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From a medical perspective, the 12 leads of the heart in an electrocardiogram (ECG) signal have functional dependencies with each other. Therefore, all these leads report different aspects of an arrhythmia. Their differences lie in the level of highlighting and displaying information about that arrhythmia. For example, although all leads show traces of atrial excitation, this function is more evident in lead II than in any other lead. In this article, a new model was proposed using ECG functional and structural dependencies between heart leads. In the prescreening stage, the ECG signals are segmented from the QRS point so that further analyzes can be performed on these segments in a more detailed manner. The mutual information indices were used to assess the relationship between leads. In order to calculate mutual information, the correlation between the 12 ECG leads has been calculated. The output of this step is a matrix containing all mutual information. Furthermore, to calculate the structural information of ECG signals, a capsule neural network was implemented to aid physicians in the automatic classification of cardiac arrhythmias. The architecture of this capsule neural network has been modified to perform the classification task. In the experimental results section, the proposed model was used to classify arrhythmias in ECG signals from the Chapman dataset. Numerical evaluations showed that this model has a precision of 97.02%, recall of 96.13%, F1-score of 96.57% and accuracy of 97.38%, indicating acceptable performance compared to other state-of-the-art methods. The proposed method shows an average accuracy of 2% superiority over similar works. [ABSTRACT FROM AUTHOR]

The automated examination of electrocardiogram (ECG) signals holds significant importance within the medical field for managing various critical cardiac conditions. Identifying cardiomyopathy and arrhythmias is presently recognized as a challenging endeavor. While machine learning techniques have garnered substantial attention for categorizing these patterns, a predominant focus has been on the classification of arrhythmias. However, existing studies have overlooked instances where arrhythmia leads to cardiomyopathy, a specific cardiac disease scenario. In our research, we introduce an innovative method aimed at distinguishing between cardiomyopathy and cardiomyopathy accompanied by arrhythmia by employing a convolutional neural network (CNN-based) model. This novel approach fills the gap in existing literature by addressing the critical need to classify cases where arrhythmia induces cardiomyopathy, thereby presenting a potential advancement in accurately identifying and managing complex cardiac conditions. The proposed model uses convolution-based CNN model for feature extraction and combines these features with temporal features. Further, a CNN combined long short-term memory (CNN-LSTM) model is presented for classification where CNN models help to obtain the spatial information and LSTM helps to retain the temporal information resulting in improved classification accuracy. the experimental analysis is carried out into two phases where we have classified the rhythms and arrhythmias. [ABSTRACT FROM AUTHOR]

One of the main sources of the Sudden Cardiac Death (SCD) is termed as Fatal arrhythmia. The electric shock treatment retrieves the regular electrical and mechanical functions of the heartbeat by controlling Ventricular fibrillation (VF) and Ventricular Tachycardia (VT). Shockable arrhythmia is easily controlled by providing electrical shock treatments. On the other hand, non-shockable arrhythmia is not controlled by electrical shock treatment. It is a very complex task to accurately discriminate these two kinds of arrhythmia using human assessment of Electrocardiogram (ECG) signals within a limited span and there may be a chance to occur faults during manual inspection. An accurate ECG diagnosis is very significant as it saves the life of the patient in advance by delivering proper therapy. To address this emerging problem, an automated model using the proposed Dwarf Mongoose Gannet Optimization Algorithm-Deep Neuro-Fuzzy Network (DMGOA-DNFN) is invented for detecting the shockable ventricular cardiac arrhythmias (SVCA). The classification is performed effectively utilizing DNFN and the weight of this classifier is optimally adjusted employing a newly developed algorithm named DMGOA, which is a consolidation of Dwarf Mongoose Optimization (DMO) and Gannet Optimization Algorithm (GOA). The proposed DMGOA-DNFN has surpassed other classical models with respect to accuracy of 93.2%, sensitivity of 95.8%, and specificity of 91.7%. [ABSTRACT FROM AUTHOR]

The majority of current machine learning and deep learning solutions for ECG-based emotion recognition utilize fully-supervised learning methods. Several limitations of this approach are that large human-annotated datasets and computing resources are required. Furthermore, the feature representations learned by fully supervised methods tend to be task-specific with limited generalization capability. In response to these issues, this paper proposed an approach based on contrastive lear-ning for ECG-based emotion recognition, which consisted of two steps, such as pre-training and fine-tuning. The goal of pre-training was to learn representations from unlabeled EGG data through contrastive learning. Specifically, it designed two simple and efficient ECG signal augmentation methods, and used these two views to learn robust temporal representations in the time contrastive module, followed by learning discriminative feature representations in the context contrastive module. Fine-tuning used labelled data to learn emotion recognition. Experiments show that the proposed method has reached the maximum accuracy on three public ECG-based emotion recognition datasets. Additionally, the proposed method shows high efficiency under the semi-supervised settings. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signal is one of the noninvasive physiological measurement techniques commonly used in cardiac diagnosis. However, in real scenarios, the ECG signal is susceptible to various noise erosion, which affects the subsequent pathological analysis. Therefore, the effective removal of the noise from ECG signals has become a top priority in cardiac diagnostic research. Aiming at the problem of incomplete signal shape retention and low signal-to-noise ratio (SNR) after denoising, a novel ECG denoising network, named attention-based residual dense shrinkage network (ARDSN), is proposed in this paper. Firstly, the shallow ECG characteristics are extracted by a shallow feature extraction network (SFEN). Then, the residual dense shrinkage attention block (RDSAB) is used for adaptive noise suppression. Finally, feature fusion representation (FFR) is performed on the hierarchical features extracted by a series of RDSABs to reconstruct the de-noised ECG signal. Experiments on the MIT-BIH arrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resist the interference of different sources of noise on the ECG signal. [ABSTRACT FROM AUTHOR]

BackgroundElectrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis.MethodsIn this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity.ResultsThe proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD).ConclusionsThe proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.

Diagnosing cardiac conditions require careful examination of an electrocardiogram (ECG). However, a significant issue arises when capturing an ECG due to interference from various noises. Noises like power line interference (PLI) and muscle artifact (MA) change the morphology, making it difficult to interpret the original signal. Our research proposes an improved variational mode extraction (IVME) technique using a Heap-based optimization (HBO) algorithm and an automatic wavelet interval-dependent thresholding (AWIT) method to eliminate such noises. First, HBO uses the envelope entropy spectrum (EES) as the objective function to find the best fitness value for optimizing the VME parameter, known as penalty factor $\alpha $ . Then, we extract a specific mode using the optimal $\alpha $ value in VME to accurately remove PLI from the signal. Finally, the AWIT method automatically computes the intervals and their respective threshold values to remove excessive MA noise from the PLI-filtered ECG signal. We evaluate the efficiency of ten random real-time ECG signals from the MIT-BIH arrhythmia database. The result analysis proves that our algorithm can accurately extract the mode containing PLI and eradicate MA from the noisy ECG signal. It also shows improvement in signal parameters like signal-to-noise ratio (SNR $_{\mathrm {improvement}}$ ), mean square error (MSE), and correlation coefficients (CC) with 36.7968 dB, 0.00030901, and 99.7278%, respectively.

In order to strengthen the gradual features of ECG signals and improve the classification accuracy of time series signals, the study proposes an adaptive algorithm IGF+Bi-LSTM based on Bi-LSTM which integrates the features of gradual data. The algorithm can adaptively select the features of gradual data with the highest degree of similarity within a certain range. Through data fusion, the algorithm enhances the interaction of gradual features in the network hidden space and expands the Bi-LSTM information transmission mode. Moreover, the study proposes an improved Bdistance based on difference to solve the problem of periodic mismatch and strength inconsistency between time series signals, it can not only characterize the difference degree S between the target data and different label data, but also adaptively adjust the fusion coefficient in IGF+Bi-LSTM. The experiment shows that the classification accuracy of IGF+Bi-LSTM algorithm on ECG dataset is 98.7% and the F1 value is 98.7%, which proves the effectiveness and practicability. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signal, which is composite of multiple segments such as P-wave, QRS complex and T-wave, plays a crucial role in the treatment of cardiovascular disease. For an analysis of cardiac diagnosis, it is required that clinicians scan the ECG signal for QRS complex or R-peaks (the highest peak of the QRS complex) detection, which relies on their expertise and takes enormous time. In order to provide more realistic treatment of cardiovascular diseases, so many computer-based techniques detecting R-peaks/QRS complex in the ECG signal with noises and different characteristics have been actively developed in research article for many years. Moreover, researchers have created various data sets for R-peaks/QRS complex detection. Although R-peaks/QRS complex detection is one of the notable research areas with so many computer-based techniques and ECG data sets, no comprehensive review paper have been published recently. The main aim of this study is to present a wide range of computer-based techniques proposed for detection of R-peaks/QRS complex. First of all, in this study, computer-based techniques proposed in the literature for the detection of R-peaks/QRS complex and their stages are introduced in detail. The generalization ability of these techniques is investigated deeply. Details are given about the most preferred ECG data sets produced in the literature for the analysis of computer-based techniques. Finally, the performances of computer-based techniques and generalization abilities are analyzed for each data set created in the literature by giving the results of the evaluation metrics. [ABSTRACT FROM AUTHOR]

Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart's electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal into its intrinsic mode functions (IMFs) and a convolutional neural network (CNN) for automatic diagnosis is proposed. Results for the ensemble empirical mode decomposition (EEMD) method and the empirical mode decomposition (EMD) method are also compared. Results demonstrate that the combination of the CEEMD and the CNN is a potential solution for SCD prediction since 97.5% of accuracy is achieved up to 30 min in advance of the SCD event. [ABSTRACT FROM AUTHOR]

Introduction: Tight glucose monitoring is crucial for diabetic patients by using a Continuous Glucose Monitor (CGM). The existing CGMs measure the Blood Glucose Concentration (BGC) from the interstitial fluid. These technologies are quite expensive, and most of them are invasive. Previous studies have demonstrated that hypoglycemia and hyperglycemia episodes affect the electrophysiology of the heart. However, they did not determine a cohort relationship between BGC and ECG parameters. Material and method: In this work, we propose a new method for determining the BGC using surface ECG signals. Recurrent Convolutional Neural Networks (RCNN) were applied to segment the ECG signals. Then, the extracted features were employed to determine the BGC using two mathematical equations. This method has been tested on 04 patients over multiple days from the D1namo dataset, using surface ECG signals instead of intracardiac signal. Results: We were able to segment the ECG signals with an accuracy of 94% using the RCNN algorithm. According to the results, the proposed method was able to estimate the BGC with a Mean Absolute Error (MAE) of 0.0539, and a Mean Squared Error (MSE) of 0.1604. In addition, the linear relationship between BGC and ECG features has been confirmed in this paper. Conclusion: In this paper, we propose the potential use of ECG features to determine the BGC. Additionally, we confirmed the linear relationship between BGC and ECG features. That fact will open new perspectives for further research, namely physiological models. Furthermore, the findings point to the possible application of ECG wearable devices for non-invasive continuous blood glucose monitoring via machine learning. [ABSTRACT FROM AUTHOR]

Successful self-management of diabetes requires Continuous Glucose Monitors (CGMs). These CGMs have several limitations such as being invasive, expensive and limited in terms of use. Many techniques, in vain, have been proposed to overcome these limitations. Nowadays, with the help of the Internet of Medical Things (IoMT) technologies, researchers are working to find alternative solutions. They succeed to predict hypoglycemia and hyperglycemia peaks using Electrocardiogram (ECG) signals. However, they failed to use it to estimate the Blood Glucose Concentration (BGC) directly and in real time. Three patients with 08 days of measurements from the D1namo dataset contributed to the study. A new technique has been proposed to estimate the BGC curves based on ECG signals. We used a convolutional neural network to segment the different regions of ECG signals as well as we extracted ECG features that were required for the next step. Then, five regression models have been employed to estimate BGC using as input sixth ECG parameters. We were able to segment the ECG signals with an accuracy of 94% using the convolutional neural network algorithm. The best performance among all simulated models was provided by Exponential Gaussian Process Regression (GPR) with Root Mean Squared Error (RMSE) values of 0.32, 0.41, 0.67 and R-squared (R2) values of 98%, 80%, and 70% for patients 01, 02 and 03 respectively. The method indicates the potential use of ECG wearable devices as non-invasive for continuous blood glucose monitoring, which is affordable and durable. [ABSTRACT FROM AUTHOR]

Abstract Purpose Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation. Methods Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared. Results IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man–machine devices and improving the safety of Pilates rehabilitation.

Electrocardiogram (ECG) is a diagnostic method that helps to assess and record the electrical impulses of heart. The traditional methods in the extraction of ECG features is inneffective for avoiding the computational abstractions in the ECG signal. The cardiologist and medical specialist find numerous difficulties in the process of traditional approaches. The specified restrictions are eliminated in the proposed classifier. The fundamental aim of this work is to find the R-R interval. To analyze the blockage, different approaches are implemented, which make the computation as facile with high accuracy. The information are recovered from the MIT-BIH dataset. The retrieved data contain normal and pathological ECG signals. To obtain a noiseless signal, Gabor filter is employed and to compute the amplitude of the signal, DCT-DOST (Discrete cosine based Discrete orthogonal stock well transform) is implemented. The amplitude is computed to detect the cardiac abnormality. The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified. The Genetic algorithm (GA) retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification. In addition, the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement. Finally, the RBFNN (Radial basis function neural network) is applied, which diminishes the local minima present in the signal. It shows enhancement in characterizing the ordinary and anomalous ECG signals. [ABSTRACT FROM AUTHOR]

In this paper, we develop a low-cost cellular internet of medical things (IoMT)-based electrocardiogram (ECG) recorder for monitoring heart conditions and used in practical cases. In order to remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that focuses on utilizing deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for noise cancellation. Due to the practical use case, the various heart physiologies and noise levels in different amplitudes and frequencies are needed to be robust against wide-range noises in actual conditions. We utilize the MIT-BIH Apnea-ECG database (APNEA-ECG) with several different heart physiologies. Next, the different noises consisting of muscle artifacts (MA), baseline wander (BW), and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) and random noise, are added to the signals. The main focus of the noise generation part is the fast Fourier transformation (FFT) of the simulated noisy signal and the practical noisy signal has a maximum cross-correlation to gain a better morphological resemblance between realistic signals and the prepared datasets. Based on the results, DeepRTSNet outperforms prior learning-based methods and conventional non-learning approaches in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD). Moreover, outcomes reveal that DeepRTSNet has an extraordinary performance with a certain amount of further complexity than others.

Electrocardiogram(ECG) monitoring is a routine monitoring method in clinical practice. By observing ECG activities and ECG waveform changes, myocardial damage, myocardial ischemia and electrolyte disorder could be detected. It is clinically significant to detect QRS complex in ECG. This paper proposes an algorithm for QRS complex detection using an adaptive threshold method based on window maximum or minimum value method, which includes detection of R-wave morphology in ECG, and verification of R-wave location accuracy rate (97. 63% ) by MIT-BIH arrhythmia database. On the basis of R-wave detection, the maximum or minimum value method and slope limit method are used to search for Q- and S-wave. After QRS complex positioning is completed, P- and T- wave are detected, and located according to their morphological characteristics. [ABSTRACT FROM AUTHOR]

In this document, the aim of this study is to identify the subjects using PQRST fragments of the electrocardiogram (ECG) signal. The ECG signal is an emerging technology for person identification. Our identification system has three principal steps, namely preprocessing, features extraction, and classification. In the first step, the filtering technique is used to remove the noise of the ECG signal. After filtering, the algorithm of T peaks detection is implemented for realizing the segmentation. (This work focuses on the PQRST fragments.) In the second step, a combination of the features such as cepstral coefficients, entropy, and zero crossing rate is proposed in this work. After feature extraction step, the machine learning model like the support vector machines is used for the classification step. A combination of the different features is evaluated using two public databases such as ECG-ID database and Massachusetts Institute of Technology–Boston's Beth Israel Hospital Arrhythmia DataBase obtained from the Physionet database. Our proposed system gives an accuracy rate of 92.5% with ECG-ID database (all-recordings) and 98.6% with MIT–BIHA database. [ABSTRACT FROM AUTHOR]

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation's identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms. Dataset: https://figshare.com/articles/dataset/Heartprint%5fA%5fMultisession%5fECG%5fDataset%5ffor%5fBiometric%5fRecognition/20105354/3. Dataset License: CC BY 4.0 [ABSTRACT FROM AUTHOR]

A job interview can be challenging and stressful even when one has gone through it many times. Failure to handle the stress may lead to unsuccessful delivery of their best throughout the interview session. Therefore, an alternative method which is preparing a video resume and interview before the actual interview could reduce the level of stress. An intelligent stress detection is proposed to classify individuals with different stress levels by understanding the physiological signal through electrocardiogram (ECG) signals. The Augsburg biosignal toolbox (AUBT) dataset was used to obtain the state-of-art results. Only five selected features are significant to the stress level were fed into neural network multi-layer perceptron (MLP) as the optimum classifier. This stress detection achieved an accuracy of 92.93% when tested over the video interview dataset of 10 male subjects who were recording the video resume for the analysis purposes. [ABSTRACT FROM AUTHOR]

Objective. Overcomplete dictionaries are widely used in compressed sensing (CS) to improve the quality of signal reconstruction. However, dictionary learning under the â„" 0 -norm or â„" 1 -norm constraint inevitably produces dictionary atoms that are negatively correlated with the original signal; meanwhile, when we use a sparse linear combination of dictionary atoms to represent a signal, it is suboptimal for the dictionary atoms to “cancel each other out” by addition and subtraction to approximate the sample. In this paper, we propose a non-negative constrained dictionary learning (NCDL) algorithm to improve the reconstruction performance of CS with electrocardiogram (ECG) signals. Approach. Non-NCDL was divided into an encoding stage and a dictionary learning stage. In the encoding stage, non-negative constraints were imposed on the encoding coefficients and obtained the sparse solution using the alternating direction method of multipliers. At the same time, a penalty term was integrated into the objective function in order to remove small coding coefficients and achieve the effect of sparse coding. In the dictionary learning stage, the block coordinate descent algorithm was utilized to update the dictionary with a view to obtaining an overcomplete dictionary. Results. The performance of the proposed NCDL algorithm was evaluated using the standard MIT-BIH database. Quantitative performance metrics, such as percent root mean square difference (PRD1) and root mean square error, were compared with existing CS approaches to quantify the efficacy of the proposed scheme. For a PRD1 value of 9%, the compression ratio (CR) of the NCDL approach was around 2.78. When CR ranged from 1.05 to 2.78, the proposed NCDL approach outperformed the method of optimal direction, k-means singular value decomposition, and online dictionary learning approaches in ECG signal reconstruction based on CS. Significance. This promising preliminary result demonstrates the capability and feasibility of the proposed bioimpedance method and may open up a new direction for this application. The non-NCDL method proposed in this paper can be used to obtain a sparse basis and improve the performance of CS reconstruction. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called "discriminative sparse-code error" into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition. [ABSTRACT FROM AUTHOR]

Recent advances in telemedicine and personalized healthcare have motivated new developments in wearable technologies targeting continuous monitoring of biosignals. Common limitations of wearables for continuous monitoring include durability and breathability of their biopotential electrodes. This paper tackles this challenge by proposing flexible, breathable, and washable dry textile electrodes made of conductive elastomeric filaments (CEFs). First, candidate CEF fibers are characterized. Using an industrial knitting machine, CEF fibers are then directly knitted into textile electrodes. To assess their performance in more realistic circumstances, smart garments with textile electrodes are knitted. Electrocardiograms (ECGs) are acquired using an underwear garment and electrooculograms (EOGs) are acquired using a headband. ECGs and EOGs with textile electrodes are found to have comparable fidelity to that of the gold standard gel electrodes. CEF electrodes are also resistant to repeated wash and dry cycles (30×) and continue to acquire high‐fidelity biosignals. Smart underwear garments are also used to perform continuous ECG measurements in five participants over 24 h of unrestricted daily activities. Results demonstrate the success of these garments in performing high fidelity continuous ECG monitoring. Collectively, these results present CEF electrodes as a promising scalable solution to the challenges of wearable technologies for long‐term continuous electrophysiological monitoring applications. [ABSTRACT FROM AUTHOR]

Abstract Significant advances in deep learning techniques have made it possible to offer technologically advanced methods to detect cardiac abnormalities. In this study, we have proposed a new deep learning based Restricted Boltzmann machine (RBM) model for the classification of arrhythmias from Electrocardiogram (ECG) signal. The work is divided into three phases where, in the first phase, signal processing is performed, including the normalization of the heartbeats as well as the segmentation of the heartbeats. In the second phase, the stacked RBM model is implemented which extracts the essential features from the ECG signal. Finally, a SoftMax activation function is used that classifies the ECG signal into four types of heartbeat classes according to ANSI/AAMA standards. This stacked RBM model is offered as three types of experiments, patient independent data classification for multi-class, patient independent data for binary classification, and patient specific classification. The best result was obtained using patient independent binary classification with an overall accuracy of 99.61%. For Patient Independent Multi Class classification, accuracy obtained was 98.61% and for patient specific data, the accuracy was 95.13%. The experimental results shows that the developed RBM model has better performance in terms of accuracy, sensitivity and specificity as compared to work mentioned in the other research papers. Article highlights The proposed RBM model is skilled to automatically classify ECG heartbeat according to the ANSI- AAMI standards with accuracy, Recall, specificity. The performance of the RBM model to correctly classify heartbeat classes was found to be improved. The model is fully automatic, hence there is no requirement of additional system like feature extraction, feature selection, and classification.

Electrocardiogram (ECG) signal can be thought of as an effective indicator for detection of various arrhythmias. However, the acquired ECG data is always corrupted by amounts of noise, which have a great influence on the diagnosis of cardiovascular diseases. In this paper, an efficient deep convolutional encoder-decoder network framework is proposed to remove the noise from ECG signal, which is termed as ‘DeepCEDNet’. This network is able to learn a sparse representation of data in the time-frequency domain via the high-order synchrosqueezing transform (FSSTH) and a nonlinear function that maps the noisy data into the clean one based on the distribution difference between signal and noise from the training set. Extensive experiments are conducted on ECG signals from the MIT-BIH Arrhythmia database and MIT-BIH Long-Term ECG database, and the added noise is obtained from the MIT-BIH Noise Stress Test database. The denoising performance is evaluated by means of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). The results indicate that the proposed DeepCEDNet can obtain superior performance in both noise reduction and details preservation with higher SNR and lower RMSE and PRD compared to the traditional convolutional neural network (CNN) and the fully convolutional network-based denoising auto-encoder (FCN). We believe that the DeepCEDNet has a wide application prospect in the biomedical field.

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. This paper aims to propose a novel robust ECG biometric method, named the Label Consistent Non-negative Representation (LCNR), for ECG classification. We propose an objective function consists of the reconstruction error, classification error and discriminative sparse-code error with the non-negative regularization term on the coding coefficients. The coding vector was restricted to be non-negative using a non-negative constrained least squares model, and a blockwise coordinate descent algorithm was used to simultaneously learn a compact discriminative dictionary and a multiclass linear classifier. The experiments are carried out for the proposed methods using benchmark MIT-BIH data and evaluated under standard scheme and category-based scheme. The evaluation and experimental results show that our proposed LCNR algorithm achieves state-of-the-art performance, specifically surpassing the label consistent KSVD algorithm in terms of classification accuracy. By means of the dictionary learning algorithm, we can improve the efficiency for a large-size training database with a significantly faster execution time (more than 5 times) than NRC. [ABSTRACT FROM AUTHOR]

Purpose: Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation.Methods: Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared.Results: IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved.Conclusion: The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation. [ABSTRACT FROM AUTHOR]

Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart’s electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal into its intrinsic mode functions (IMFs) and a convolutional neural network (CNN) for automatic diagnosis is proposed. Results for the ensemble empirical mode decomposition (EEMD) method and the empirical mode decomposition (EMD) method are also compared. Results demonstrate that the combination of the CEEMD and the CNN is a potential solution for SCD prediction since 97.5% of accuracy is achieved up to 30 min in advance of the SCD event.

Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world & time-series datasets. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a critical biological signal, which usually carries a great deal of essential information about patients. The high quality ECG signals are always required for a proper diagnosis of cardiac disorders. However, the raw ECG signals are highly noisy in nature. In the paper, we propose a hybrid denoising scheme to enhance ECG signals by combining high-order synchrosqueezing transform (FSSTH) with non-local means (NLM). With this method, a noisy ECG signal is first decomposed into an ensemble of intrinsic mode functions (IMFs) by FSSTH. Then, some noise is removed by eliminating a set of noisy IMFs that are determined by a scaling exponent obtained by the detrended fluctuation analysis (DFA); while the remaining IMFs are filtered by NLM. Finally, the denoised ECG signal is obtained by reconstructing the processed IMFs. Experiments are carried out using the simulated ECG signals and real ones from the MIT-BIH database, and the denoising performances are evaluated in terms of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). Results show that the hybrid denoising scheme involving both FSSTH and NLM is able to suppress complex noise from ECG signals more effectively while preserving the details well.

Arrhythmia is one of the most persistent chronic heart diseases in the elderly and is associated with high morbidity and mortality such as stroke, cardiac failure, and coronary artery diseases. It is significant for patients with arrhythmias to automatically detect and classify arrhythmia heartbeats using electrocardiogram (ECG) signals. In this paper, we develop three robust deep convolutional neural network (DCNN) models, including a plain-CNN network and two MSF(multi-scale fusion)-CNN architectures (A and B), to aid in better feature extraction for the detection of arrhythmia and thus significantly improve the performance metrics. The proposed models are trained and tested with a public MIT-BIH arrhythmia database on five types of signals. Six groups of ablation experiments are conducted to analyze the performance of the models. The accuracy, sensitivity, and specificity obtained from MSF-CNN architecture A are higher than those from the plain-CNN model, demonstrating that the different parallel group convolution blocks (1 × 3, 1 × 5, and 1 × 7) dramatically improve a model's performance. Additionally, the best model MSF-CNN architecture B achieves an average accuracy, sensitivity, and specificity of 98.00%, 96.17%, and 96.38%, respectively. This illustrates the method with residual learning and concatenation group convolution blocks has a profound effect on the feature learning of the model. The results of ablation experiments show that our proposed biometric recognition and diagnosis network with residual learning (MSF-CNN B) achieves a rapid and reliable diagnosis approach on ECG signal classification, which has the potential for introduction into clinical practice as an excellent tool for aiding cardiologists in reading ECG heartbeat signals.

In this paper, we have exploited the first-order sliding mode control method to track the ECG data of the human heart by three different nonlinear control laws. In order to lessen the intrinsic chattering of the classic sliding mode control system, smooth function approximations of the control input, by means of the hyperbolic tangent and the saturation function, were used. The fast Fourier transform was used to evaluate the average chattering frequency of the control inputs. The synthesized control schemes namely SMC-sign, SMC-tanh, and SMC-sat, were able to track the real-world ECG signal with an average root mean square error of 0.0306 and a chattering frequency of 92.7 Hz. The findings show that the sliding mode controllers can be implemented in electronic artificial pacemakers to provide the intended results successfully. Based on today's electronics, the involved frequency range (556.4 Hz for the worst case) is quite acceptable and practical.

In this paper, an ECG biometric identification method, based on a two-dimensional convolutional neural network, is introduced for biometric applications. The proposed model includes two-dimensional convolutional neural networks that work parallel and receive two different sets of 2-dimensional features as input. First, ACDCT features and cepstral properties are extracted from overlapping ECG signals. Then, these features are transformed from one-dimensional representation to two-dimensional representation by matrix manipulations. For feature learning purposes, these two-dimensional features are given to the inputs of the proposed model, separately. Finally, score level fusion is applied to identify the user. Our experimental results show that the proposed biometric identification method achieves an accuracy of %88.57 and an identification rate of 90.48% for 42 persons.

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF.

The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation’s identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms.

Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is widely used to provide high speed data transmission and spectrum efficiency in modern wireless communication systems. Specifically, the transmission of Electrocardiogram (ECG) signal plays a main role in health monitoring systems. The privacy and security of the patient identification and medical records are considered as a main concern in health monitoring systems. In this paper, the Lightweight cryptography (LWC) is proposed to secure the ECG signal transmission from unauthorized users through the MIMO-OFDM system. The LWC is mainly used to minimize the amount of logical elements using the gate level architecture and simple key schedule in the MIMO-OFDM. The turbo code is used in MIMO-OFDM is due to its error correcting capacity that minimizes the amount of error caused during communication under the constraints of burst error and Inter Symbol Interference (ISI). Here, the ECG signals from the MIT arrhythmia database is used to analyse the secure ECG signal transmission of LWC-MIMO-OFDM method. The performance of the proposed LWC-MIMO-OFDM is taken by means of area, delay, power, number of slices, flipflops and LUTs. The LWC-MIMO-OFDM method is compared with Advanced Encryption Standard (AES) to evaluate the efficiency of LWC-MIMO-OFDM. The delay of the LWC-MIMO-OFDM for Virtex 5 device is 13.3ns, it is less when compared to the delay caused by the AES. [ABSTRACT FROM AUTHOR]

In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode, and the PCs provide the representation of the ECG signal with less redundancy. K-nearest neighbour and support vector machine classifier are utilised to assess the performance of the extracted features for the detection and classification of MI and normal (healthy control). The proposed method achieved a specificity of 99.88%, sensitivity of 99.90%, and accuracy of 99.88%. Experimental results demonstrate that the proposed method with the multiscale mode energy and PC features achieved better output compared to the previously published work.

Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachycardia. Even though all types of tachycardia diseases have fast beat rhythm as the common characteristic feature, existing deep learning architectures are trained with the corresponding disease-specific features. Most of the proposed works lack the interpretation and understanding of the results obtained. Hence, the objective of this letter is to explore the features learned by the deep learning models. For the detection of the different types of tachycardia diseases, the authors used a transfer learning approach. In this method, the model is trained with one of the tachycardia diseases called atrial fibrillation and tested with other tachycardia diseases, such as ventricular fibrillation and sinus tachycardia. The analysis was done using different deep learning models, such as RNN, LSTM, GRU, CNN, and RSCNN. RNN achieved an accuracy of 96.47% for atrial fibrillation data set, 90.88% accuracy for CU-ventricular tachycardia data set, and also achieved an accuracy of 94.71, and 94.18% for MIT-BIH malignant ventricular ectopy database for ECG lead I and lead II, respectively. The RNN model could only achieve an accuracy of 23.73% for the sinus tachycardia data set. A similar trend is shown by other models. From the analysis, it was evident that even though tachycardia diseases have fast beat rhythm as their common feature, the model was not able to detect different types of tachycardia diseases. The deep learning model could only detect atrial fibrillation and ventricular fibrillation and failed in the case of sinus tachycardia. From the analysis, they were able to interpret that, along with the fast beat rhythm, the model has learned the absence of P-wave which is a common feature for ventricular fibrillation and atrial fibrillation but sinus tachycardia disease has an upright positive P-wave. The time-based analysis is conducted to find the time complexity of the models. The analysis conveyed that RNN and RSCNN models could achieve better performance with lesser time complexity.

Pneumonia is a common disease in patients. This paper discusses the signal processing techniques of electrocardiogram (ECG) and the statistical analysis of myocardial enzymes in the diagnosis and treatment of patients with pneumonia and analyzes its significance. Detecting the activity of creatine kinase (CK), isoenzyme (CK-MB), and cardiac troponin T (CTnT) before and after treatment in patients with pneumonia and recording the changes of the electrocardiogram with non-invasive bedside conventional electrocardiogram can be useful for medical diagnosis. In addition, in order to eliminate the ECG signal noise to improve the accuracy of analysis and diagnosis, we have introduced a Butterworth filter to achieve the suppression of interference signals. Moreover, the Butterworth filter method is used to denoise the ECG signal, and the selection of wavelet function, the analysis of noise frequency band, and the threshold processing are solved accordingly. This method has the characteristics of simple calculation and fast processing speed. It can effectively suppress baseline drift noise and improve the overall operation speed. The experimental results show that the proposed method can well filter baseline drift, power frequency interference, EMG interference, and other high-frequency interference in ECG signals, and the real-time performance of the Butterworth filter method in the sampling process has good performance, which provides help for diagnosis. Via comparing and analyzing different ECG signal parameters between the groups, the serum levels of CK, CK-MB, and troponin are demonstrated to be significantly increased with the severity of the disease in patients with pneumonia. Abnormal changes in the ECG indicate that the myocardium may be affected after pneumonia in patients. The detection of CK-MB and troponin levels in the serum of patients with pneumonia has very high sensitivity and specificity. Recording electrocardiogram has good intuitive utility. ECG and myocardial enzyme monitoring are of great significance in the diagnosis and treatment of the development of the disease in patients with pneumonia.