Reversible data hiding (RDH), a promising data-hiding technique, is widely examined in domains such as medical image transmission, satellite image transmission, crime investigation, cloud computing, etc. None of the existing RDH schemes addresses a solution from a real-time aspect. A good compromise between the information embedding rate and computational time makes the scheme suitable for real-time applications. As a solution, we propose a novel RDH scheme that recovers the original image by retaining its quality and extracting the hidden data. Here, the cover image gets encrypted using a stream cipher and is partitioned into non-overlapping blocks. Secret information is inserted into the encrypted blocks of the cover image via a controlled local pixel-swapping approach to achieve a comparatively good payload. The new scheme  The communication network has been growing at a rapid pace, and so has content sharing. Furthermore, the number of internet users has grown substantially. A secure way of communication plays a vital role in shielding secret information. Hence, cryptography and data-hiding approaches have been in focus ,2. CryptDigital watermarking: In digital watermarking, unique information gets embedded within the cover medium, which will be used for data authentication or copyright protection . Unique Steganography: Steganography is similar to the watermarking approach that varies in its purpose. In steganography, the information hidden is much larger in capacity, and the receiver does not care about the cover image used for concealing purposes. Here, the cover medium at the receiver end is not so significant after the extraction of hidden information . As noteReversible data-hiding (RDH) schemes are derived from steganography, which aims at lossless recovery of the actual cover medium, along with the extraction of the hidden secret information ,8. In thTwo main approaches in RDH are histogram shifting and difference expansion. In the case of difference expansion-based RDH, the adjacent pairs of pixels are selected, and their difference is expanded to hide the message bits ,15. OverRDH in natural images  and RDH Retainment of entropy and histogram: The bulk of the existing RDH schemes in encrypted images predominantly alters the pixel distributions in the encrypted image during data hiding. However, this manuscript proposes an RDH scheme called the Embedding capacity is independent of the structural properties of the cover image: The Real-time performance: The practical computation time incurred during the embedding and extraction phase is much less, proving its real-time applicability.Less overhead at the receiver side: Machine learning models are explored in RDH to improve the efficiency of the recovery process. However, these trained models must be shared with the receiver before the image transmission. In comparison, the The proposed Furthermore, the manuscript discusses literature works on RDH schemes in the encrypted images in In this section, we discuss a few RDH schemes in encrypted and non-encrypted images. The scheme reported in  proposedThe RDH scheme proposed in  uses a hA bit of secret data is embedded in every block using the data-hiding key , but in Instead of creating different channels as in , the encSecret data can also be embedded in a non-encrypted domain. The quality of the stego image is very important as the original pixels are modified while embedding the data . Unlike To embed the secret information, most of the existing schemes have been utilizing the vacant room created by compression or by shifting or finding pixel differences, and a few have tried modifying the original bits of the pixel, either MSBs or LSBs. A sufficient amount of redundant space is required to improve the embedding capacity. We should also reduce the complexity that leads to errors during the recovery process. Here, we propose a novel A detailed literature review reveals that the existing RDH schemes in encrypted images have low embedding rate, and there is wide scope to improve this parameter without compromising the image restoration capability. Another important observation is that the majority of the pre-existing works on RDH in encrypted images distort the encrypted pixel values, which may result in a new pixel distribution in the marked image. A uniform pixel distribution is expected from an encrypted image, and the unusual pixel distribution in the image due to the data-hiding process will lead to security concerns. In this research, a novel Most of the existing schemes modify the entropy of the encrypted image after hiding the additional information, while the proposed scheme does not. Thus, the proposed scheme comes under the banner of a secured RDH scheme from the encryption algorithm purview;A slight change in the entropy of the fully encrypted image is an indication of the presence of information. As we retain the same entropy of the encrypted image, the proposed scheme does not expose the presence of hidden information;The properties of the proposed The advantages of the proposed E is fed as an input to the RDH system. The system handles the input image as blocks of identical size without overlapping.The E of size E is considered to be an encrypted image, processed as independent E is given as follows:N is the maximum blocks of size E, which are not overlapping. Now,In this phase, data are embedded in an image Furthermore,D is chosen accordingly. The properties of the secret data D are mathematically defined in Equations (Since the newly introduced quations  and 5)..MPSA schE as non-overlapping blocks to embed the secret information. In the current scenario, each Algorithm 1: Proposed pixel-swapping-based approach for RDH.We know that when the size of the encrypted image is Let Thus, the total embeddable secret informationFurthermore,In the proposed local pixel-swapping process, four different ways are formulated to address the embedding of four different bits such as M is already shared with the receiver.The above-mentioned technique is computed on each M make up the inputs to this phase. At first, we generate a pseudo-random matrix M, which will hold values in the range [The process of extracting the hidden secret information and the restoration of the cover image from the marked image The receiver employs the same local pixel-swapping process to recover the 2-bit information hidden in each block of the image Then, the four different pixel arrangements, Algorithm 2: Secret data extraction and image restoration.Kth block of Kth block of Applying Equation  to all tquations  through quations . We perfiQ as one of the The correlation between pixels in the decrypted blocks Q is computed as follows:The smoothness measure The smoothness measure of every overlapping Case 1: If Case 2: If Case 3: If Case 4: If Consider F and the extracted message D. An example of the proposed Algorithm 3: Computing multi-directional smoothness measure.I.e., the correct block identified via the smoothness measurement calculation helps in recovering the hidden data in that block. It is obvious that the data hidden are 00 if the recovered block is Equation , which cThe sequence of operations performed to hide the data and to restore the image is schematically shown in P where The receiver also processes the received image Embedding rate (ER)Bit error rate (BER)Peak signal-to-noise ratio (PSNR)Structural similarity index (SSIM)EntropyHistogram analysisImage recovery analysisComputation timeThe RDH scheme can secure both the cover image and the secret information. To validate the proposed I images , and theI images  databaseIn the next sub-sections, we discuss all the above-mentioned results in detail.Embedding capacity, payload capacity, or embedding rate are different terms used by researchers working in the domain of data hiding, which eventually points to the same context. It refers to the quantity of data that one can hide on a cover medium. This has been an ongoing research activity to improve the embedding rate by introducing a new algorithm or by modifying the existing schemes. Embedded rate (ER) can be expressed as follows:Bit error rate (BER) is also an important parameter in any RDH approach. This parameter is an implication of how efficiently the RDH algorithm retrieves the embedded secret information. BER refers to the bulk of data recovered incorrectly, considering the quantity of data that was hidden in the cover medium. The best schemes should result in a BER value of 0.The ER that is achievable from the In addition to this, we analyzed the BER from well-known images such as airplane, baboon, boat, and peppers to showcase the results of the experiment. airplane, boat, and peppers, are restored without any kind of error with an baboon image was restored completely only when the block size was baboon image as per the newly introduced scheme is baboon image has a highly textured property that leads to more errors when processed with low block sizes. This led to a lower embedding rate of the The results claim that the three well-known images, I and the restored image is R. The ∞ for a good approach.PSNR is a factor of the maximum possible value of a pixel to the error that distorts the original quality of the image. It is expressed as follows:The SSIM gives the structural similarity index, another parameter for effective quality estimation of the restored image. Here, the structural quality degradation of the restored image gets quantified based on the luminance masking and contrast masking factors. SSIM should be 1 for the correctly recovered original image. The results of PSNR and SSIM values are also tabulated in baboon, were restored without any loss of information. This yields a PSNR ∞ and SSIM measure of 1 with a block size of The PSNR values returned from the I is the image, Y indicates the peak gray level , and L. If the encrypted image is highly random, then the entropy measure should be near 8 for a grayscale image.The encryption algorithm’s efficiency can be analyzed from (a) the entropy of the encrypted image and (b) the histogram of the encrypted image. Entropy estimates the level of randomness of an image pixel. It characterizes the texture of the image. The entropy is defined as follows:The value of entropy measured over the original cover image, the image acquired through encrypting the cover image, and the image acquired from hiding a given sequence of secret data are given in airplane image, over the image acquired through encrypting the image, and over the image acquired from hiding a given sequence of secret data is shown in The histogram of an image indicates how the image pixels are distributed. Any kind of changes that occur during data hiding will alter the original image’s pixel distribution. A uniform distribution of pixel values is anticipated from an encrypted image. In such a case, the encrypted image histogram will look flat. Hence, changing encrypted image pixel values during the data-hiding process can lead to a different histogram. However, as we discussed earlier, the proposed Misc., Aerials, Sequences, and Textures. We considered all 39 Misc. images, 38 Aerials images, 69 Sequences images, and 64 Textures images of the USC-SIPI image dataset and 10,000 images from the BOWS-2 dataset for experiments. The image recovery statistics are given in The images in the USC-SIPI image dataset are of four different categories: The success of the proposed airplane image to analyze the computed smoothness measure. The block size used for the analysis is of size We selected 20 random blocks during the image recovery process of the airplane, baboon, boat, and peppers. All three images, except the baboon image, are recovered correctly when Baboon is a highly textured image that was restored successfully with baboon was reduced to A comparison of the proposed orted in ,46,47,48better embedding efficiency of the proposed ∞ and SSIM of 1. It is noteworthy to mention that none of the schemes other than the proposed scheme retains the entropy of the encrypted image and, similarly, the histogram of the encrypted image despite embedding the secret information.The results listed in The system configuration employed for the experiments includes a x64-based Intel(R) Core(TM) i5-9300H CPU @ 2.40GHz with 8GB RAM. In a real-time scenario, the efficiency of an algorithm predominantly rests on time complexity. Here, we disclose the execution time taken by the proposed K), where M is the number of rows in the processed image, and N is the number of columns in the processed image.The average execution time incurred by images under various classes of the USC-SIPI dataset while processing as This paper proposes a computationally efficient and novel entropy and histogram retained


Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results. Compared with upstream genomics, transcriptomics, and proteomics research, metabolomics can expand from the field of genes to the area of small molecules, realize the integration of small molecules into bioinformatics, and combine other omics technologies to discover systematic biomedicine research in the field. Nowadays, as one of the most widely used platforms of metabolites, mass spectrometry can repeatedly detect thousands of metabolites from cells6, tissues, and biological fluids, assist biomedical research in detecting multiple feature subsets, and is of great significance in promoting biomarker screening9, pathological research12, and drug development15. In metabolomics, researchers used mass spectrometry to analyze changes in the levels of metabolites in organisms to study the dynamic development of organism physiology. Although histopathology is still the gold standard for modern disease diagnosis, proteomics and metabolomics studies based on mass spectrometry technology have been widely used in the diagnosis of various diseases, which can Achieve early and accurate diagnosis of clinical-level diseases16. Exploring the metabolite molecules corresponding to biomarkers in the metabolic process of conditions will help elucidate the mechanism of disease in the disease process and find marker molecules that play an essential role in the occurrence and development of diseases17.Metabolomics plays a crucial role in biological systems and aims to investigate the development of biology and disease by studying all metabolites in biological samples18. Therefore, there is an urgent need to find an efficient data processing method to extract and analyze large-scale multidimensional raw spectral data, especially raw Mass spectral data generated from clinical samples.Due to the difference in sample sampling time, the sample length is different. At the same time, mass spectrometry data has a high feature dimension, which contains much biological information and a large amount of redundant information and noise. How to remove noise and errors from such high-dimensional features and extract useful biological information has become one of the leading research points of bioinformatics22. These machine learning methods are suitable for preprocessed mass spectrometry data, but differences in preprocessing pose a significant challenge for any comparative analysis. As a critical step in sample classification, data preprocessing is used to align mass spectrometry sample data and eliminate noise errors and redundant information from original mass spectrometry data. However, taking inappropriate preprocessing operations will result in the loss of useful information in the data. Therefore, proper preprocessing methods should be used when performing data preprocessing23.At present, many machine learning (ML) models have been widely used for accurate diagnosis of diseases and biomarker mining under mass spectrometry data, such as support vector machine (SVM), random forest (RF), Bayesian neural network (NB), and Linear discriminant analysis (LDA) has achieved good resultsThis paper mainly aims at the uneven raw data containing noise and uses two-dimensional mass spectrometry data to achieve accurate diagnosis of diseases and mining of biomarkers. The features of mass spectrometry data are constructed by the data preprocessing method, and the feature selection is carried out by the chi-square test method. In training ten supervised learning models, the model's classification effect is evaluated through various evaluation indicators.A step-by-step overview of the data processing in this paper is shown in Fig. The datasets used in this experiment mainly include the colorectal and liver datasets from Proteome Xchange and the coronary heart disease datasets provided by Thermo Fisher Scientific. Table 24. The plasma data is divided into two batches, in which 1-Batch contains 21P samples and 20N samples, and 2-Batch contains 38P samples and 23N samples. The pathogenesis of coronary heart disease is studied by identifying new biomarkers in plasma (Dataset connection: https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.s8k81).The first dataset was provided by Thermo Fisher Scientific in Massachusetts and included plasma and urine samples from 59 coronary heart disease (CHD) patients and 43 healthy controls (Control)25. The liver metastases of the same colorectal liver metastases and their adjacent tissue samples have certain similarities in the related proteins. Still, they have significant differences in the number of distributions, and the liver metastases are much more extensive in some essential proteins. Therefore, try to use the critical information of its high-precision mass spectrometry samples to distinguish the two samples through machine learning algorithms. .The second dataset is from the Proteome Xchange platform, including 30 colorectal liver metastasis (CRLM) samples from the same patient with colorectal liver metastasis and 30 adjacent normal tissue samples (Control)In the process of mass spectrometry data collection, the collection length of each sample is inconsistent, and the collected data is relatively sparse, so it is necessary to find a way to align the mass spectrometry data. In addition, there is external interference in the process of mass spectrometry data acquisition, and the collected data will contain a lot of noise. This feature construction method can align samples with different lengths, reduce the noise in mass spectrometry data and reduce the redundancy of original data. Figure 26.Data feature selection is widely used in extensive data mining, analysis, and machine learning. Especially in high-dimensional mass spectrometry data, selecting important data features is a critical factor in identifying disease biomarkers. The chi-square test is a conventional feature selection algorithm used for feature selection in different research worksThe Chi-square test is a statistical analysis method specially used for counting data in statistical analysis. Using the 2, the stronger their correlation. Get a sorted list of features according to the χ2 value, then select features according to the list. The chi-square test is used for feature selection, and the chi-square value estimates the importance of high-dimensional data features. This method can quickly find the relevant information in the data features and select the feature subset with the most significant correlation and the least redundancy27.Among them, F is the feature; 28 fits the relatively linear data with less loss and enables the fitted model to better predict the data. A Support Vector Machine (SVM)29 defines a linear classifier with the most significant margin in the feature space, capable of separating samples from two different classes. When predicting a new sample, K-Nearest Neighbor (KNN)30 judges which category the sample belongs to according to which type the K samples closest to the sample belong. The decision tree (DT)31 obtains the final result through the decision-making process and discrimination of the problem by the tree. Random Forest (RF)32 is a method that uses multiple decision trees to train, classify and predict sample data. Lightweight Gradient Boosting Machine (LightGBM)33 is a fast, distributed, high-performance decision tree-based gradient boosting algorithm that starts with weak models and trains models iteratively, each model adding to the predictions of previous models to produce a reliable Overall forecast. Naive Bayes (NB)34 estimates the conditional probability of each category by calculating the frequency of occurrence of each type in the training samples and dividing each feature attribute, and records the results, and finally outputs the classification of the samples. Bagging35 estimates a sample by averaging the posterior probabilities of the non-uncounted nodes it falls into. Reinforcement learning (Adaboost)36 trains different classifiers on the data set and then combines these weak classifiers to form a more robust final classifier. The classification effect is better than the conventional weak classifier. Classifier model effect. The deep learning model is a multilayer perceptron (MLP)37 or a multilayer perceptron. This forward-structured artificial neural network maps a set of input vectors to a group of output vectors and is used to fit complex functions or solve problems—classification problem.In order to verify the original mass spectrum, Table 38.To evaluate the prediction model, this paper uses seven evaluation indicators widely used in the medical field to assess the classification effect of ten models, including Precision, Specificity, Sensitivity, Matthew Coefficient (MCC), Accuracy, F1-score, and Area under roc curve (AUC)To evaluate the feasibility of the method proposed in the paper, the precision rate, sensitivity, specificity, Matthew coefficient, and accuracy rate were evaluated, respectively. The calculation formula is as follows:Among them, true negatives (TN) are the number of predicted healthy labels that match the true healthy labels; true positives (TP) are the number of predicted disease labels that match the actual disease labels; false negatives (FN) refer to the number of patients with disease The number of sick labels that are misidentified as healthy; false positives (FP) are the number of healthy labels that are predicted to be diseased.F1-score is the harmonic mean of precision rate and recall rate. The F1 score is calculated to measure the classification performance of samples of different categories. The calculation formula is:39. Calculated as follows:AUC refers to the area covered by the ROC (receiver operating character) curve. Since it is difficult for ROC to reflect the difference between models, AUC can remember the difference between models as a numerical value. The closer the AUC is to 1, the better the performance of the modelAmong them, positive class indicates that the category is a positive example, Considering that the original mass spectrometry data set is too large and inconvenient for analysis, it is necessary to use open sources of mass spectrometry software such as MZmine2.53, Raw Converter, and MSConver for sampling to extract detailed key data information in mass spectrometry. The main steps of sampling through MZmine2.53 mass spectrometry software in this paper are: (1) Input the original Raw file into MZmine2.53 software and save important mass information through mass detection; (2) Set the mass detector to exact Mass for sampling, set the noise level to 0, and finally, generate chromatographic column information through ADAP Chromatogram builder, to ensure that all data are complete; (3) Export all mass spectrometry information related to this experiment from the generated feature lists, and the exported CSV file contains 24 columns Mass spectral information, where important information includes Mass, retention-time, peak-height, and peak-area. These raw data are sequentially transformed into regular feature matrices through a series of subsequent preprocessing methods for subsequent supervised learning models.In the process of data collection, the length of each sample is different because of the different sampling time. In order to maintain the consistency of each sample in the M/Z dimension and construct suitable data for the machine learning model, it is necessary to use feature engineering method to construct and align the original data. Due to the difference of sampling time in the public coronary heart disease data set, the data set is divided into two batches. In this part of the experiment, the difference between the two batches was ignored and the samples of the two batches were mixed. The peak area and peak height of M/Z dimension are constructed by MSFS method, and the M/Z values in all samples are traversed, and the minimum and maximum M/Z values of 54 and 1223 are obtained. The minimum value and the maximum value are taken as the start value and the end value of the sliding window. Then, by setting the sliding window step size, the average of peak area and peak height in each sample window is taken as the feature value after feature construction.Taking two mixed coronary heart disease data sets as the research objects, the training set and the test set are divided according to the ratio of 2:1, the step size of the sliding window is 0.1, and the feature construction is carried out on the original data. The chi-square test is used to screen the minimum non-redundant feature combination. Training is performed on ten supervised learning models, and the classification results are evaluated by various evaluation metrics Table . The optThe 1.20.3 version of Numpy and the 1.3.4 version of Pandas were used to process the data, using the CH2 feature screening algorithm provided in the 0.24.2 version of Sklearn and ten kinds of Supervised learning algorithms .Under different window steps, the data exhibits different distributions, which provide different degrees of information for the machine learning model. This part compares the feature information extracted from five sliding windows with different step size parameters and finally determines the optimal sliding window step size parameters through the classification results on ten supervised learning models. Table To explore the effect of different sliding window step sizes on sample prediction, the same data preprocessing, feature screening, and model validation methods are used throughout the experiment, under the condition that only the window step size is changed. The influence of five different window steps on the classification results was verified on the coronary heart disease data set to determine the optimal window parameters. Through Table As shown in Fig. 40. Table This part of the experiment uses the widely used mass spectrometry software MetaboAnalyst 5.0 version to analyze the public coronary heart disease data set. The results are compared with the method proposed in this paper41, and verify the classification results under the optimal window step size. When there are two features on peak area and peak height, on the bagging model, the AUC is 1.000. The AUC of the comparison paper is 0.999 when there are eight features. Compared with the results of the comparison paper, the method proposed in this paper uses fewer features to achieve the same results as the comparison paper, proving the feasibility of the method proposed. The experimental results show that the method proposed in this paper has superior performance in distinguishing coronary heart disease and normal samples.Divide the coronary heart disease data set according to the ratio of the comparison papers With a sliding window of 1 as the step size, using the chi-square test, Peak-area and Peak-height screen out the two feature combinations with the most considerable chi-square value on the data of two batches mixed and two batches of mutual verification, respectively. Using visualization, use these feature combinations to draw scatter plots and further verify the classification effect between coronary heart disease samples and healthy samples. At the same time, it is confirmed that batch differences between batches 1 and 2 due to differences in collection time are generated on the coronary heart disease data set. Figure From Fig. From Fig. By visualizing the results in the previous summary, it can be found that there is a significant difference between different batches in the coronary heart disease dataset. To verify the influence of the differences between the two batches of samples on the experimental results, this part of the experiment uses different batches of samples to test each other to verify the classification effect of the model. The coronary heart disease data set contains two batches of samples. This part of the experiment considers the differences between the two batches of samples. Based on the optimal parameters of the previous model, one batch of samples is used as the training and verification sets. One batch of samples is left as the test set to verify the classification effect of the model further.The results in Table The significantly different mass-to-charge ratio intervals found by the method proposed in this paper have biological significance. By measuring the proteome expression profile and phosphorylated proteome profile of coronary heart disease and normal samples, it is possible to discover the precise treatment of coronary heart disease—potential biomarkers. Taking the feature subset corresponding to the optimal classification result in the mixed dataset when the sliding window step size is one as an example, two different features are found on Peak-area and Peak-height, Peak-area and Peak-height. The intersection is the F_59 difference feature. Using the feature to construct the index can determine the original cytoplasmic ratio interval and the biomarker of coronary heart disease. Formula 3.1 calculates the original feature interval through the feature through the sliding window calculation method. The calculation formula is as follows:Among them, The original quality interval can be calculated by formula  by usingOn the Peak-area and Peak-height, aiming at the remarkable feature F_59 corresponding to the original Mass interval 113–114, we traverse the common Mass values of all samples in the interval, and find that the mass value of 113.02 is the remarkable marker of healthy samples and sick samples, and the corresponding biological compounds can be obtained by combining metabolomics to compare the database in the later stage, thus revealing the pathogenesis of coronary heart disease.This part of the experiment uses the public colorectal liver metastases dataset to verify the classification results of the proposed method on other mass spectrometry datasets. In this part of the experiment, the training set and the test set are randomly divided according to the ratio of 7:3. By traversing the M/Z values in all colorectal liver metastases samples, the minimum and maximum M/Z values were 400 and 1600, which were used as the starting and ending values of the sliding window, respectively. Since the original data sparsity of the colorectal liver metastases is different from that of the coronary heart disease dataset, it is found that setting the sliding window step size parameter to 0.1 can effectively extract the effective original information of the colorectal liver dataset. Finally, the chi-square test is used for feature screening, model training is carried out on ten supervised learning models, and the classification effect is evaluated through various evaluation indicators.The results in Table In this study, MSFS method is used to construct the features of the original mass spectrometry data, so as to realize the alignment operation of the original mass spectrometry data and reduce the redundant information and noise in the data. Chi-square test is used to select the least non-redundant feature combination, and the classification effect is verified on 10 models, and the feasibility of this method is verified on two public data sets. Different batches of sample mixtures were tested and verified internally on the coronary heart disease data set. When there are two features, the classification accuracy of peak area and peak height can reach 1.000, and the classification result is better than MetaboAnalyst software and the current experimental results. At the same time, chi-square test was used to find out two significantly different chi-square characteristics corresponding to two batches of samples in coronary heart disease data set, and visual analysis was made. It is found that the two batches of samples are quite different. External verification is carried out on different batches to verify the classification effect of the method proposed in this paper on samples with significant batch differences. The results show that the classification accuracy is as high as 0.950 when there are four features in the peak area, which verifies the method proposed in this paper. Feasibility. At the same time, the approximate interval of biomarkers of coronary heart disease can be further determined by mapping the two groups of features with the best classification results back to the original M/Z interval. Finally, the method proposed in this paper is verified on the data set of liver metastasis of colorectal cancer. When there are 20 features in the peak area, the classification accuracy can reach 1.000, which shows that the method proposed in this paper is feasible in mass spectrometry data sets (Supplementary Information Supplementary Information.


A special generative manufacturing (AM) process was developed for the partial integration of active ingredients into open-porous matrix structures. A mixture of a silver-containing solution as an antibacterial material with an alginate hydrogel as a carrier system was produced as the active ingredient. The AM process developed was used to introduce the active ingredient solution into an open-porous niobium containing a β-titanium matrix structure, thus creating a reproducible active ingredient delivery system. The matrix structure had already been produced in a separate AM process by means of selective laser melting (SLM). The main advantage of this process is the ability to control porosity with high precision. To determine optimal surface conditions for the integration of active ingredients into the matrix structure, different surface conditions of the titanium substrate were tested for their impact on wetting behaviour of a silver-containing hydrogel solution. The solution-substrate contact angle was measured and evaluated to determine the most favourable surface condition. To develop the generative manufacturing process, an FDM printer underwent modifications that permitted partial application of the drug solution to the structure in accordance with the bioprinting principle. The modified process enabled flexible control and programming of both the position and volume of the applied drug. Furthermore, the process was able to fill up to 95% of the titanium matrix body pore volume used. The customised application of drug carriers onto implants as a drug delivery system can be achieved via the developed process, providing an alternative to established methods like dip coating that lack this capability. Successful orthopedic implant systems are designed to restore the physical function of the affected joint or bone and to promote bone regeneration. Accordingly, they must have properties that mimic natural bone, i.e., adapted mechanical and biocompatibility behaviour.In addition, the implants used should not release unwanted toxins or cause bone cells to be shielded from mechanical stress (stress shielding behaviour). An improved antimicrobial capacity of the implant, for example through the use of drug delivery systems, is intended to combat microbially induced local inflammation and promote the ability to regenerate bone ,2,3.A contemporary implant must fulfil all requirements objectively. The interlocking of modern techniques is necessary to reach such objectives. This paper addresses these challenges by combining two processes. The implant or the matrix structures were produced by using the process of selective laser melting (SLM) ,5. The bSelective laser melting (SLM) aims to meet the requirements of personalised and more precisely fitting implants ,7. PatieIntroducing an open-pore surface structure could enhance the osseointegration of the implant, improving the connection between the implant and bone cells . The spaIn addition to optimising the product design through construction and manufacturing process, the behaviour of the implant can be improved by functionalising the surface. The product surface serves as the most important contact point between the artificial implant and the organ cells. Thus, the product properties, such as chemical, physical but also topological, play a decisive role .Clinical studies have demonstrated that the implant used during human implantation is a primary risk factor for infection development . The plaBased on the Annual Report of the Arthroplasty Registry, 17,752 follow-up procedures were carried out on hips in 2021, with infection cited as the primary reason . To avoiMost methods for destroying bacteria are based on mechanisms that hinder cellular respiration, cell division or cell wall formation . Many phVarious methods exist for introducing active substances into open-porous structures. At present, techniques for coating matrix material to enhance osseointegration and antibacterial effectiveness include dip-coating processes. In such processes, the matrix material is dipped into the coating solution and then removed by dripping. New technologies, such as bioprinting, facilitate the layer-by-layer production of tissue substitutes or synthetic organs through generative manufacturing. Bioprinting enables the recreation of cell distributions of tissues and organs precisely in a three-dimensional space . The bioThis study aimed to assess the feasibility of a process combining these features with regard to their process influences and fluctuations, as well as their inflation and functionalisation behaviour. For this purpose, specific open-porous samples, created via the SLM technique, were infused with a modified bioprinting process that utilised a silver-containing hydrogel mixture to later analyse the effectiveness of the process amalgamation.The matrices utilised for this study were fashioned from titanium-based alloys and manufactured using additive techniques. The powder bed-based selective laser melting (SLM) process allowed for the construction of intricate matrix structures as a result of its considerable design flexibility. Samples were generated in the Concept Laser Mlab while being attended to by a controlled argon inert-gas ambience. The structures were designed to guarantee bone ingrowth while ensuring stability in the load-bearing area. Technical term abbreviations are explained when first used. The hydrogel’s penetration behaviour into the pores was evaluated via the measurement of the contact angle θ with the matrix structure. To achieve this, SLM techniques were employed in the production of a compact material with varied surface properties.a and the arithmetic mean value of the individual roughness depths Rz for the three surfaces based on the guidelines of DIN EN ISO 4287 and ASME B46.1. A measuring distance of 8000 mm and a measuring speed of 0.300 mm/s were chosen for the roughness measurement at room temperature. The selection of the measurement parameters relied on the choice of the measurement section for aperiodic profiles according to the standard DIN EN ISO 4288. The measurement was repeated five times per condition, and the resulting data were used to calculate the arithmetic mean and standard deviation for the average roughness Ra and roughness depth Rz parameters.To assess the impact of distinct surface textures on the performance of the drug delivery system, we previously analyzed the sample surface utilizing a profilometer and optical microscopy. The SURFCOM 1500SD-13 surface measurement system by Zeiss was employed for gauging the surface roughness of the specimens. In this measurement, we determined the arithmetic mean roughness value RThe Keyence digital microscopes VHX-1000 (magnification 50–200×) and VHX-5000 (magnification 100–1000×) were primarily used for surface characterisation and to measure the BCC structure parameters indicated in To investigate the interaction behaviour of a bio-ink on various titanium surfaces, a sodium alginate hydrogel was utilized. In order to manage the infiltration of the hydrogel into the matrix, a cross-linking solution based on calcium chloride was added to the hydrogel solution. These cross-linking solutions were expected to offer optimal control over the quantity of biopolymer introduced. The experimental solutions’ compositions are presented in The sodium alginate solution was obtained from the powdered form, as indicated in To assess the infiltration and wetting behaviour of the silver-containing alginate solution, as well as its stability after cross-linking, contact angle measurements were employed as an evaluation method. Therefore, bulk material refer to  was usedAs part of the wetting behaviour assessment, contact angle measurements were taken on the previously mentioned bulk material of the β-titanium-based alloy containing niobium.To achieve this, a measuring setup was employed where the specimen was positioned in a chamber located in front of a light source with a frosted glass plate. Fluids were dosed through the top chamber opening using syringes. An endoscope (KARL STORZ), which could be adjusted to the height of the relevant sample, was utilized to capture images. The gathered data were subsequently transferred to an associated monitor or data carrier for evaluation. Herein, the images produced were assessed based on the angular ratio between the bulk material and the generated solution droplet to derive the corresponding static contact angles θ without the use of software only by visual assessment. The contact angle was measured three times for each drop and an average value was calculated.In relation to the functionalisation of the titanium structures employed, the density was determined using Archimedes’ principle. The resulting data were subsequently function to determine the structure filling. The method employed to determine density based on Archimedes’ principle is founded on buoyancy. An electrical precision balance, which was adapted as a hydrostatic balance from Sartorius, was employed for this purpose. In order to determine density, the mass of the sample was initially measured in air. The mass in ethanol was measured to determine the density and volume of the samples. These values formed the foundation for ascertaining the density and volume of the samples. The measurement process was done five times to calculate the arithmetic mean for all samples.1 was determined. After functionalisation, the sample was weighed again to determine the total mass m2 after functionalisation. After subtracting the masses, the difference is the mass of the alginate-containing solution mA. Together with the density ρA of the respective alginate solution concentration, the solution volume VA could then be determined. To determine the free volume VPores of a BCC structure, the measured volume of the BCC structure is subtracted from the volume of a cube of the same size (10 mm × 10 mm × 13 mm) made of bulk material. The percentage structure filling is the ratio of the volume VA to VPores multiplied by a factor of 100.A gravimetric approach was chosen to evaluate the percentage of structure filling or the free residual volume in the structure. For this purpose, the structure was weighed before functionalisation and its initial mass mIn order to be able to introduce the selected carrier in combination with an active ingredient into the structures, the FDM printer had to be modified in several steps into a functionalisation system. This subsection deals with the necessary modifications and their implementation.The first step was to construct an enclosure that provided a constant temperature field for processing the alginate-containing solution.The second step in the modification of the printer was the development of a holder for the functionalisation nozzle. A section of the parts drawing for the holder is shown in ® Space) from B. Braun SE was chosen. In addition to setting the flow rate in various units, the pump also offered the option of specifying a total volume to be delivered or a delivery time. Syringes  with a total volume of 20 mL were used as reservoirs for the silver-containing alginate solution to be pumped, specifically designed for use in the syringe pump mentioned above.In addition to the design and manufacture of the holder for the functionalization nozzle, a pump system was required to deliver the drug delivery solution. As the pump had to deliver small volumes, the syringe pump , which was in the centre of the left side of the printing plate, and the speed at which the nozzle moved (G1 F1200.000).After these presettings, the nozzle remained in the approached start position for approximately 106 s to build up the pressure stage. If the pressure buildup time was set to 99 s, the nozzle would remain in this position for a further seven seconds and then move to the new position just before the drop broke. During the pressure buildup, a plastic Petri dish was placed under the nozzle to collect the outflowing solution. During this pause, the BCC structure was placed on the print bed. The position was measured and marked with the g-code. After the pause, the nozzle moved with the command G1 to the position of the blue point . This position was 5 mm further to the right and 31 mm below the start position described before. The nozzle remained at this position for 13 s.This procedure was repeated in the same way for the remaining groups of unit cells  in two cycles. The hydrogel seeped through the surface by gravity. By functionalising with nine drops, a structure filling of more than 90% could be achieved. It can therefore be assumed that almost all of the open-pore volume in the structure was filled with the drug delivery solution. It could also be shown that additional cross-linking with the calcium chloride solution after functionalisation reduced the structure filling by about 70% in a preliminary test. One explanation for the reduced structure filling due to cross-linking could be that the mass withdrew into the interior of the structure during cross-linking and was therefore no longer visible at the edges. Another possible explanation could be that the immersion of the structures in the cross-linking solution washed out some of the solution containing the alginate. However, this does not necessarily mean that the cross-linking solution cannot be applied. It is possible to create partial or gradual coatings. This could be done by using several alginate-based hydrogels; for example, with different active ingredients or active ingredient concentrations. For example, an implant could be functionalised at a specific site, this site could then be cross-linked and the cross-linked area could be expanded with another drug component. Other active ingredients with antibacterial activity, such as copper, could also be considered, particularly as the efficacy of silver has not yet been evaluated ,34. In aIn summary, the objective of developing a process based on generative manufacturing for the targeted and controllable functionalisation of medical devices was achieved.An FDM printer was converted into a functionalisation system in four steps.The BCC structures as well as the wetting and flow behaviour of the sodium alginate used as hydrogel for the drug delivery solution was characterised.It is clear that the alginate-containing solution led to the best possible wetting on the as-built and polished surfaces.However, this was only true for the first two states, as the solidification of the compound made it impossible to evaluate the contact angle in the third state.It was also shown that the 2 wt.% sodium alginate solution had a favourable flow behaviour for processing with the syringe pump.It was shown that with nine drops for the two cycles, the filling of the structure increased to more than 90%. These tests showed that, in addition to the process setup, the variations of the SLM process need to be taken into account. For future test series, the aim is to optimise the process in terms of cleaning the structures in order to achieve more consistent results in terms of free pore volume prior to functionalisation.For this purpose:


This paper proposes to remotely estimate a human subject’s blood pressure using a millimeter-wave radar system. High blood pressure is a critical health threat that can lead to diseases including heart attacks, strokes, kidney disease, and vision loss. The commonest method of measuring blood pressure is based on a cuff that is contact-based, non-continuous, and cumbersome to wear. Continuous remote monitoring of blood pressure can facilitate early detection and treatment of heart disease. This paper investigates the possibility of using millimeter-wave frequency-modulated continuous-wave radar to measure the heart blood pressure by means of pulse wave velocity (PWV). PWV is known to be highly correlated with blood pressure, which can be measured by pulse transit time. We measured PWV using a two-millimeter wave radar focused on the subject’s chest and wrist. The measured time delay provided the PWV given the length from the chest to the wrist. In addition, we analyzed the measured radar signal from the wrist because the shape of the pulse wave purveyed information on blood pressure. We investigated the area under the curve (AUC) as a feature and found that AUC is strongly correlated with blood pressure. In the experiment, five human subjects were measured 50 times each after performing different activities intended to influence blood pressure. We used artificial neural networks to estimate systolic blood pressure (SBP) and diastolic blood pressure (SBP) with both PWV and AUC as inputs. The resulting root mean square errors of estimated blood pressure were 3.33 mmHg for SBP and 3.14 mmHg for DBP. Blood pressure is a parameter that provides fundamental yet critical information pertaining to human health. High blood pressure, also termed hypertension, is among the riskiest symptoms of heart disease. Hypertension is the leading cause of mortality around the world; moreover, approximately 30% of adults have the disease ,2. HyperBlood pressure is a function of cardiac output, systemic vascular resistance, and arterial stiffness. It also varies depending on the individual’s psychological situation, emotional state, physical activity, and relative health/disease status. In general, blood pressure consists of systolic blood pressure (SBP) and diastolic blood pressure (DBP); in addition, the difference between SBP and DBP is called pulse pressure (PP). These pressures are measured in terms of millimeters of mercury (mmHg).Historically, the cuff-based sphygmomanometer has been the most widely used instrument employed to measure blood pressure. A sphygmomanometer has many variations that can be used to perform measurements at different body locations, including the arm, wrist, and finger. In general, blood pressure is measured in the upper right arm by the inflation and deflation of a cuff, sometimes periodically repeated, which controls the flow of blood in the vessels. While this method represents the most direct way to measure blood pressure, it has several disadvantages. Examples include the time needed to take the reading through the process of inflation and deflation, the discrete and not continuous nature of the measurement, and the discomfort and inconvenience of wearing the cuff. Many alternative methods for blood pressure measurements have been suggested. Researchers have proposed several blood pressure measurement methods based on pulse wave velocity (PWV). The relationship between blood pressure and PWV has been studied extensively, ultimately revealing that PWV is proportional to blood pressure . TherefoThe use of only PPG has been advanced to measure blood pressure continuously and conveniently using wearable devices. PPG sensors have been installed in devices such as smartwatches and wrist bands that can provide blood flow information continuously at specific sites, providing a less intrusive measurement method. The device obtains the reflection of infrared waves from the movement of the blood vessel and analyzes the waveform to estimate blood pressure . ScholarRecent progress has featured the use of optical imaging of facial patterns to calculate PPG information directly from facial variations depending on heartbeats . HoweverIn , the papIn this paper, we investigate the feasibility of using frequency-modulated continuous-wave (FMCW) radars to achieve a contactless estimation of a human subject’s blood pressure without the detailed shape of the pulse wave. The FMCW radar has been used to detect sub-millimeter level vibrations of a body part remotely. Although Doppler radar has also been previously studied for the detection and remote sensing of various vital physiological variables such as respiration, heart rate, blood vessel motion, and pulse pressure ,17,18, aTo date, the sphygmomanometer has been the gold standard for blood pressure measurement. When the cuff is fully inflated, the cuff’s high pressure completely blocks the artery from allowing any flow of blood. As the cuff pressure slowly deflates, the cuff pressure eases enough to let the artery start to resume the blood flow, which is SBP. Meanwhile, DBP is obtained when blood flow is normal, without any external pressure from the cuff, which causes no vibrations in the artery. Scholars have recognized PP as a more accurate diagnostic method than blood pressure by itself, especially in older adults. High PP is correlated with cardiovascular risk factors such as diabetes and smoking. Measurement of PP along with heart rate also enables the calculation of stroke volume and cardiac output. The PP, which equals the volume over compliance value, is directly proportional to the stroke volume and therefore to the displacement of the heart wall. Compliance is determined by the physical properties of the artery at a specific location. The aorta has the highest compliance in the arterial system due, in part, to a relatively greater proportion of elastin fibers versus smooth muscle and collagen. This characteristic serves the crucial function of damping the pulsatile output of the left ventricle, thereby reducing the initial systolic pulse pressure but slightly raising the subsequent diastolic phase. If the aorta becomes rigid because of disorders such as arteriosclerosis or atherosclerosis, the pulse pressure will be very high as the aorta becomes less compliant due to the formation of rigid lesions in the aorta wall.Pulse arrival time (PAT) has also been recognized as having a strong correlation with blood pressure. The definition of PAT specifies the time elapsed between an ECG signal from the chest and the peak from the PPG signal measured at a second site. In other words, this time delay is the sum of the pulse ejection period (PEP) and PTT. The PEP is obtained from the ECG and SCG. PAT is inversely proportional to blood pressure, thus providing a method to measure blood pressure. Alternatively, PTT-based blood pressure measurements that have also been explored have set the PEP, which is the period from ventricular depolarization to ventricular ejection, as a constant value ,20. HoweL denotes the vessel length, ρ represents the blood density, oR is the inner radius of the vessel, oh signifies the vessel wall thickness, oE is the zero-pressure modulus of the vessel wall, and P refers to the blood pressure within the vessel. Based on these two equations, blood pressure can be computed from the PTT, assuming all other parameters are held constant. In [Numerous equations have been proposed to describe the correlation between blood pressure and various parameters, such as PAT, PTT, PEP, and PWV. The most popular equations describing the relationship between PWV and blood pressure are the Moens–Korteweg (M.K.) equation and Hughes equation ,23. The tant. In , a relata is related to elastic modulus at zero pressure, and b is an overall offset to the entire curve. Meanwhile, a PTT-based blood pressure calculation was made in [The constant  made in , as follThis equation features the inverse relationship between PTT and blood pressure. The two constant values are subject-dependent, which are related to the elastic modulus, distance to the wrist, blood density, and other factors. It is found that Equation (4) has a strong correlation with DBP .PTT has previously been measured using contact-based devices. In contrast, this project focused on obtaining the PTT parameter with no contact using millimeter-wave FMCW radar. The time delay of the pulse on two parts of the body  can provide the PTT, which can be simply described as the time delay of the pulse measured at two locations. Blood flow causes the chest vibration and wrist artery pulse transit to make a sudden outward push of the skin. This skin displacement yields the change of phase of the received radar signal. Our study employed measurement at a subject’s chest and wrist to determine the time delay of the pulse. Even though this study focused on PTT between the heart and wrist because the skin vibration could be easily detected due to the strong pulse, other locations, including the foot, ankle, knee, and elbow, would also be usable. The PTT values were used to correlate with the DBP based on the relation between the two parameters revealed in Equation (4). Two AWR1243 FMCW radars developed by Texas Instruments were employed to obtain the degree of skin displacement at the chest and wrist through phase variation. The FMCW radar used in the current study operated at 77 GHz and provided a maximum of 4 GHz of bandwidth. The vital sign measurements were taken at a distance of 0.5 m. We conducted the fast Fourier transform to the time domain data (fast time) with each frame to construct a range–time map in the form of a as shown in The phase information obtained from the range profile was subject to DC noise. As a high DC component could have negatively influenced the phase accuracy required to obtain a small displacement, this noise was removed by compensating for the error. Among various methods ,13, the In the measurement campaigns, as shown in As shown in the previous section, the proposed radar system was able to measure the PTT. To determine the relationship between the PTT and blood pressure, it is required to take the blood pressure measurement using a cuff for multiple participants. We used Omron’s Sphygmomanometer (HEM-FL31), and the cuff readings were performed on the left upper arm. The measurements were taken while the participating individual sat on a chair. The five individuals who participated in the investigation ranged in age from 20 to 76 years and included both genders. The characteristics of the five individuals can be found in After the measurements, we investigated the correlation between PTT and SBP/DBP/PP for each individual. Even though the PTT was related to blood pressure, PWV was more directly related as people’s arm length varied. The PTT divided by arm length provided the PWV. The values that we calculated for PWV are presented in In the next step, we evaluated the error for each case. Polynomial curve fitting was obtained by using MATLAB to determine the best polynomial regression model. The root mean square error (RMSE) was used to evaluate this case as it quantified the error between the model and measured data. The third-order polynomial was found to provide the best RMSE results for the preliminary data. Notably, Individual 3, with a pre-existing condition, had the most uniquely different dataset, especially the DBP values, which provided the largest RMSE. This outcome demonstrates that the measurements can be tailored for individuals with specific backgrounds. Individual 2, who also had a case of hypertension, yielded average pressures that were much higher than other individuals’ normal readings. Nevertheless, this individual’s dataset was able to fit much better compared to that of Individual 3. Without Individual 3′s data, the average RMSE for DBP is 3.23 mmHg.This section describes our investigation of the feasibility of estimating blood pressure with a single radar. The previous approach using PWV required two radar devices and arm length information, which added complexity as well as increased the possibility of error. In this step, we focused on finding the correlation between blood pressure and the characteristic of the displacement graph shown in AUC is intended to measure the sharpness of the peak. First, the amplitude of each peak of the displacement graph was normalized to the unit level because the peaks of the phase graph varied. After normalizing amplitude to one, we defined AUC as the area bounded by the graph itself and a line of 50% of the peak , as shown in the green box of To evaluate the AUC, we introduced a regression model that found the best fit to the data to determine the RMSE as presented in We investigate the feasibility of estimating blood pressure using both PWV and AUC for data samples from multiple subjects. All data points except those for subject 3 were used in prediction analysis with machine learning. Subject 3′s data were an outlier case due to medication. First, we constructed third-order polynomial regression models. The next step entailed plotting the true values and the predicted value from the regression model. RMSEs were found for the four remaining human subjects’ data, which were analogous to the training error in the context of machine learning. On the basis of the calculations, for single feature correlation, SBP and DBP were both better correlated with AUC than PWV. The RMSE of SBP was 3.89 mmHg while that of DBP was 3.83 mmHg. In addition, we applied a multivariate polynomial regression model using two inputs of PWV and AUT, which yielded values of 3.51 mmHg for SBP and 3.35 mmHg for DBP, as shown in Next, we used artificial neural networks (ANNs), a popular machine learning technique, to predict blood pressure given PWV and AUC. ANNs are made of several layers; each neuron has a weight and activation function that calculates outputs for the following layer. In this study, the input layer took the features calculated and passed them down to the hidden layers. The fully connected layers were used for the hidden layers. The hyperparameter of the ANN was heuristically determined. The model consisted of two fully connected layers of which the sizes are 20 and 10, respectively. The architecture was kept small due to the small amount of available data. It is noted that a deeper neural network was applied, but it caused an overfitting problem that resulted in a high test error. For training the net, we used the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton algorithm as a loss function minimizer. A fivefold cross-validation was also performed to ensure that the model did not overfit due to the small amount of data. Cross-validation allowed the model to be trained and validated based on different data for each iteration. Then, the model that provided the lowest loss was used on a test set of 25 data points. The training and validation data consisted of 175 data points. The results reveal that AUC was the most important feature for estimating SBP, which was in line with the conclusion for individual cases. The use of a combination of features decreased the estimation error. For DBP, the PWV was a key feature; here, the use of combination features was also able to decrease the estimation error. Interestingly, the use of ANN performed better compared to the regression model.This study proposed a method of contactless, continuous blood pressure estimation using millimeter-wave FMCW radar and reported preliminary results. The PWV and AUC data obtained from measurements from multiple human subjects were found to be strongly correlated with SBP and DBP. The lower RMSE value of SBP was from AUC while the combination of features even decreased the error. Regarding DBP, PWV was more correlated compared to AUC while the combination of features showed better results. However, Individual 3 who takes medicines for hypertension and diabetes, as an outlier, greatly increased the RMSE value for DBP.This method can provide continuous monitoring of patients with zero contact, which is potentially useful in the medical field or in daily life, ensuring the subject’s safety and comfort while making the measurement unobtrusive or even invisible. For example, the proposed method using radar could be installed at home for individuals who might require continuous remote monitoring to detect any signs of a blood pressure-related disease. Therefore, the method can be used to be as an auxiliary method to the traditional methods, offers ease of use, and will become financially affordable as the price for radar decreases over time. Future studies can focus on the further development of remote monitoring systems for patients that can alert medical officials when blood pressure readings diverge appreciably from normal ranges. This remote contactless blood pressure measurement might increase the frequency of blood pressure readings, which can construct big data regarding blood pressure.While these preliminary findings are promising, many obstacles remain to obtain more reliable results. Motion noise significantly affects the shape of the signal, so developments in signal processing techniques should be made to resolve the motion noise issue. While signals from ECG and PPG have many more details that can be exploited for blood pressure estimation, features from the radar are rather simple. Further research on feature extraction should be conducted. In addition, the pulse from the wrist is relatively weak. To capture the pulse from the wrist effectively, the use of terahertz radar needs to be considered.