer therapeutic applications relies to a major extent on the genetic modification of viruses. Several strategies to enhance the therapeutic effect of oncolytic virotherapy involve the genetic “arming” of replicating viruses with transgenes, such as tumor suppressor genes, immune regulatory genes, apoptosis inducing genes, angiogenesis inhibitory genes and genes coding for pro-drug converting enzymes or heat shock proteins. Besides the insertion of transgenes with an inherent therapeutic effect, other sequences can be inserted or deleted that are involved in the targeting of OV .Inserted gene sequences and their gene products should be considered for their potential impact on the viral life cycle alterations , on the capacity of recombination of the virus or on the host . All of these elements may alter biodistribution and persistence in the subjects and may impact shedding following administration of the OV.In the next section, we will focus on elements that may have an impact on the shedding properties of OV, based on examples in the clinical field, and elaborate on the assessment of shedding data as one of the key aspects in the outcome of the ERA of OV.As mentioned above, shedding corresponds to the dissemination of viral (vector) particles in any form into the environment via the excreta or secreta, skin and blood from the treated subject . While iFrom our review of the literature see , it is oThe test method used to assess the shedding potential of oncolytic viruses should be sufficiently sensitive ,26,27. POur review of the literature shows that many studies limit the collection of shedding data by conducting quantitative real-time PCR (qRT-PCR) analyses. Of the 70 clinical trials mentioning shedding analysis, 4 did not mention the method. In 32 of the clinical trials, only a PCR test was performed, while 14 of the studies did only perform an infectivity test such as a cell culture or a plaque assay test. In 17 of the trials, both a PCR and a cell culture or plaque assay were performed.®®, for which swabs from the surface of injected lesions from subjects with unresectable recurrent melanoma tested positive for infectivity [In most of the clinical trials that performed infectivity tests alone or in combination with a PCR, no infectious particles were observed in the various shedding samples analyzed. However, in a few trials 6/32), the presence of infectious particles was shown by cell culture or plaque assay in some shedding samples. This was the case for some saliva samples from metastatic prostate cancer subjects treated with a high dose of the oncolytic Adenovirus CG7870 , and forectivity , as wellectivity . We also, the preectivity . All theA proper ERA of OV addresses several aspects that may impact the release of the viral particles by the host. This includes among others an examination of the genetic stability, the conditions under which replication is occurring, replication competence and the route of administration of the OV. Characteristics from the wild-type virus from which the OV has been derived, such as the pathogenicity, the tropism, the host range, the natural route of transmission and the shedding pattern, may provide valuable information to perform the ERA of OV. Furthermore, each of these aspects may be altered by the overall design and the genetic modification proper to a given OV ,34.®® [®® and OrienX010 both express the transgene GM-CSF used to boost the anti-tumor immune response by promoting dendritic cell recruitment and activation following tumor antigens’ liberation from lysing tumor cells. Although OH2 also expresses the transgene GM-CSF, no shedding of viral particles has been observed [With regard to the type of virus from which OV is derived, the detection of a viral DNA/RNA genome was observed at least once for all virus families . A rathe®®  and Orie®® . No shed®® , G207 [3®® ,38,39, G®® , HF10 [4®®  or HSV17®® ,43. Imlyobserved .Differences in shedding pattern have also been observed within the Picornaviruses class. Shedding of the viral particles from subjects treated with the naturally occurring replication-competent Picornavirus, Coxsackievirus  or SenecIn addition to the intrinsic properties of the OV and irrespective of the type of virus, due account should be given to the interaction of the OV with the host, which may have an effect on this interaction and hence on the shedding pattern. One should therefore remain cautious with extrapolating pre-clinical data to human beings. For example, the results of quantitative real-time PCR (qRT-PCR) indicate that VSV-IFNβ-NIS RNA was detectable in some early nasal, oral, and rectal swabs of inoculated pigs , or in sThe route of administration used during clinical application usually differs from the natural portal of entry of the wild-type virus from which the OV are derived, and may also impact shedding pattern A,B. IntrThe same observation can be made with two oncolytic Poxvirus GL-ONC1 and Pexa-Vec. Shedding was observed when GL-ONC1 was delivered intravenously to subjects with advanced head and neck carcinoma , whereasFor oncovirotherapy, direct intratumoral injection is a preferred route of administration for brain tumors (18/23), despite the fact that it may pose significant challenges. Intratumoral injection of the OV is also mainly used for easily injectable tumors, such as melanoma (16/22), or head and neck cancers (6/8) C.OV are also often administered intravenously in subjects with metastatic tumors. In this case, OV encounter many physiological barriers before reaching cancer lesions and repeated doses may be necessary, thereby triggering the recognition and attack by the immune system and clearance of the OV by neutralizing antibodies. While the latter is a concern for efficacy reasons, elicitation of a strong immune response may also result in a shorter duration of shedding.As has been observed by Dunn et al.  and WeilFinally, the reactivation of latent OV, influenced by the immune status of the subject, could possibly lead to a shedding pattern that is different from what could have been predicted from former clinical experiences. For oncolytic viruses with the potential for latency reactivation, the collection of additional samples for shedding analysis when subjects show signs of infection due to reactivation could be planned.Each of the above-mentioned elements contributes to a risk-based approach in the design and the extent of shedding studies. It may provide insights into the choice of biological samples to be analyzed, as well as the frequency and the duration of monitoring.Samples most commonly collected include urine and oral swabs. Other sample types such as feces, nasal swabs and injection site samples are also collected, but less commonly see .The collection of data should also take into account the occurrence of metastasis. For example, secondary tumors may also be located in the oral cavity, larynx, pharynx or esophagus, justifying the collection of saliva samples.®® [®® DNA was observed on the surface of injected lesions for all treated subjects, and it was still detected for 14% of subjects during the safety follow-up period. Of the 740 swabs from the surface of injected lesions, 8 tested positive for infectivity [When OV are administrated by the intradermal route, there is a possibility of transmission of the OV through skin contact. Therefore, the collection of skin swabs at the site of injection, in addition to samples routinely assessed for shedding, should be recommended in order to determine whether an appropriate occlusive dressing is required as a precautionary measure. According to our analysis, none of the trials performing intradermal injection reported shedding results. On the other hand, eight trials where the OV were administrated intratumorally included swabs from the injection site in their shedding analysis. From these eight trials, shed viral DNA at the injection site was observed in five trials. Of these five clinical trials, four trials were performed with Imlygic®® ,64,65,66®® . Detectaectivity . The Adeectivity .The duration and frequency of sample collection should be decided on a case-by-case basis, taking into account the characteristics of the parental virus, the conditionally replicative competence of the OV and the immune status of the subject. As most of the OV are still replication-competent, the duration of sample collection should also take into account the possible appearance of a secondary peak of shedding. As one of the numerous examples of viral replication, it was shown that in subjects treated with the intratumorally injected HSP70, a telomerase-specific replication-competent oncolytic Adenovirus (telomelysin), viral DNA was detectable in plasma or sputum at days 7 and 14 post-treatment, despite being below detectable levels at 24 h post-treatment .The identification and characterization of potential adverse effects associated with the use of a given OV and the assessment of their likelihood of occurrence may lead to the identification of potential risks. It is also possible that the case-specific assessment reveals remaining uncertainties, precluding any conclusion on the risks for human health and the environment. In the first case, the set-up and implementation of risk management measures aim at minimizing identified risks, while in the second case, these could serve as precautionary measures.In cases wherein data support the low likelihood of shedding or where no risk related to the shedding is identified for human health or the environment, good working practices involving proper hand hygiene, personal protective equipment (PPE) and proper decontamination and waste procedures at the clinical setting will be effective to limit the inadvertent exposure of personnel and the possible dissemination of the OV into the environment . The corWhen shedding by the treated subject cannot be excluded and potential risks for human health and the environment have been identified, risk management measures should also focus on minimizing the exposure of third parties outside the clinical setting, thereby giving particular attention to immunosuppressed or any vulnerable people . A list of possible measures that may be considered, taking into account the considerations made as part of a case-specific risk assessment, is provided in a guidance document endorsed by several national competent authorities involved in the risk assessment GMOs  and has Geletneky et al. indicated in their article that glioblastoma subjects treated with the oncolytic Parvovirus H-1 were obliged to stay isolated in the study center until shed viral genomes were no longer detected in feces, urine or saliva, or until the first occurrence of H-1-specific antibodies .It could be hypothesized that OV applications that have entered the market and that have successfully gone through the several stages of clinical development would rely on shedding data to determine risk management measures, if deemed relevant according to a proper risk assessment, and that data on the occurrence of transmission to close contacts would allow further refinement of risk management measures. In this ideal scenario, evidence-based data would contribute to a set of management measures that are as proportionate as possible to the environmental risk. However, the occurrence of secondary infections is barely addressed during clinical development. Given that these viral vectors are often derived from infectious viruses, any studies that contribute to the assessment of possible secondary infection would further contribute to the ERA of the medicinal product.®®, Andtbacka et al. reported that some mucosal or skin lesions were observed in close contact, and investigators all tested negative for T-VEC. However, none of the lesions were tested for wild-type HSV-1 or HSV-2, leaving an uncertainty as to the origin of the lesion [During a phase II clinical trial with modified Herpesvirus Imlygice lesion .®® are also found in the current subject brochure for Imlygic®®, which stipulates measures to be taken by the subject to avoid the direct contact of thirds with the subjects’ bodily fluids or injection sites, such as covering injection sites with airtight and watertight dressings, implementing a proper disposal of used dressings and cleaning material to prevent household contacts from directly touching them [The precautionary measures that were implemented for the subject during this phase II study with Imlygicing them .®®, started in 2017, is among others counting the numbers of herpetic infections, with the detection of T-DNA among close contacts and healthcare providers as a secondary outcome [®® are being pursued.An ongoing post-marketing study of melanoma subjects treated with Imlygic outcome . This exFinally, if animals may be infected, appropriate measures to limit exposure to susceptible pets or other animals in the immediate surroundings of the treated subject should be considered. Newcastle disease virus (NDV), for example, is a naturally occurring oncolytic virus that causes severe illness in birds and poultry, and could therefore pose an environmental risk even if it is non-pathogenic in humans. In three clinical trials with intravenously injected NDV-PV701 in subjects with advanced solid cancer ,72,73, lAnother example of clinical trials where measures to avoid contact with animals have been proposed involved the use of a recombinant chimeric Vesicular stomatitis virus, in which the VSV glycoprotein was replaced to abrogate neurotoxicity and pathogenicity . As the The inventory presented in this paper provides a state-of-the-art of the shedding analysis of OV in clinical practice, and raises the question: to what extent it is possible to build upon the experience gained so far with shedding data to draw conclusions for each of the different types of OV? This may be particularly relevant in cases where historical shedding data have been obtained for well-characterized OV, of which the designs present relevant similarities with a novel investigational OV. Our literature search reveals that in 89% of the studies with Adenovirus-derived OV reporting shedding analysis, positive shedding results were obtained. However, as illustrated above by the several examples developed in this paper, the shedding patterns remain diverse and complex, as well as for Adenovirus-derived OV, thereby hindering the development of a standardized study design. Diversity in shedding patterns was observed even when using OV derived from the same type of virus. A number of elements may impact the shedding pattern, such as the specific design of the viral vector and the transgene harbored by the viral vector, the interaction of the OV with their host organisms, the immune status of the patient or the variety of the clinical protocols, such as differences in the administration route or the use of concomitant drugs.Valuable information and insights into the toxicity, biodistribution and shedding pattern of OV could be obtained from non-clinical studies. As compared to clinical studies, animal studies are more amenable to be conducted in the early stages of the development of investigational OV. However, it should be taken into account that data are not readily extrapolable from animals to human, in particular when different routes of administration are used or no data have been collected in larger animals, such as non-human primates. Also, an animal model fails to mimic the patient-specific immune status. This means that an absence of viral shedding in animal studies does not necessarily preclude viral shedding in humans, as a different host organism may induce a different behavior of the virus .Because the shedding pattern strongly depends on different factors and because it can also differ between animals and humans, the collection of shedding data in the earliest phase of clinical development of investigational OV is strongly recommended regardless of the OV vector.The FDA guidance for the design and analysis of shedding studies also recommends the collection of shedding data on OV in the earliest phase of clinical development (phase I) and anytime afterwards if the dose, the route of administration, the regimen or the indication are modified . LikewisA possible concern, for which we could not find experimental data, is the likelihood of an in vivo recombination between the OV and endogenous viruses circulating in the trial participants. This assessment should not be neglected because recombination events could lead to the formation of uncharacterized variants that could be more virulent and that could affect the shedding pattern and the potential for transmission. These newly generated viruses could therefore compromise the environmental safety. In general, the likelihood of the recombination between viruses significantly increases with the prevalence of co-circulating viruses in the population, and with their genetic homology. High viral loads, often a relevant feature of replication-competent OV, increase the chance of exchanging strands, explaining why, in many cases, recombination is often replication-dependent . MoreoveWhilst the collection of shedding data undoubtedly supports an evidence-based ERA, it is important to be aware that the shedding of OV or any other viral vector or virus does not necessarily involve a risk. Potential adverse effects for close contacts or the environment that could arise following shedding do not only result from the presence of viral particles in the shedding samples, but also depend on the stability of the shed viral particles under environmental conditions outside the host, the route of transmission , the capacity of the shed particles to infect cells of other persons or animals, and, as a last element in the chain of events for environmental risk to occur, the capacity of the OV to cause adverse effects in the novel host organism. To possibly alleviate remaining uncertainties in secondary transmission, it is therefore important to answer the questions of whether the observed shed particles are only vector DNA/RNA or a remnant thereof, and whether these shed particles are still infectious. These observations will contribute to a proportionate risk management by allowing the determination of appropriate precautionary measures.Indeed, a fundamental question when uncertainties remain regarding the actual risk associated to the shedding of OV is what level of risk management measures should be taken, or what level of uncertainty would warrant precautionary measures. If it remains unclear whether shedding may occur and what risks it may entail for human health and the environment, a drastic and conservative scenario would be to eliminate any possibility of release of OV into the environment by keeping patients for several weeks/months in the hospital setting. However, the implementation of stringent measures may carry drawbacks, as it may increase costs and time not only for the appropriate training of personnel, the set-up and follow-up of waste disposal procedures and logistics, but also for the recruitment of subjects requiring their informed consent and the training of close contacts if the trial participant is discharged. Ideally, risk management measures should be as proportionate as possible to the actual risk posed so as not to hinder the development of research and innovation and to safeguard patient access to innovative treatments, while ensuring the proper protection of human health at large and the environment. A way to contribute to proportionate risk management is to continue to gathering data-based evidence by including within the shedding analysis the determination of the fraction of infectious particles in early phases of the clinical development of OV.With the diversity of OV that entered the clinical research and development landscape, this work demonstrates the current gaps in data-based evidence on shedding and the challenge of defining risk management measures that are proportionate to the actual risk posed for human health and the environment. In accordance with GMO legislation requiring a case-specific and risk-proportionate approach, this paper aims at encouraging the collection of shedding data as early as possible in the developmental plan in the rapidly growing area of OV. The demonstration of the shedding of infectious particles may warrant assessments of the potential of secondary transmission. The collection of real-world transmission data is expected to provide a better understanding of transmissibility, which is key to characterizing the risk for the human population and the environment. It will also benefit future clinical trials developers in establishing a clinical protocol based on evidence-based risk assessment.