Breast cancer is an important factor affecting human health. This issue has various diagnosis process which were evolved such as mammography, fine needle aspirate, and surgical biopsy. These techniques use pathological breast cancer images for diagnosis. Breast cancer surgery allows the forensic doctor to histologist to access the microscopic level of breast tissues. The conventional method uses an optimized radial basis neural network using a cuckoo search algorithm. Existing radial basis neural network techniques utilized feature extraction and reduction parts separately. It is proposed that it overcomes the CNN approach for all the feature extraction and classification process to reduce time complexity. In this proposed method, a convolutional neural network is proposed based on an artificial fish school algorithm. The breast cancer image dataset is taken from cancer imaging archives. In the preprocessing step of classification, the breast cancer image is filtered with the support of a wiener filter for classification. The convolutional neural network has set the intense data of an image and is used to remove the features. After executing the extraction procedure, the reduction process is performed to speed up the train and test data processing. Here, the artificial fish school optimization algorithm is utilized to give the direct training data to the deep convolutional neural network. The extraction, reduction, and classification of features are utilized in the single deep convolutional neural network process. In this process, the optimization technique helps to decrease the error rate and increases the performance efficiency by finding the number of epochs and training images to the Deep CNN. In this system, the normal, benign, and malignant tissues are predicted. By comparing the existing RBF technique with the cuckoo search algorithm, the presented model attains the outcome in the way of sensitivity, accuracy, specificity, F1 score, and recall. The most common disease for women is breast cancer. It is cells with extra growth of mass in women's breast region. This breast tissue forms the tumor, which is classified as benign or malignant. The malignant is the most affected cancerous region, and the benign is the non-cancerous region. This disease is diagnosed by biopsy. The researchers analyze various automated diagnosis approaches to determine breast cancer. The stroma maturity of cancer in the breast is classified by the histological image . The breThe support vector machine learning, extreme learning machine, RBNN, DNN, and other classifiers performed the classification . The optIn this, the CS optimizer is utilized for the selection process. The RBNN performs the characterization of the given image, which classifies the various properties from the input image . ComputeThe image augmentation process has different properties performed with various classes in the morphological approach. This image augmentation process performs the cropping, image resizing, enhancing, and random patches. Reference  has presc, which means fuzzy logic. In the training process, the fuzzy rule is set for performing the feature extraction. The Kernel function is used to enhance the support vector machine learning technique. The k-means scheme is used in the feature extraction process. GLCM provides the result of energy, entropy, correlation, contrast, and homogeneity in the feature extraction process [In the machine learning approach, the multilayer preceptor and the SVM are performed with two layers of the training process . This ap process . The bre process . The tis process . The RBF process . The inpReis et al.  presenteReference  illustraReference  presenteReis et al.  have intReference  analyzedReference  proposedReis et al.  have desReference  presenteReference  presenteReference  introducReference  describeReference  presenteReference  presenteReference  describeThe proposed Deep CNN for classifying the breast cancer image is optimized using the artificial fish school optimization algorithm and is modeled to provide the best classification result. In this section, the proposed methodology is described with classifier and optimization technique and their application of breast cancer image analysis.Here, the artificial fish school technique is used to give the direct image set to the training process of the classifier. The random permutation of the AFS is selected by initializing the direct values. The maximum epochs are given in the optimizer at the stage of variable initialization. Hence, the preprocessing stage is performed with the wiener filter for improving the quality of the image. These filtered images are sent to the convolution layer, which sets the image to filter with a convolution filter for activating the features of the given breast image. The convolution 2D layer will normalize the image to perform the training process in the ReLu layer. The pooling layer selects the higher value of the image by the sampling process. The single GPU processor is used for Deep CNN to perform classification. The AFS algorithm provides a direct training image to the Deep CNN. Based on the training image, the supposition accuracy is given in the AFS. The convolution neural network having three layers with sublayers is used. The activation function enables the feature map for the regional size of each image. Then, it sends to the pooling layer for selecting the maximum value of the trained image. The proposed Deep CNN architecture is given in In the convolution neural network, the above-mentioned layers performed different functions to activate the process. In this proposed method, the 90 input images are extracted for classifying breast cancer, which is given to the input layer of CNN. It is normalized in this convolution layer. The MIAS dataset is extracted for breast cancer images, and it has 1024 × 1024 pixels. A single GPU processor is capable to analyze the 64 × 64 image pixels since it is resized to perform the feature extraction. The convolution 2D layer is processed with the grey-scale image of breast cancer image to remove the features.Here, the ReLu layer is used to provide the positive images to the CNN; otherwise, it outputs to zero. The pooling layer is the major function to activate the CNN. Here, the greater pooling layer is utilized to select the maximum value of pictures from the ReLu layer. The fully connected layer gives the output of feature extraction by classifying the breast cancer image. The extraction procedure is activated in the pooling layer. The probability of picture is applied in the multiclass functions, and it is performed with the softmax layer. In this, the artificial fish school optimization algorithm is implemented for selecting the training image with supposition accuracy. A single Deep CNN process performs the three operations such as feature extraction, feature selection, and classification. The accuracy, sensitivity, specificity, and computation time are measured to analyze the performance.http://peipa.essex.ac.uk/info/mias.html. The mammographic picture analysis society provides the digital mammogram image for classifying the different features. The database comprises of seven columns representing image, type of tissue, abnormality, its size, location, stage, and image information. This classification approach is also capable to analyze the screening mammography with a digital database. In this proposed system, the MIAS database is used, which has 200-micron pixel with the padding of 1024 × 1024. The dataset containing 322 digits with 2.3 GB 8 mm tapes is used. This digital mammogram has the density of 0–3.2, and it is represented with each single pixel that has 8 bits. The MIAS database has different characters of background tissues. The abnormality of the breast cancer image is classed by masses, image distortions, asymmetry, and calcification. It classifies malignant, benign, and normal breast images.The MIAS database is utilized to separate the images of breast cancer from mammography. The database is downloaded from The first stage of mammography image classification uses the preprocessing step to enhance the image quality. The input has 1024 × 1024 pixel size, which is resized with 64 × 64 pixels. The wiener filter is used in this preprocessing stage. The 2D convolution operation removes the noise presented in the input signal. The overlapping patches, removing edges, improving the visual quality by removing the noise, resizing, and filtering are performed in this process. Here, the normal, benign, and malignant images are filtered using the wiener filter.(i)Convolution 2D layer: this layer is used for normalizing the input pictures to perform the feature extraction process. The 2D convolution process involves the operation as follows:(ii)This process improves the image by the normalizing factor. This allows the 2D matrix images for normalizing the values. This performance is done with the grey scale image.(iii)f. It acts as a rectifier, and it performs the differentiations by neuron points in the network. Feature representation with prefiltered images is represented as follows:Rectified linear unit layer: the ReLu layer activates the neurons presented in the convolutional neural network. It selects the maximum value in the function of (iv)The nonlinear rectification is performed to activate CNN functions. The nonlinear function activation is the major concern for various systems. The probability function with different classes is given as follows:(v)Max. Pooling layer: the pooling layer is employed to perform the feature extraction process. Here, it selects the maximum value of breast image to activate the pooling layer. The training image sets are selected randomly from the artificial fish-school optimization technique. It also assumes the accuracy of the each image sets.(vi)The objective function with maximum value representation is given by(vii)The spatial size of the image is reduced for the feature reduction approach. It operates only with the feature map of each image. The maximum subsampling of the pooling layer will map the features based on the classes. The 3x3-convolution matrix strides are used to select the maximum value. The selected training dataset is labeled with the different classes, and it performs testing. Based on the stride and padding in the matrix, the ReLu performs to activate the pooling operation.(viii)Fully connected layer: it is the output of feature maps, and it is performed with the AFS optimization. Each node in the Deep CNN is interconnected to perform the breast cancer image classification. It performs the feed-forward neural network model by connecting the feature reduction approaches in the convolution layer. It holds the information about the image features and provides in the vector form. The pooling layer reduces the feature map, and it is optimized with the AFS algorithm, which is connected to perform the classification. The training process selects 22 images, and the testing process is carried out with 8 images. Here, the epoch is 20 based on this; the training process will be performed to provide the classification accuracy.(ix)Softmax layer: the softmax layer solves the multiclass problems of integral functions. It selects the maximum value of final class predictions. The softmax activation function normalizes the input image and provides a positive result. The nodes of the fully connected layer are the softmax layer, which activates the function by selecting the multiclass classifications. The pretrained set of images is configured with the dense characteristics of a given dataset.(x)Classification layer: the classification layer is the output layer of the Deep CNN, and it provides the classified result of breast cancer images. It has the functionality to know about the image features. Based on stride representation, image pixels are shifted to the matrix. Then, it performs the feature extraction and reduction using Deep CNN layers. The Deep CNN layers form the array in the order of the above-mentioned layers.The CNN is the type of a deep learning technique. The DNN model performs the same operation in the convolutional neural network. The proposed Deep CNN is modeled to separate the breast cancer image with the characteristics of normal, benign, and malignant. This process performed with various layers of convolution approach is as follows:  Requires Deep CNN Output Vector of feature mapi = 1 to Number of Epoch Do(1) For (2) Startlocation(3) Initialize crowd of the fish(4) Initialize the fitness(5) Evaluate (6) Whileno of epoch is not obtained(7)(8) Dounique movement(9) Analyze the fitness(10) Calculate the new location(11) Enhance the fitness difference(12) Analyze the fish weight(13) Analyze the variance of the weights(14) Analyze the instinctive movement(15) Analyze the new location(16) Enhance the barycentre(17) Analyze the volitive movement(18) Analyze the fitness(19) Evaluate the individual step(20) Enhance the volitive step(21) Update the (22) End ForThe optimization of image classification is an important process. In this proposed method, the artificial fish school optimization algorithm is employed to provide the direct training files to the classifier. It also gives the approximate accuracy for each training image. Initially, the population size, crowding factor, maximum iteration, and distance are given in the parameter initialization process. Then, the population is initialized to produce the weight of the fish. The bulletin initialization process evaluates the present state of fishes. If the state of best fish and its position is found, it terminates the process; otherwise, it again performs the operation to select the best AF.In the presented model, the AFS algorithm is implemented to select the training image, and it provides directly to the image classifier. It also provides the respective accuracy to the images. This optimal solution is used to train the image, and it is tested. These images are classified based on the features. The approximate accuracy is used for the classification result. The training accuracy is approximately given to the Deep CNN. However, it is not an exact accuracy result of the classifier. The objective function of AFS is given byThe artificial fish performs swarming that is given in the following expression:If the artificial fish follows the neighbor partners for food search, the behavioral description represents the fitness value. The forward companion of AF while getting the fitness value is given byThe movement of the AF selects the path randomly, and its prey behavior is given byThe best possible image is selected by the following equations:The global search of the AFS algorithm is to optimize the training image to perform classification. The convergence speed shows the search optimization result of AF. Artificial fish school optimizer finds better classification accuracy. In this proposed image classification method, the convolution layers performs the extraction and reduction procedure, which is optimized by selecting the direct training files to the classifier. This optimization algorithm performs the behavior of the artificial fish to select the food in the sea. This scheme is applied to the CNN for direct selection of training images. The sensitivity, accuracy, and specificity of the proposed scheme are analyzed to compare with the existing work. By this performance analysis, the accuracy, sensitivity, and specificity are achieved for the presented model.Thus, the classification of breast cancer images using DCNN and AFS optimization algorithms is modeled, and it obtains the best outcome as compared to the previous work. The MIAS dataset helps to classify the images of breast cancer with three stages, that is, normal, benign, and malignant. These images have 1024 × 1024 pixels in size. The input data of normal breast cancer images are illustrated in The preprocessing stage of classification uses the wiener filter for resizing and filtering the breast image input. Here, 64 × 64 images are applied to DCNN for classification. The properties of the breast image are filtered with the help of the wiener filter, which is shown in Here, the AFS optimization algorithm is used to select the training images while performing the feature extraction process in Deep CNN. The optimal result is found in the fifth position of the training set, and its average accuracy is provided. The optimal training image position and its corresponding accuracy from AFS optimizer is shown in The training process in Deep CNN uses the direct selection of the training file, which uses the artificial fish-school optimization process. Here, 20 iterations are performed to obtain the best result, and they also give the reduced losses of both training and testing. The training progress is shown in In this, the proposed Deep CNN using AFS optimizer provides the best result, and its performance is evaluated see  based onPerformance evaluation parameters are calculated by obtaining the result of accuracy, recall, and F1 score. In this, accuracy, sensitivity, and specificity are calculated to achieve the result of overall performance of the Deep CNN using the AFS algorithm.By this measurement, the performance is evaluated for both training and testing processes. The comparison result of proposed and existing methods is given in Table 1Time complexity is the time required for running the process. In the proposed method, the time complexity is reduced with the minimal training images. In the presented work, the image classification is modeled based on the deep learning approach of the convolution neural network. This technique provides the classified result by optimizing the training process in the classifier, which uses the behavioral approach of the artificial fish-school optimization technique. The accuracy, sensitivity, specificity, and computation time of both training and testing processes are evaluated and compared with the conventional approach. The proposed Deep CNN using the artificial fish school model achieves the best result for breast image classifications. Therefore, the analysis report shows that the proposed Deep CNN using the artificial fish school is a better image classifier than the conventional methods.The artificial fish-school optimization technique is used for selecting the number of epochs in the design of the deep convolutional neural network model, and it is utilized for classifying the mammography images from the MIAS database which is presented. It can classify the malignant, normal, and benign characteristics of breast cancer images. Here, the single CNN mode performs the three operations like preprocessing and feature extraction, and also it enhances the design model. The Deep CNN is optimized using AFS in the training process. Here, the artificial fish-school optimization technique selects the training images by directly assigning them to the classifier. Here, this optimizer also provides the average accuracy level of the training images. This enhancement makes the system efficient by a reduced computation time. Here, the proposed CNN model performs feature extraction, feature reduction, and classification. By comparing the performance analysis of the proposed method versus conventional methods, the proposed Deep CNN using AFS optimizes the result by providing better accuracy, sensitivity, and specificity. Thus, the system concludes that the breast image classification is performed efficiently using Deep CNN based on the AFS algorithm. In the future, the optimization technique is improved for utilizing the operations in both training and testing processes. The CNN model is modified to provide better efficiency than this system. These numbers of properties are extracted to characterize the various properties in breast cancer images. The proposed work is concentrated only on the training image and epoch. It can be improved by including other hyperparameters. –31.


Background: Urinary incontinence (UI) is a significant social problem. The latest figures show that it affects as many as 17–60% of the female population, and it is one of the most common chronic diseases. Incontinence substantially decreases the quality of patients’ lives. The transobturator tape (TOT) procedure is the gold standard in surgical treatment due to its high efficacy and low complication rate. Objective: The aim of this study was to assess the quality of life (QoL) of patients with stress incontinence before and after the TOT procedure. Method: The study included 57 patients diagnosed with stress incontinence on the basis of ultrasonography and history. The QoL before and after surgery was measured using the Incontinence Impact Questionnaire (IIQ-7) and the Incontinence Quality of Life (I-QOL) standardised questionnaires. Results: The IIQ-7 scores for each question were higher  before surgery than after surgery. The results for almost all domains were statistically significant. The I-QOL results also showed that, in most cases, the quality of patients’ lives improved after the surgery. Statistically significant changes were observed in all three questionnaire domains of avoidance/limiting behaviour, psychosocial impact, and social embarrassment. Conclusion: Surgical treatment of stress incontinence with TOT results in resolution of bothersome symptoms in the majority of patients, leading to improved comfort in life. Urinary incontinence is a serious social problem. The exact number of patients affected by this condition is unknown, because there are different definitions of this disorder and the classification systems are not uniform. As the latest data show, urinary incontinence affects as many as 17–60% of the female population, making it one of the most common chronic diseases . The mosUrinary incontinence is defined as involuntary leakage of urine through the urethra due to dysfunction of the closure mechanism of the bladder. In stress incontinence, this is typically induced by a sudden increase in the intra-abdominal pressure (IAP) . Other sage;vaginal delivery;disturbed innervation of the pelvic floor;disturbed structure of connective tissue;hypoestrogenism;obesity;chronic pulmonary diseases.Factors contributing to stress urinary incontinence include the following :age;vagiAlthough women’s awareness of this condition is increasing year by year, many patients still consider it a natural symptom of ageing.Urine loss significantly reduces the quality of life in SUI patients and affects both their physical and mental well-being . Due to With acute symptoms, basic life activities are impaired. In his research, Hunskaar estimated that depressive symptoms occur in as many as 80% of respondents with severe incontinence, whereas in patients with mild symptoms that number oscillates around 40% .In a different study, Milsom assessed the impact of SUI on performing daily tasks. Nearly 100% of the patients indicated that this problem reduced their quality of life. Over 50% of the respondents said that these ailments hindered their daily functioning. As many as 70% of the women surveyed said that they could not travel longer than 30 min because of this condition. The researcher observed the occurrence of emotional disorders in 45% of the patients. Moreover, he pointed out that urinary incontinence also has a direct impact on the deterioration of the quality of life of their families [The problem of urinary incontinence also affects the economy. In a study carried out in Great Britan, Turner et al. investigated the costs associated with urinary disorders. They qualified 23,183 people for the study. The findings showed that the total annual expenditure incurred by the National Health Service was 233 million pounds, while individual patient costs were estimated to be 178 million pounds. The study demonstrates that urinary incontinence has a measurable impact not only on the national budget, but also on every patient. The financial expenses involved in the purchase of sanitary napkins, nappies and pant diapers are very high and constitute a major problem for patients with urinary incontinence. Many women who feel more comfortable when they have additional protection for their underwear or clothing use them preventively .The latest reports indicate that latent urinary incontinence is also extremely common. It is estimated to affect 36–80% of patients with pelvic floor prolapse and is associated with urethral flexion or pressure, potentially leading to micturition disorders . These cOne of the most frequently used techniques in the surgical treatment of SUI is a mid-urethral sling via the retropubic or transobturator approach. Randomised controlled studies showed a similar success rate between TVT (80.8%) and TOT (77.7%) ,16. The The definition of quality of life was proposed by the World Health Organization in 1994 states that the QoL is an individual’s perception of their position in life with regard to the culture and the value system in which they live. Factors that directly affect the QoL include somatic health, interpersonal contacts, and the community characteristics that are essential for a particular person . It is sAt present, not only strictly medical goals, but also non-medical goals are crucial elements of the therapeutic process. The latter include improving the patient’s well-being and functioning, both physically and socially. QoL studies are part of the holistic approach . In mediIn the available literature, there are not many reports that assess the quality of life of patients 12 months after the TOT procedure. In this study, the authors sought to assess the long-term impact of the procedure on women’s quality of life. In most cases, researchers evaluate the effectiveness or correctness of TOT in terms of quality of life a few weeks after the surgery. As TOT is one of the most frequently used methods in the treatment of SUI, there should be many reliable studies assessing their impact on the quality of life also in the long-term perspective.The aim of this study was to assess QoL in patients with stress incontinence before and after stress incontinence surgery using the TOT method.Eighty patients were qualified for the study, but due to the long duration of the research, both sets of questionnaires were finally obtained from 57 patients who had been diagnosed with stress urinary incontinence on the basis of ultrasound examination and history at the gynaecology ward at the Hospital of the Ministry of Internal Affairs and Administration in Wrocław. Urodynamic examination was performed in patients with doubtful diagnosis. The patients were qualified for surgery based on literature guidelines. Additionally, all patients gave their informed written consent to take part in the study prior to entering the project. All the participants completed the IIQ-7 and I-QoL questionnaires twice: once upon admission to the ward and 12 months after the surgery. The patients were qualified for the study on the basis of the inclusion and exclusion criteria.stage II and stage III stress incontinence confirmed by ultrasound examination and an interview;not taking hormone replacement therapy (HRT) before or after the surgical procedure;informed written consent of the patient to take part in the project.The inclusion criteria were:women with overactive bladders (OAB) or mixed urinary incontinence (MUI);women with urinary tract fistulas;women with congenital or acquired defects of the urethra or the bladder;women with urinary tract infections;women taking medication that contributes to an overactive bladder.The exclusion criteria were:insertion of a bladder catheter;incision and dissection of the vaginal mucosa and fascia;proper placement of the tape;preventing the implants from curling or rolling up;avoiding implant infections;optimal tension-free suturing of the vaginal walls .All eligible patients underwent the TOT suburethral sling procedure, which decreases urethral hypermobility and consequently eliminates or reduces the number of involuntary urine leakage episodes caused by physical effort. The TOT procedure, proposed by Delorme in 2001, is considered the gold standard in the surgical treatment of stress incontinence, due to its high effectiveness and low complication rate. We used this method because it is as effective as the tension-free vaginal tape approach but takes half the time . The manAll the patients enrolled in the project were given the IIQ-7 and IQOL questionnaires. The questionnaires were filled in on admission to the ward, before the TOT procedure and 12 months after the procedure at the follow-up examination. The number of responses to particular questions varied, as some patients marked two or none of the responses that they felt best described their quality of life at that time.The Incontinence Impact Questionnaire (IIQ-7) is used to assess the quality of life of women with urinary incontinence. The IIQ-7 questionnaire is an abridged version of the IIQ questionnaire, which contains 30 questions. All seven questions in the IIQ-7 questionnaire concern the impact of urinary incontinence on patients’ lives. The questions refer to the ability to do household activities, physical activities, entertainment, travel, leisure, and mental health. Each question was rated on a scale of 0 to 3, where 0 = not at all, 1 = a little, 2 = medium, 3 = very. The average of the scores obtained is calculated . Only thThe Incontinence Quality of Life (I-QOL) questionnaire is a commonly used validated QoL instrument specific for urinary incontinence. It consists of 22 questions divided into three domains: avoidance/limiting behavior, psychosocial impact, and social embarrassment. Each item can be rated on a five-point scale where 1 = extremely, 2 = quite a bit, 3 = moderately, 4 = a little, and 5 = not at all . The totp = 0.05. A non-parametric test was used due to the incompatibility of the distribution of the analysed variables (domain scores) with the normal distribution, which was verified with the Shapiro–Wilk test at the significance level of p = 0.05.Statistical analysis was performed using R Project software . The staThe project involved 57 women with a diagnosis of stage II and stage III stress incontinence. The mean age of the patients was 70.28 years, and the range was 61–87 years.The number of responses to individual questions differed, which resulted from the specificity of the questionnaires used. In the IIQ-7 questionnaire, when the symptoms did not affect the activity indicated in the question, the patient did not mark the answer to the question that would explain its impact. On the other hand, in the IQOL questionnaire, the range of responses to each question ranged from 0–5.The analysis of the results obtained from the IIQ-7 questionnaire shows that the operation did not significantly affect the improvement of the quality of life of the respondents .The answers obtained in questions 1–7 show that the surgery also increased the patients’ ability to function in everyday life. The obtained results were not statistically significant .In most cases, the I-QOL results also confirmed that the patients’ QoL improved after the surgery. Statistically significant changes were observed in the three questionnaire domains, namely, avoidance/limiting behavior, psychosocial impact, and social embarrassment . The majThe main purpose of this study was to assess the quality of patients’ lives before and after TOT surgery for stress urinary incontinence. The findings demonstrated that the patients’ QoL substantially improved after the surgery, as the symptoms that had substantially limited everyday functioning subsided in most cases. In their study, Ellerkmann et al. measured the QoL of patients with anterior bladder wall prolapse and urinary incontinence. Both their findings and ours clearly show that urogynaecological problems have a great impact on the quality of patients’ lives . Digesu As reported by Nilsson et al. and Lee, urinary incontinence noticeably lowers women’s QoL, irrespective of age, and has a negative impact on women’s relationships ,32. AccoThe effect of urinary incontinence on mental health was examined by Goldacre et al., who demonstredted that women with urinary incontinence are more prone to depression and self-harm. Women with advanced symptoms of urinary incontinence suffered from severe depression three times as often as those with mild incontinence. Moreover, the relationship between urinary incontinence and anxiety symptoms is also higher in this group of patients. This problem has a significant impact on women’s social life, especially in the case of older patients. Women with pelvic organ prolapse feel humiliated and embarrassed, which frequently leads to withdrawal from family and social life. Some researchers have emphasized that depression and urinary incontinence might be strongly correlated with each other . Oh and Both this study and those cited above show how important it is to assess the QoL in patients with stress incontinence. Successful treatment not only improves patients’ physical well-being by eliminating an anatomical defect, but also positively affects their mental health. The results presented here show that healthcare professionals should not only provide surgical treatment but also assess and promote patients’ QoL and mental health. It therefore seems necessary to carry out further research in order to develop strategies for the prevention, early diagnosis, and treatment of patients with stress incontinence.Surgical treatment of stress incontinence with the TOT method may alleviate bothersome symptoms and improve quality of life.The IIQ-7 and the I-QOL questionnaires are useful instruments that measure the effectiveness of treatment and help patients understand how their QoL improved after surgery.Further research seems necessary to develop strategies of managing patients who suffer from stress incontinence and have lower QoL.Our paper has some limitations. Our findings may rely on an insufficient number of subjects and therefore this research requires a follow-up. However, our limitation was obtaining a homogeneous group of women who met the study inclusion criteria. To our knowledge, this is the first paper to analyse the relationships between the TOT procedure and the quality of life in women before surgery and 12 months after surgery.


P < 0.05. The second type of fetal heart rate monitoring images appeared in the highest among the diagnostic standards, and the difference in the second type of fetal heart rate monitoring images between the various diagnostic standards was statistically significant, P ≤ 0.001.Fetal heart sound is an important part of fetal monitoring and has attracted extensive research and attention from scholars at home and abroad in recent years. The fetal heart rate, extracted from the fetal heart sound signal, is one of the important indicators that reflect the health of the fetus in the womb. In this study, a maternal-fetal Holter monitor based on f ECG technology was used to collect maternal heart rate, fetal heart rate, and uterine contractions signals, isolate the fetal heart rate, and design an algorithm to extract the fetal heart rate baseline, acceleration, variation, wake-up cycle, and nonlinear parameters. Using statistical methods to analyze the average value and range of various characteristic parameters of fetal heart rate under continuous long-term monitoring, the results show that the baseline has a downward trend from 10 o'clock in the night to 4 o'clock in the morning and is the lowest around 2 o'clock in the morning. The area and acceleration time were significantly higher than those in the suspicious group. However, there was no significant difference in the number of acceleration values between the two groups; the proportion of small mutations in the normal group was lower than that of the suspicious group and the proportion of medium mutations was higher than that of the suspicious group. There is no statistically significant difference in maternal age, gestational age at childbirth, pregnancy comorbidities, and complication rates in the five-level interpretation system of ACOG (2009), RCOG (2007), SOGC (2007), and the United States (2007). The difference of pregnancy and parity in various images was statistically significant,  In recent years, with the full liberalization of the national second-child policy and the gradual increase of elderly pregnant women, the work pressure of the hospital's fetal heart rate monitoring department has also increased . How to The normal fetal heart rate not only reflects the healthy heart movement function of the fetus but also reflects the control function of the fetal nervous system . The occThe mother-fetal Holter monitor can continuously monitor and track the fetal heart rate in real time and then initially calculate the parameters such as acceleration, deceleration, and the number of mutations. The retrospective analysis of the fetal heart rate data obtained by the mother-fetal Holter monitor to extract more effective fetal heart rate characteristic parameters is of great help to the understanding of prenatal fetal development and the classification of fetal types. This is also important for further analysis of perinatal outcomes and early warning of the occurrence of fetal distress. After the remote fetal heart rate monitoring platform was launched, 1,200 pregnant women in early pregnancy were invited to participate in the trial of the research group, and the system was gradually introduced to the market. Based on the data from the cloud platform, our hospital is initially establishing a continuous health monitoring model of comprehensive indicators for pregnant women and fetuses and establishing a correlation with the Apgar score of newborns. The relationship between fetal heart rate baseline and monitoring time was analyzed, and the relationship between fetal heart rate baseline, baseline variability, and acceleration parameters and gestational age was studied. The long-term fetal heart rate monitoring of NST suspicious fetuses showed abnormal characteristics in terms of baseline fetal heart rate variability and fetal heart rate acceleration, including reduced fetal heart rate baseline variability, decreased proportion of normal variability, and decreased area of fetal heart rate acceleration. Umbilical artery blood flow S/D represents the ratio of placental vascular resistance to the placental blood supply. As the gestational age increases, placental vascular resistance decreases, blood supply increases, and the S/D ratio decreases. When placental resistance increases and blood supply decreases, then the S/D ratio increases. The results of this study show that the higher the S/D value of late umbilical artery blood flow, the higher the score of the fetal heart monitoring imaging scoring method, the higher the risk of adverse birth conditions, and the worse the neonatal outcome.The methods of obtaining fetal heart rate mainly include the heart sound transducer method, fetal electrocardiogram method, and ultrasound Doppler method . The heaAt present, the research direction of scholars on fetal heart sound monitoring technology is mainly three parts: fetal heart signal collection, noise reduction processing, and fetal heart rate extraction –11. In tResearchers use smart sensors to obtain fetal heart sounds and build a wireless platform to realize real-time monitoring of fetal heart rate . Among tDomestic clinical NST monitoring time and interpretation standards are both 20 minutes. If it is suspicious, further evaluation is required; that is, NST is reexamined; if NST is still suspicious after reexamination, NST needs to be reexamined and further ultrasound examination is required. The hospital conducts NST several times a day. Such repeated inspections increase the psychological pressure of pregnant women to a certain extent and occupy some medical resources. NST suspicious fetuses can be normal in a quiet state, so it is difficult to distinguish normal fetuses with suspicious monitoring characteristics from them. Therefore, it may be necessary to carry out long-term monitoring of fetuses with suspicious NST, in order to more accurately assess the fetal situation through longer monitoring.In the method of calculating the fetal heart rate, the researchers used a relatively simple method, that is, calculating the time interval between two fetal heart signal peaks and further calculating the number of peaks within one minute to represent the fetal heart rate. Relevant scholars use the time window method –21. FirsWith the rapid development of the Internet and mobile communication technology, more and more smart wearable devices and smart medical devices have begun to enter ordinary households. Pregnant women can easily transmit the fetal heart rate data collected by the smart medical fetal heart rate device to the cloud platform through a 4G or WIFI network. At the same time, the doctor side of the cloud platform obtains the fetal heart rate data, analyzes and diagnoses, and then transmits the diagnosis result. The pregnant woman informs the analysis result or reminds the pregnant woman to go to the hospital for examination immediately. This kind of “one-to-one” closed-loop management of doctors and patients allows doctors to directly manage and track their patients remotely. The storage of the cloud platform can accept all patients' medical history data and avoid the trouble caused by loss of paper data, so that doctors have a comprehensive understanding of the whole pregnancy of pregnant women.(1)Pregnant women who are more than 12 weeks pregnant .High-risk mothers, such as pregnancy-induced hypertension, pregnant women who have had an artificial abortion, spontaneous abortion, or abnormal fetal position.Pregnant women who want to know the health of their fetus at all times.It is not convenient for pregnant women who frequent the hospital.Applicable people:(2)Download the software and search the product name through the application store to get the APP.Connect the probe, open the application software, and turn on the Bluetooth function of the mobile phone.To find the fetal heart rate, place the probe on the abdomen and move it slowly. After the fetal heart rate is found, the mobile phone application will display the monitoring data, and the pregnant woman can record the fetal heart rate and upload the recording to the cloud to wait for the diagnosis result after the specified time.How to use:The hospital research group uses an ultrasonic fetal heart rate monitor based on a smart terminal, which is composed of smart hardware, straps, and smart terminals. After connecting the fetal heartbeat smart hardware with a smart terminal , the fetal heartbeat and APP applications will automatically start. It is an electronic medical device that can monitor, record, remotely consult, and share.The system adopts a multilayer architecture, using fetal heart monitoring equipment technology, cloud technology, mobile wireless communication technology, Bluetooth communication technology, remote data transmission technology, etc. to realize the design and application of the remote fetal heart rate monitoring platform, and the basic fetal heart rate of pregnant women, acceleration times, deceleration times, high and low change period minutes, long mutation period and amplitude, short mutation period and amplitude, fetal movement, and other parameters are monitored and recorded together. At the same time, functions such as display, storage, printing, and recall can be realized. The specific architecture is shown in Pregnant women can monitor and measure their own fetal heart rate at any time by turning on their mobile phones and fetal heart rate monitors at home or in other places with a network environment. After the pregnant woman connects the device by turning on the Bluetooth of the mobile phone and the client app, they read the information promptly and then look for the fetal heart rate, and the fetal heart rate monitor displays the test data, then records the qualified data for a specified time, and finally uploads the cloud platform to wait for the doctor to remotely diagnose. It saves pregnant women from the trouble of traveling to and from the hospital and waiting for treatment and brings safety and convenience to the health of pregnant women and fetuses.Doctors can log in to the cloud platform through the mobile client or PC to view the fetal heart rate monitoring information of pregnant women they manage anytime and anywhere. Through the push of mobile phone messages, the doctor can open and view the monitoring data recorded by the pregnant women. The system can display important data such as basal fetal heart rate, acceleration and deceleration times and changes, high and low changes in minutes, long mutation period and amplitude, short mutation period and amplitude, fetal movement, and other important data to doctors in each time period, providing clinical information for reference; it is convenient for doctors to remotely diagnose patients.Similarly, the system also has the function of remote consultation and message, because the initial design of the platform is based on the contracting system between doctors and patients. After the patient signs the informed consent form, the doctor and the patient basically maintain a one-to-one relationship throughout the pregnancy. You can leave a message in the system when you have doubts, so that the doctor can receive information pushes or return the consultation results after opening the doctor's terminal to accept the consultation, so that pregnant women can receive detailed guidance services. And the platform also provides pregnant women's social circle, so that pregnant women can share their experiences and conditions during pregnancy and promote their health during pregnancy.The time-domain analysis method is the most direct and the earliest method to analyze FHR. The time-domain analysis method and the morphological method have similarities. Clinical obstetricians analyze the physiological state of the fetus in the uterus based on the morphological characteristics of the fetal heart monitoring chart. The morphological parameters are mostly features that can be subjectively observed from the monitoring chart, such as acceleration and baseline trends. This parameter is also a time-domain feature. The time-domain analysis method directly extracts the waveform characteristics from the FHR signal, such as the fetal heart rate amplitude, average value, and acceleration duration, which has the advantages of clear physical meaning and intuitiveness. Time-domain analysis methods often use methods such as peak detection, analysis of variance, and histograms for analysis. Since the parameters obtained by the time domain analysis method are easier to understand and accept by clinicians, the parameters provided by the currently widely used electronic fetal heart rate monitors are mostly time domain or morphological parameters.In electronic fetal heart rate monitoring, the baseline of fetal heart rate is defined as the average fetal heart rate within 10 minutes of fetal heart rate fluctuations within 5 bpm, and the segments with acceleration, deceleration, and significant variation are excluded. The baseline must be a segment that lasts for more than 2 minutes in any 10 minutes. The segment can be discontinuous; otherwise, the baseline is uncertain during the observation period. In this case, the baseline can be determined by referring to the previous 10 minutes of images. Clinically, the heart rate chart of the fetus is traced and remembered through the mother-fetal Holter monitor, showing a fluctuating curve, subjectively assessed by obstetric experts whether it is normal. The baseline fetal heart rate refers to the imaginary line in the center of the graph. Therefore, the baseline extraction method is not unique. This research proposes an algorithm similar to scanning extraction.The fetal heart rate signal after preprocessing isM segments with a fixed length of one minute and discard those with a length of less than one minute. The h-th segment is d(h) and folded into matrix A. Matrix A is a matrix with (M-9) rows and 10 columns, and each row of the matrix is a fetal heart rate signal with a fixed length of ten minutes. The baseline value after average optimization processing isWe divide it into signal Fetal heart rate is stimulated by contractions, fetal movement, sound, palpation, etc.; it will accelerate or slow down for a short time and then return to the baseline level. This phenomenon is called a transient change in fetal heart rate, which is used to judge the fetal intrauterine.Accelerated fetal heart rate indicates that the fetus is in good condition. As an important part of the fetal electronic monitoring image, acceleration is a phenomenon in which the fetal heart rate rises rapidly, which is manifested in the image as a protrusion whose duration and heart rate amplitude both reach a certain threshold.At present, the analysis of fetal heart rate acceleration is mainly to classify and count the acceleration by visual inspection, and the information contained in the acceleration of the fetal heart rate signal is not sufficiently extracted. Using digital signal processing technology to digitize the fetal heart rate signal can obtain more objective parameter values, which is helpful to obtain more comprehensive information.Fetal heart rate deceleration is divided into early deceleration and late deceleration. Early deceleration and late deceleration mostly occur during delivery. This study only discusses the variable deceleration that occurs before delivery. Clinically, it is believed that the variable deceleration is caused by the compression of the umbilical cord and the excitement of the vagus nerve during contractions.The expression of the total area of fetal heart rate acceleration isL of the acceleration time of the fetal heart rate obtained above and the total area S of the acceleration of the fetal heart rate. The unit acceleration rate expression isWe use the total length Among them, PAR is the unit acceleration rate, which is characterized by the acceleration amplitude of the fetal heart rate per unit time during the acceleration period of the fetal heart rate monitoring. The unit acceleration rate PAR can be combined with specific parameters to obtain the number of grids occupied by the acceleration area in the fetal heart rate monitoring chart. The expression of the number of grids for acceleration per unit time isA total of 4 fetal heart rate acceleration parameters are extracted. The acceleration ratio is the ratio of the length of the acceleration time to the length of the effective superintendent care. The number of acceleration values is the number of acceleration values that occur within an average of 20 minutes, and the acceleration duration is the duration of acceleration within an average of 20 minutes. The acceleration area is the number of grids of the recording paper occupied by the acceleration coverage area in an average of 20 minutes. The recording paper is the fetal heart rate curve printed by the clinical fetal heart rate monitor, and each grid is 30 seconds long and 10 bpm high. The deceleration ratio is the ratio of the length of the deceleration time to the length of the effective supervisory time.The baseline variation of fetal heart rate is the result of the interaction between the vagus nerve and the sympathetic nerve. Sympathetic nerve excitement accelerates the fetal heart rate, and vagus nerve excitement slows down the fetal heart rate. It is generally believed that if the fetal heart rate variability is reduced, the fetus's intrauterine vascular reserve capacity is reduced, and there may be acidosis. The study of the fetal wake-up cycle is very important to determine whether the NST is reactive. If a normal fetus is in a quiet sleep cycle during the NST test, it is easy to be misdiagnosed as NST suspicious (or NST nonresponsive), thereby increasing the false positive rate. A normal fetus diagnosed as NST suspicious requires repeated inspections, which will waste a lot of medical resources.F (t) measured by the electronic fetal heart monitoring instrument. F (t) contains the mother's heart rate signal, fetal heart rate signal, the number of movements of the mother, and the uterine myoelectric signal and separates the fetal heart rate signal from the signal F (t). In the deceleration recognition module, the separated fetal heart rate signal f (t) is preprocessed, and the deceleration definition is used to extract and identify the fetal heart rate deceleration segment. The following is the specific identification method of deceleration.We receive the signal e (t), we use e (t) to calculate the characteristic parameter c to identify the fetal quiet sleep period. The first is to screen the effective signal e (t). For the fetal heart rate signal g (k), we divide it into signal segments min(k) with a fixed length of one minute. We use the effective fetal heart rate signal e (t) to calculate the characteristic parameter c. A characteristic parameter c is calculated corresponding to the effective signal e (k) of each minute length, as follows:b (k) is the baseline heart rate corresponding to the heart rate signal e (k). max(e (k)) is the maximum fetal heart rate in e (k), and min(e (k)) is the minimum fetal heart rate in e (k). max(b (k)) is the maximum baseline heart rate value corresponding to the effective signal e (k) of this segment, and min(b (k)) is the minimum baseline heart rate value corresponding to the effective signal e (k) of this segment.After filtering out the effective signal m points in the sequence and the approximate probability of a polyline segment connected by m + 1 points, so it reflects when the dimensionality changes from m. When it is increased to m + 1, a new mode possibility is generated. Approximate entropy Ap En has three parameters, n is the data length, r is the similarity tolerance, and m is the number of data segments. r is generally used as a percentage of the standard deviation of the data. The smaller Ap En value indicates the data comparison rules; the larger the Ap En value, the greater the probability of generating a new pattern, so the sequence is more complicated. The characteristics of Ap En value can be used to reflect the changes in the physiological characteristics of fetal heart rate. In recent years, the application of nonlinear dynamics methods to analyze physiological signals has gradually matured. Fetal heart rate signals are generally considered to have chaotic components. Therefore, nonlinear dynamics analysis is often more effective than common statistical methods or power spectrum analysis methods. This article uses Pincus' approximate entropy analysis method to study fetal heart rate parameters. We analyze the correlation between the Ap En value of fetal heart rate and heart rate variability and the fetal type and initially evaluate the clinical application value of this indicator in the early diagnosis of fetal distress.Approximate entropy reflects the difference between the approximate probability of a polyline segment connected by Because the data length is too long, the calculation time will be seriously extended, but the random 5-minute fetal heart rate data reflects less physiological information, and the fetal heart rate baseline value step of 1 minute will ignore a lot of information. Therefore, the approximate entropy of fetal heart rate variability with a relative time length of 2 hours and a step length of 10 seconds is more significant and statistically significant between the two groups.P = 0.01); those with a pH < 7.2 and higher surgical yield than pH ≥ 7.2 had statistical differences (P ≤ 0.001). The comparison of gestational age at delivery of 1200 fetal umbilical vein pH values is shown in 1200 cases of parturients all underwent umbilical vein blood gas analysis after the fetus was delivered. Basic and general comparisons found that there was a statistical difference in those with pH < 7.2 and less than pH ≥ 7.2  guidelines were abnormal, and RCOG (2007) stipulated suspicious and pathological fetal heart monitoring images as abnormal images, and SOGC (2007) stipulates that atypical and abnormal fetal heart rate monitoring images are abnormal images. The images above blue in the five-level evaluation standard of the United States (2007) are abnormal images. The positive predictive value of the four interpretation standards is very low, while the negative predictive value is high, as shown in Umbilical venous blood pH <7.2 was diagnosed as fetal acidemia, and Apgar 1 minute <7 was used to divide the neonatal adverse birth conditions. After assigning points for baseline, variation, and deceleration, the scores were added together to obtain the total score. A receiver operating curve was used to analyze the diagnostic ability of the total score on fetal acidemia. The receiver operating curve was used to analyze the diagnostic ability of the total score for the newborn's adverse birth conditions. The area under the curve was 0.62, which means that the diagnostic accuracy rate was 62.0%. According to the receiver operating curve analysis result, when the total score is ≥5.5, the sum of sensitivity and specificity is the highest. The receiver operating curve is shown in We use ACOG (2009) standards to classify intrapartum fetal heart monitoring. Class I fetal heart monitoring images are considered normal images, and Class II and III fetal heart monitoring images are considered abnormal images; SOGC (2007) normal and atypical images are considered normal images, and abnormal images are used as abnormal images; RCOG (2007) normal and suspicious images are used as normal images, and pathology is used as abnormal images. Taking the age, gestation, and parity, gestational week of delivery, pregnancy complications and complications, umbilical cord condition, birth sex, birth weight, amniotic fluid state, amniotic fluid volume, and labor process as independent variables, and whether the fetal heart rate monitoring image is normal dependent variables, multivariate logistic regression analysis was performed. The results showed that the fewer the births, the greater the possibility of abnormal fetal heart monitoring images, as shown in The adverse fetal outcome is the dependent variable. The third type of image of the better fetal monitoring grading system in the previous statistical analysis is abnormal. The score obtained by the fetal heart monitoring scoring method is combined with the uterine artery PI, RI, and S/D in the second and third trimesters. The PI, RI, and S/D values of umbilical artery in late and full-term pregnancy and full-term biophysical scores were used as independent variables to perform multivariate logistic regression analysis, and the regression equations were included as fetal heart monitoring image scores and late cord blood flow S/D values. The results show that the higher the score obtained from the fetal heart monitoring image, the higher the late cord blood flow S/D value, and the higher the risk of poor birth conditions of the newborn, as shown in Through the data analysis capabilities of the smartphone 4G network and the fetal heart rate monitoring cloud platform, the fetal heart rate sensing device can continuously track and intelligently remind pregnant women and fetuses during their stay at home. On the one hand, pregnant women and their family members can intuitively understand fetal development and health-related data through mobile phones. On the other hand, they can continuously record the monitored data into the pregnant women's personal information through mobile and WIFI networks and store, track, and correspond to the data through the cloud platform. The doctor's analysis makes medical assessments of the health of pregnant women and fetuses. If abnormalities are found, they will use smartphones to remind pregnant women of some precautionary measures they need to take or require pregnant women to go to designated medical institutions for in-depth examination and observation. In this project, the fetuses are divided into normal and suspicious groups based on the NST test results of clinical prenatal electronic fetal heart monitoring. Using long-term monitoring data, the baseline, acceleration, variation, wake-up period, and nonlinear parameters of fetal heart rate are extracted as characteristic parameters. According to the difference between the above characteristic parameters in the normal group and the suspicious group, we analyze the trend of the fetal heart rate characteristic parameters with the monitoring time and gestational week. The frequency of variable deceleration and the degree of prolonged deceleration are closely related to the adverse neonatal outcome. It is recommended that the index of the degree of variable deceleration should be included in the interpretation standard of intrapartum fetal heart rate monitoring. For prolonged deceleration, not only the extension of the deceleration duration but also the comprehensive extending deceleration should be considered.With the healthy delivery of pregnant women monitored by a large number of platforms, the remote fetal heart rate monitoring platform is gradually accepted and used by more pregnant women, which not only reduces the pressure of hospital admissions and pregnant women's waiting but also optimizes the process, saves time, and saves resources for pregnant women. At the same time, the one-to-one service system makes full use of the fragmented time of offline women's insurance hospitals at all levels and the resources of community responsible doctors. The hierarchical management of pregnant women, community responsible doctors, and specialist doctors is helpful for exploring the establishment of a family contract-style home. The continuous health management model combining online and offline for pregnant women provides a reference basis. In the initial design and application stage, the original connection and transmission of the fetal heart rate sensing device and the mobile phone were completed through the audio headset cable. Due to the forward-looking nature of the development team, taking into account the fact that later smartphones will gradually cancel the audio jack, such as the Apple 7th generation, the device is improved in time to adopt the Bluetooth transmission method, avoiding the disadvantage of not being able to connect the phone and the instrument with a data cable. However, since doctors also have time to rest, it is indeed impossible to fully achieve the anytime/anywhere service model advertised by the platform. Considering that if the number of users on the platform increases in the later period, a working model of doctors on duty will be established in the cloud to provide pregnant women with all the time.


Ei), and wavelet packet entropy (WpEn) of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. The classification accuracy with Ei and WpEn is higher than MSpEn, which shows that Ei and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed.Electroencephalogram (EEG) has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions (happiness and anger) with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy (MSpEn), wavelet packet energy ( Affective computing is a multidisciplinary field involving computer science, psychology, and cognitive science and its potential applications include disease diagnosis, human-computer interaction (HCI), entertainment, autonomous driving assistance, marketing, teaching, etc.,  , electrocardiogram (ECG), skin resistance (SC)  are commonly identified in the feature extraction phase. Compared to traditional time domain and frequency domain analysis, time-frequency based, and non-linear are more widely used  and wavelet packet entropy (WpEn). MSpEn is a combination of sample entropy and multi-scale analysis  is defined asWavelet packet decomposition is a generalization of the wavelet transform, which is with multi-resolution characteristics. It can finely analyze signals more than wavelet analysis, so it is very suitable for processing non-stationary signals such as EEG signals and has been widely used in the field of EEG signal processing. Wavelet transform is a multi-scale signal analysis method. It can characterize local features of signals in both time and scale  is the wavelet function, and t is the time.where The discrete wavelet transform is defined aswhere Wavelet analysis has been widely used in various fields as the main tool for time-frequency analysis. Compared to Fourier transform and short-time Fourier transform, wavelet analysis has the advantage of multi-resolution analysis, which can reflect the local details of signals on multiple scales. However, the traditional wavelet transform only further decomposes the low-frequency components of each decomposed signal. The high frequency components are ignored and the signal details are not adequately reflected. Wavelet packet decomposition is a generalization of the wavelet transform, which is with multi-resolution characteristics. In order to extract the EEG features, the original signal is decomposed by the Mallat algorithm, and the wavelet coefficients of the corresponding nodes are reconstructed to obtain the final coefficients.To reduce noise and other factors, high frequency components are filtered out, leaving a frequency range below 256 Hz. After four layers wavelet decomposition, the original signal can be decomposed into 16 bands.i (Ei) is defined asThe wavelet packet energy of band N is the number of corresponding band coefficients, ni is the wavelet packet coefficient. The total wavelet packet energy is defined aswhere The wavelet packet energy distribution is expressed asThe energy ratio of each wavelet packet node is calculated after the wavelet packet decomposition of the original EEG signals. The result is shown in According to the above analysis, EEG activities are mainly concentrated in the  ~ . Therefore, it is not necessary to use all frequency bands in actual analysis. In order to cover the EEG rhythm as much as possible and avoid the effects of noise and artifacts in EEG records, this article only deals with wavelet packet nodes. The wavelet packet energy of the packet node  ~  is extracted and analyzed whether it is a contribution to subliminal emotion face recognition.Information entropy can provide a quantitative measure of information contained in various probability distributions. It is a measure of the degree of uncertainty and can be used to estimate the complexity of random signals. The energy distribution of wavelet packet decomposition coefficient and information entropy are combined to define WpEn as:Ei) on the classification performance.This study employed and evaluated two classifiers, the decision tree algorithm  according to labels. Set the attributes set of D as An, where An = {A1, A2, ..., An}, the ith attribute of Ai with w different values is defined as {ai1, ai2, ..., awi}. The data set is divided into w different subsets i ≤ w) according to the attribute, where Ci in the subset Set the training set as pi = |Ci|/|D|. The information entropy of subset divided according to attribute Ai is defined aswhere Gain(Ai) represents the information gain of the training set divided by the attribute Ai, which is defined aswhere SplitInfoA(D) is defined asThe split information Ai is defined asThe information gain ratio of the dataset divided by the attribute Step 3: According to the attribute which is corresponding to the maximum information gain ratio, the current data set is divided into different subsets, the decision tree branches are established, and the new child nodes are formed.Step 4: Steps 2 and 3 are recursively called for the new node until the class labels are the same in all nodes.Random forest algorithm is a typical multi-classifier algorithm. The basic classifier that constitutes the random forest algorithm is the decision tree. The basic principle of the random forest algorithm is to use the resampling technique to form a new training set by randomly extracting samples. Then, decision tree is modeled and composed of random forests, and the classification results are used for voting decisions Bo, .The random forest algorithm is similar to the Bagging algorithm in that it is resampled based on the bootstrap method to generate multiple training sets. The difference is that the random forest algorithm uses the method of randomly selecting the split attribute set when constructing a decision tree.T training sets, S1, S2, , ST.Step 1: Use the bootstrap method to resample and randomly generate m attributes are randomly extracted from M attributes as the split attribute set of the current node, and the node is split by the best classification among the m attributes.Step 2: Generate a corresponding decision tree using each training set; before selecting attributes on each internal node, Step 3: Every tree grows intact without pruning.X, use each decision tree to test and get the corresponding category.Step 4: For the test set sample T decision trees is taken as the category to which the test set sample X belongs.Step 5: Using the voting method, the category with the most output in the Random forest algorithm can be divided into the following steps:However, the random forest algorithm also has deficiencies. This article uses a random forest algorithm based on the C4.5 tree algorithm. The attribute division strategy is based on the level of information gain rate to select the characteristics of the division. The principle of attribute division has the disadvantage of biasing features with many values. The voting on the classification result of the decision tree adopts the “majority voting principle,” which means the number of votes is the final classification result, and the strength of the decision tree classifier cannot be distinguished. This article will improve the random forest algorithm and apply it to the classification of subliminal emotional faces.Random forest algorithm is improved from the following mechanisms:(1) In the choice of test attributes, attribute division by information gain rate will have the characteristic of biasing the features with more values, the Pearson coefficient is introduced to compensate.The C4.5 decision tree algorithm uses the information gain ratio to select the test attribute. The larger the information gain ratio, the stronger the correlation between the attribute and the class attribute, the possibility of the attribute being selected as the test attribute the larger.The C4.5 decision tree algorithm takes into account the influence of attributes on class, but it does not involve the influence between attributes. If an attribute has a strong correlation with other attributes, there will exist redundancy between them. Therefore, the Pearson coefficient is used to express the temporal correlation of attributes in this article, and the influence of attributes with high correlation is eliminated. The quotient of the covariance and the standard deviation is defined as the Pearson correlation coefficient, which can reflect the degree of correlation between two variables. The Pearson coefficient is defined asWhen the Pearson coefficient is 0, it means there is no correlation between two variables. When the Pearson coefficient is positive, it indicates that there is a positive correlation between the two variables. The larger the value, the greater the positive correlation between the two variables. When the Pearson coefficient is negative, it means that there is a negative correlation between two variables. The greater the value, the greater the negative correlation between two variables. The range of Pearson's coefficient is .The improved attribute division algorithm is defined asAccording to the improved attribute selection method, the attribute which has the high information gain ratio and low correlation with other attributes, has the greater probability of being selected as the test attribute.(2) The voting decision process is an important mechanism of the random forest algorithm. Random forest algorithm adopts the principle of majority voting, assigning each decision tree the same weight, and ignoring the difference between the strong classifier and the weak classifier, which affects the overall classification performance of random forest. This article uses the weighted voting principle to improve random forest algorithm. During the formation of the random forest, according to the classification performance of each decision tree, each decision tree is assigned a corresponding weight. Then, the final classification effect is obtained by weighted voting.Actree corresponding to the sample set can be obtained. The larger Actree, the better the classification effect of the decision tree, which belongs to the strong classifier. Actree of each decision tree is used as the weight of the corresponding decision tree and add the weights corresponding to decision trees with the same output class target. Finally, classification result with higher weight is the final category. In the process of generating a decision tree, the bagging method is used to extract samples from original training set with replacement to form a sample set, and the decision tree classification accuracy rate Eiare extracted as feature vectors for the classifier, and the classification results of different features are compared. Bai et al. .First, low pass filtering is applied to each EEG signal segment. According to the sampling theorem, the maximum frequency of the signal is about 500 Hz. To reduce noise and other factors, high frequency components are filtered out, leaving a frequency range below 256 Hz. The sample entropy has a strong ability to characterize nonlinear sequences on a macroscopic scale and cannot describe the details. The wavelet packet decomposition has excellent description ability in detail. Therefore, MSpEn, WpEn, and i et al.  pointed n et al.  pointed Ei extracted by wavelet packet transform and MspEn calculated by multi-scale sample analysis is put into decision tree algorithm, respectively. The averaged classification performance of the decision tree algorithm with MSpEn, WpEn, and Ei on 10 subjects is shown in In our experiment, there were 80 sets of sample data for each subject, 40 groups were selected as training samples for training the proposed model, and the remaining 40 groups were used as test samples for verifying the performance of the model. WpEn and Ei, and WpEn are input to the decision tree classifier. The experimental results show that decision tree algorithm can effectively classify subliminal emotional faces combined with the three feature vectors, and different feature vectors have different classification capabilities for subliminal emotional faces. The classification accuracy with Ei as a feature vector is significantly higher than other features, and its average classification accuracy is up to 94.33%. The classification accuracy using WpEn as the feature vector is slightly lower, and its average classification accuracy is 93.32%. The classification accuracy with MSpEn as the feature vector is the lowest, and its average classification accuracy is 75.52%.Ei and WpEn are adopted as the input feature vector of the decision tree classifier, the average classification accuracy is significantly higher than MSpEn and the classification accuracy is more stable.In order to compare the classification performance of different features more intuitively, Ei and WpEn is better compared with MSpEn, which shows that wavelet packet decomposition features are more powerful for subliminal emotional face recognition.In summary, a decision tree classifier can effectively classify subliminal emotional faces. In the perspective of feature vectors, classification effect of Ei extracted by wavelet packet transform and MspEn calculated by multi-scale sample analysis are input into improved random forest, respectively. The averaged classification performance of the improved random forest algorithm with MSpEn, WpEn, and Ei on 10 subjects is shown in This article further studies the classification effect of improved random forest algorithm on subliminal emotional faces. WpEn and Ei was evidently better than those based on other feature types under the same conditions, while the accuracy can reach up 96.75%. While WpEn and MSpEn are used as input feature vectors, the unconscious emotions can be classified combined with improved random forest. The highest classification accuracy can be achieved at 93.38 and 88.89%. For the classification results with Ei and the decision tree algorithms and the improved random forest algorithms, it was noted that the random forest algorithms outperformed the decision tree algorithm by 1 ~ 6% for most subjects.As we can see from Ei and WpEn as the input feature vectors of the classifier compared to MSpEn. Due to the individual differences of the subjects, the classification accuracy of different subjects shows a small range of fluctuations, and the overall performance shows that Ei and WpEn have a stronger classification ability than the MSpEn.The comparison of classification accuracy of improved random forest classifier with different inputs feature vectors is shown in Ei as input feature vectors. It can be seen that the classification accuracy of the decision tree algorithm of only one subject is higher than that of the improved random forest algorithm. The classification accuracy of the improved random forest algorithm of the remaining 9 subjects is higher than that of the decision tree algorithm. Overall, the classification accuracy of the improved random forest algorithm is higher than that of the decision tree algorithm, and the average classification accuracy is improved by 2.42%. This article further analyzes and compares the classification accuracy of the two classifiers under the same feature extraction method. The classification results are shown in At present, there are few studies and references on subliminal unconscious emotions. In order to confirm the effectiveness of the proposed method, this article compares the classification results of several different classifier algorithms. The experimental results are shown in Ei and WpEn obtained by wavelet packet decomposition have obvious advantages for subliminal emotion face classification, and their ability to classify emotional faces is significantly stronger than MSpEn. From the perspective of the classifier, improved random forest is superior to decision tree.In summary, combining three features with decision tree classifier and an improved random forest classifier can realize the classification of subliminal emotional faces. From the perspective of feature extraction, Ei and WpEn) with two classifiers is significantly higher than MSpEn, which shows that wavelet packet decomposition can better characterize the EEG signals triggered by subliminal emotional face stimuli.From the perspective of psychology, we explore the neural mechanisms of brain activity under subliminal face stimulation , the OMO Course Group Advanced Computer Networks of Shandong Normal University, the Teaching Team Project of Shandong Normal University, Teaching Research Project of Shandong Normal University 2018Z29), Provincial Research Project of Education and Teaching (No. 2020JXY012), and the Natural Science Foundation of Shandong Province . The content of this manuscript has been presented in part at the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Shi et al., Z29, ProvThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.