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Background: Electrocardiograms (ECGs) in athletes commonly reveal findings related to physiologic adaptations to exercise, that may be difficult to discern from true underlying cardiovascular abnormalities. North American and European societies have published consensus statements for normal, borderline, and abnormal ECG findings for athletes, but these criteria are not based on established correlation with disease states. Additionally, data comparing ECG findings in athletes to non-athlete control subjects are lacking. Our objective was to compare the ECGs of collegiate athletes and non-athlete controls using Z-scores for digital ECG variables to better identify significant differences between the groups and to evaluate the ECG variables in athletes falling outside the normal range. Methods: Values for 102 digital ECG variables on 7206 subjects aged 17–22 years, including 672 athletes, from Hawaii Pacific Health, University of Hawaii, and Rady Children's Hospital San Diego were obtained through retrospective review. Age and sex-specific Z-scores for ECG variables were derived from normal subjects and used to assess the range of values for specific ECG variables in young athletes. Athletes with abnormal ECGs were referred to cardiology consultation and/or echocardiogram. Results: Athletes had slower heart rate, longer PR interval, more rightward QRS axis, longer QRS duration but shorter QTc duration, larger amplitude and area of T waves, prevalent R' waves in V1, and higher values of variables traditionally associated with left ventricular hypertrophy (LVH): amplitudes of S waves (leads V1-V2), Q waves (V6, III) and R waves (II, V5, V6). Z-scores of these ECG variables in 558 (83%) of the athletes fell within − 2.5 and 2.5 range derived from the normal population dataset, and 60 (8.9%) athletes had a Z-score outside the − 3 to 3 range. While 191 (28.4%) athletes met traditional voltage criteria for diagnosis of LVH on ECG, only 53 athletes (7.9%) had Z-scores outside the range of -2.5 to 2.5 for both S amplitude in leads V1-V2 and R amplitude in leads V5-6. Only one athlete was diagnosed with hypertrophic cardiomyopathy with a Z-score of R wave in V6 of 2.34 and T wave in V6 of -5.94. Conclusion: The use of Z-scores derived from a normal population may provide more precise screening to define cardiac abnormalities in young athletes and reduce unnecessary secondary testing, restrictions and concern. Key Points: • Athletes had slower heart rate, longer PR interval, greater QRS axis, longer QRS duration, shorter QTc interval, higher peak amplitude of S waves in leads V1 and V2, Q waves in leads III and V6, R waves in leads II, V5, and V6 compared to control subjects. • However, most of the athletes had ECG variable Z-scores within range of -2.5 and 2.5 (83%) and − 3 and 3 (91.1%), all of which had no identified cardiac pathologies. • ECG assessment in athletes utilizing Z-scores derived from normal subjects may guide clinical decision making regarding secondary screening. [ABSTRACT FROM AUTHOR]

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rate variability (HRV) for exercise fatigue classification. First, the ECG signals are converted into 2-D images by using the short-term Fourier transform (STFT), and image features are extracted by the visual geometry group (VGG). The extracted image and linear features of ECG and HRV are sent to the different types of classifiers to distinguish distinct exercise fatigue level. To validate performance, the proposed methods are tested on (i) an open-source EPHNOGRAM dataset and (ii) a self-collected dataset (n = 51). The results reveal that the classification based on the concatenated features has the highest accuracy, and the calculation time of the system is also significantly reduced. This demonstrates that the proposed novel hybrid approach can be used to assist in improving the accuracy and timeliness of exercise fatigue classification in a real-time exercise environment. The experimental results show that the proposed method outperforms other recent state-of-the-art methods in terms of accuracy 96.90%, sensitivity 96.90%, F1-score of 0.9687 in EPHNOGRAM and accuracy 92.17%, sensitivity 92.63%, F1-score of 0.9213 in self-collected dataset. [ABSTRACT FROM AUTHOR]

Introduction: The QRS complex is the most prominent waveform within the electrocardiograph (ECG) signal. The accurate detection of the QRS complex is an essential step in the ECG analysis algorithm, which can provide fundamental information for the monitoring and diagnosis of the cardiovascular diseases. Methods: Seven public ECG datasets were used in the experiments. A simple and effective QRS complex detection algorithm based on the deep neural network (DNN) was proposed. The DNN model was composed of two parts: a feature pyramid network (FPN) based backbone with dual input channels to generate the feature maps, and a location head to predict the probability of point belonging to the QRS complex. The depthwise convolution was applied to reduce the parameters of the DNN model. Furthermore, a novel training strategy was developed. The target of the DNN model was generated by using the points within 75 milliseconds and beyond 150 milliseconds from the closest annotated QRS complexes, and artificial simulated ECG segments with high heart rates were generated in the data augmentation. The number of parameters and floating point operations (FLOPs) of our model was 26976 and 9.90M, respectively. Results: The proposed method was evaluated through a cross-dataset test and compared with the sophisticated state-of-the-art methods. On the MITBIH NST, the proposed method demonstrated slightly better sensitivity (95.59% vs. 95.55%) and lower presicion (91.03% vs. 92.93%). On the CPSC 2019, the proposed method have similar sensitivity (95.15% vs.95.13%) and better precision (91.75% vs. 82.03%). Discussion: Experimental results show the proposed algorithm achieved a comparable performance with only a few parameters and FLOPs, which would be useful for the application of ECG analysis on the wearable device. [ABSTRACT FROM AUTHOR]

Backgrounds: The validity of heart rate variability (HRV) has been substantiated in mental workload assessments. However, cognitive tasks often coincide with physical exertion in practical mental work, but their synergic effects on HRV remains insufficiently established. The study aims were to investigate the combined effects of cognitive and physical load on autonomic nerve functions. Methods: Thirty-five healthy male subjects (aged 23.5 ± 3.3 years) were eligible and enrolled in the study. The subjects engaged in n-back cognitive tasks (1-back, 2-back, and 3-back) under three distinct physical conditions, involving isotonic contraction of the left upper limb with loads of 0 kg, 3 kg, and 5 kg. Electrocardiogram signals and cognitive task performance were recorded throughout the tasks, and post-task assessment of subjective experiences were conducted using the NASA-TLX scale. Results: The execution of n-back tasks resulted in enhanced perceptions of task-load feelings and increased reaction times among subjects, accompanied by a decline in the accuracy rate (p < 0.05). These effects were synchronously intensified by the imposition of physical load. Comparative analysis with a no-physical-load scenario revealed significant alterations in the HRV of the subjects during the cognitive task under moderate and high physical conditions. The main features were a decreased power of the high frequency component (p < 0.05) and an increased low frequency component (p < 0.05), signifying an elevation in sympathetic activity. This physiological response manifested similarly at both moderate and high physical levels. In addition, a discernible linear correlation was observed between HRV and task-load feelings, as well as task performance under the influence of physical load (p < 0.05). Conclusion: HRV can serve as a viable indicator for assessing mental workload in the context of physical activities, making it suitable for real-world mental work scenarios. [ABSTRACT FROM AUTHOR]

Continuous monitoring of physiological parameters is critical in order to minimize casualties, therefore automated healthcare computer-assisted diagnostic systems require efficient signal processing techniques. It is currently the primary concern upon which the majority of research is concentrated. It is becoming a way of life to reach long age by which accurate and error-free treatments are done. Electrocardiograms (ECGs) are crucial in the current era for minimizing the global mortality rate. An essential clinical feature of electrocardiograms (ECGs) is the determination of heart rate. It is measured in beats per minute. It differs based on age and a number of other associated variables, including body position, temperature, emotion, smoke, and hormonal imbalances, among others. Arrhythmia of the heart, characterised by an irregular heart rate, is currently the leading cause of increased mortality. The complete diagnosis of ECG is accomplished using the blocks such as pre-processing, feature extraction, and detection/classification. Unfortunately, the analysis of ECG becomes complex under the wide variety of the recording datasets which is influenced by variety of noises and artifacts. It motivates to choose a novel combination of above-mentioned blocks which can serve the purpose. The fractional wavelet transform (FrWT) is chosen in conjunction with independent principal component analysis (IPCA) in this article. FrWT, which combines the fractional Fourier transform and the wavelet transform, satisfies the requirements for feature extraction and pre-processing, respectively, whereas IPCA satisfies the requirement for detection and classification. It also helps to understand the cardiac and non-cardiac behaviour of the electrical recording of the specific critical cases. [ABSTRACT FROM AUTHOR]

The number of people experiencing mental stress or emotional dysfunction has increased since the onset of the COVID-19 pandemic, as many individuals have had to adapt their daily lives. Numerous studies have demonstrated that mental health disorders can pose a risk for certain diseases, and they are also closely associated with the problem of mental workload. Now, wearable devices and mobile health applications are being utilized to monitor and assess individuals' mental health conditions on a daily basis using heart rate variability (HRV), typically measured by the R-to-R wave interval (RRI) of an electrocardiogram (ECG). However, portable or wearable ECG devices generally require two electrodes to perform bipolar limb leads, such as the Einthoven triangle. This study aims to develop a single-arm ECG measurement method, with lead I ECG serving as the gold standard. We conducted static and dynamic experiments to analyze the morphological performance and signal-to-noise ratio (SNR) of the single-arm ECG. Three morphological features were defined, RRI, the duration of the QRS complex wave, and the amplitude of the R wave. Thirty subjects participated in this study. The results indicated that RRI exhibited the highest cross-correlation (R = 0.9942) between the single-arm ECG and lead I ECG, while the duration of the QRS complex wave showed the weakest cross-correlation (R = 0.2201). The best SNR obtained was 26.1 ± 5.9 dB during the resting experiment, whereas the worst SNR was 12.5 ± 5.1 dB during the raising and lowering of the arm along the z-axis. This single-arm ECG measurement method offers easier operation compared to traditional ECG measurement techniques, making it applicable for HRV measurement and the detection of an irregular RRI. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing. • A multi-branch convolutional neural network model based on channel attention and bidirectional LSTM is proposed. • Cross-validation is used to improve the generalization capability of the network. • Multi-branch convolutional neural network is used to solve the problem of gradient disappearance or gradient explosion caused by network depth. • The classification performance comparison with other models verifies the superiority of our model. [ABSTRACT FROM AUTHOR]

In this study, we investigated the use of empirical mode decomposition (EMD)-based features extracted from electrocardiogram (ECG) RR interval signals to differentiate between different levels of cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes mellitus (T2DM). This study involved 60 participants divided into three groups: no CAN, subclinical CAN, and established CAN. Six EMD features (area of analytic signal representation— A S R a r e a ; area of the ellipse evaluated from the second-order difference plot— S O D P a r e a ; central tendency measure of SODP— S O D P C T M ; power spectral density (PSD) peak amplitude— P S D p k a m p ; PSD band power— P S D b p o w ; and PSD mean frequency— P S D m f r e q ) were extracted from the RR interval signals and compared between groups. The results revealed significant differences between the noCAN and estCAN individuals for all EMD features and their components, except for the P S D m f r e q . However, only some EMD components of each feature showed significant differences between individuals with noCAN or estCAN and those with subCAN. This study found a pattern of decreasing A S R a r e a and S O D P a r e a values, an increasing S O D P C T M value, and a reduction in P S D b p o w and P S D p k a m p values as the CAN progressed. These findings suggest that the EMD outcome measures could contribute to characterizing changes associated with CAN manifestation in individuals with T2DM. [ABSTRACT FROM AUTHOR]

Humans often perform rhythmic synchronized movements. Professional musicians and dancers particularly perform such movement tasks well and have a higher interoceptive accuracy (IAcc) than non-musicians and nondancers. We thus hypothesized that rhythmic synchronized movements might be enhanced by a higher IAcc. To investigate this hypothesis, this study conducted a heartbeat counting task and a rhythmic synchronization tapping task with normal (easier) and slow (harder) tempi metronomes. Inconsistent with our hypothesis, however, a higher IAcc was negatively correlated with timing control, but only in the slow tempo condition [r (30) = 0.46, p < 0.05]. This suggests that a higher IAcc did not enhance timing control in rhythmic synchronized movements but rather weakened it, resting heart rate variability was not correlated with timing control. [ABSTRACT FROM AUTHOR]

This paper is to explore the improvement of clinical symptoms in patients with cardiovascular neurosis (CN) by physical exercise based on the deep learning architecture of edge computing, and to deeply explore the effect of physical exercise on autonomic function. Fifty-two patients with CN in this cardiovascular rehabilitation center were randomly divided into drug group and exercise group, with 26 cases in each group, and their electrocardiogram (ECG) was examined. Based on the deep learning architecture of edge computing, a four-layer stacked sparse auto encoder (SSAE) deep neural network was constructed, and the accuracy rates of least squares support vector machine (LSSVM), message passing neural network (MPNN), convolutional neural network (CNN), and SSAEs were measured to be 95.4%, 93.6%, 96.3%, and 99.5%, respectively. After physical exercise intervention, the total score of Symptom Checklist 90 (SCL-90) as well as each single item score were lower in the exercise group than in the drug group (P < 0. 0 5). Heart rate recovery (HRR1) improved more significantly after 1 min of exercise in patients in the exercise group (P < 0. 0 5). The low-frequency (LF) power and normalized low-frequency (LFn) power of blood pressure variability (BPV) parameters in the exercise group were lower than those in the drug group (P < 0. 0 5); the total power (TP), high-frequency (HF) power, and normalized high-frequency (HFn) power of heart rate variability (HRV) parameters in the exercise group were higher than those in the drug group (P < 0. 0 5), LF/HF in the exercise group was lower than that in the drug group (P < 0. 0 5); and the baroreflex sensitivity (BRS) in the exercise group was higher than that in the drug group (P < 0. 0 5). A four-layer SSAEs was successfully constructed; the mechanism of exercise may be related to the regulation of cardiovascular autonomic nervous function, and it can effectively prevent and treat the clinical symptoms of patients with CN. [ABSTRACT FROM AUTHOR]

The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research. • A scoping review identified 124 papers, 19 using machine learning and 105 without. • EEG and ECG biomarkers are associated with posttraumatic-stress disorder. • ECG and classical ML methodologies were the focus of most research. • Biomarkers were largely associated with arousal regulation issues. [ABSTRACT FROM AUTHOR]

Heart health monitoring based on wearable devices is often contaminated by various noises to varying degrees. Using signal quality indicators (SQIs) to achieve signal quality assessment (SQA) is among the most promising ways to solve this problem, but the performance of SQIs in expressing ECG quality features contaminated by motion artifact (MA) noise remains disappointing. Here, we present a novel SQA method that fuses the proposed depth local dual-view (DLDV) features and the dual-input transformer (DI-Transformer) framework to improve the recognition ability of MA-contaminated ECGs. The proposed DLDV features are to identify subtle differences between MA and ECG through depth local amplitude and phase angle features. When it fuses with the temporal relationship features extracted by DI-Transformer, its accuracy is significantly improved compared to the SQIs-based methods. In addition, we also verify the robustness and the accuracy of DLDV features on four traditional classifiers. Finally, we conduct our experiments on the two datasets. On the PhysioNet/Computing in Cardiology Challenge dataset, the DLDV features (Acc = 95.49%) outperform the combination of six SQIs features (Acc = 91.26%). When combined with our DI-Transformer, it delivered an accuracy of 99.62%, outperforming the state-of-the-art SQA methods. On the artificial testset constructed by MA noise, our DI-Transformer outperforms four traditional methods and also delivered an accuracy of 97.69%. [ABSTRACT FROM AUTHOR]

This paper focuses on designing a field-programmable gate array (FPGA)-based architecture for R-peak detection and heart rate calculation using lifting-based discrete wavelet transform (DWT). An efficient and low-cost architecture for Daubechies 4 lifting-based DWT for a decomposition level of four is also proposed. The proposed architecture is folded after the first decomposition level to avoid repetitive blocks for different decomposition levels. The noise-removal and preprocessing of the electrocardiogram (ECG) signals are carried out using lifting-based DWT. The 16-bit fixed-point representation is employed throughout the design to reduce the hardware complexity. The entire design is evaluated in both MATLAB R2020a and XILINX VIVADO 2017.4. The proposed architecture is validated using three ECG databases, namely the MIT-BIH arrhythmia, the MIT-BIH supraventricular, and the QT database. A total of 122 distinct ECG datasets are tested on FPGA to determine the effectiveness of the proposed R-peak detection architecture. The R-peaks detected using the proposed technique show no noticeable error with respect to actual R-peaks. The FPGA implementation is performed using the Artix-7 board that utilizes 2197 LUTs and 486 flip-flops at an operating frequency of 44 MHz. The proposed architecture achieves sensitivity, accuracy, positive predictivity, and detection rate of 99.52, 99.43, 99.91, and 0.565%, respectively, in MATLAB, and 99.44, 98.88, 99.43, and 1.09%, respectively, in FPGA. In terms of both hardware utilization and R-peak detection rate achieved, the proposed architecture is suitable to use as a part of low-cost smart biomedical devices to perform continuous monitoring of ECG signals and automatic detection of heart rates. [ABSTRACT FROM AUTHOR]

In the present scenario, Electrocardiogram (ECG) is an effective non-invasive clinical tool, which reveals the functionality and rhythm of the heart. The non-stationary nature of ECG signal, noise existence, and heartbeat abnormality makes it difficult for clinicians to diagnose arrhythmia. The most of the existing models concentrate only on classification accuracy. In this manuscript, an automated model is introduced that concentrates on arrhythmia type classification using ECG signals, and also focuses on computational complexity and time. After collecting the signals from the MIT-BIH database, the signal transformation and decomposition are performed by Multiscale Local Polynomial Transform (MLPT) and Ensemble Empirical Mode Decomposition (EEMD). The decomposed ECG signals are given to the feature extraction phase for extracting features. The feature extraction phase includes six techniques: standard deviation, zero crossing rate, mean curve length, Hjorth parameters, mean Teager energy, and log energy entropy. Next, the feature dimensionality reduction and arrhythmia classification are performed utilizing the improved Firefly Optimization Algorithm and autoencoder. The selection of optimal feature vectors by the improved Firefly Optimization Algorithm reduces the computational complexity to linear and consumes computational time of 18.23 seconds. The improved Firefly Optimization Algorithm and autoencoder model achieved 98.96% of accuracy in the arrhythmia type classification, which is higher than the comparative models. [ABSTRACT FROM AUTHOR]

In recent decades, it has been uncovered that the autonomic nervous system (ANS) can be influenced using non-contact neuromodulation via odor stimulation. Increasing parasympathetic-vagal activation of the ANS is integral to improving the sympathovagal balance between the sympatheticand parasympathetic nervous systems, which is often imbalanced in several chronic inflammatory disorders, such as rheumatoid arthritis and inflammatory bowel diseases. Although research into olfactory stimulation has been observed on the ANS, it is still lacking in the exploration of odor concentration and odor-specific effects. This is particularly the case as research has not utilized specified tools, such as the olfactometer to provide precise odor delivery. Furthermore, no research has compared the results in separate sex cohorts to investigate the role of sex or the menstrual stage on the subsequent interactions. In this study, we investigated the olfactory stimulation effects of four natural odors (mushroom, lavender, jasmine, and rose) in three concentrations (low, moderate, and high) on the ANS. To observe activity from the ANS, we used an electrocardiogram (ECG) based heart rate variability (HRV) and eye-tracker technology (pupil diameter). We found for the first time in literature that there were acute dose- and durationspecific odor effects of odors on the ANS. We also found sex and menstrual cycle effects in this interaction. Furthermore, there were stark distinctions in sympathovagal activity dependent ANS activation (HRV) in comparison to the oculomotor nerve-parasympathetic/cervical sympathetic nerves dependent ANS responses (pupil diameter). Sympathovagal activity dependent HRV showed odor, sex, and menstrual-stage interactions in both divisions of the ANS while the pupil responses only indicated increased sympathetic activation. These results shed light on the use of odor-specific stimulation to modulate the ANS activity in the context of sex and the menstrual stage. Future studies should be performed using a chronic odor delivery design to investigate the long-term effects of odors on the ANS. [ABSTRACT FROM AUTHOR]

• Prediction of the emotional states in children with ASD using ECG signals. • Customized elicitation protocol using audio and video clips for data acquisition. • Analysis of geometrical and time domain features of positive and negative emotions. • KNN and Ensemble classifier were used for the classifying the two emotional states. • Geometrical features resulted in good accuracy of 84.8% and 74.7% in both states. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by various social impairments. Children with ASD have major difficulties in expressing themselves, resulting in stress and meltdowns. Understanding their hidden feelings and needs may help in tackling and avoiding such strenuous behaviors. This research aims to aid the parents and caretakers of children with ASD to understand the hidden and unexpressed emotional state by using physiological signals obtained from wearable devices. Here, electrocardiogram (ECG) signals pertaining to two valence states ('like' and 'dislike') were recorded from twenty children (10 Control and 10 children with ASD). The heart rate variability (HRV) signals were then obtained from the ECG signals using the Pan-Tompkins's algorithm. The statistical, higher order statistics (HOS) and geometrical features which were statistically significant were trained using the K Nearest Neighbor (KNN) and Ensemble Classifier algorithms. The findings of our analysis indicate that the integration of major statistical features resulted in an overall average accuracy of 84.8% and 75.3% using HRV data for the control and test population, respectively. Similarly, geometrical features resulted in a maximum average accuracy of 84.8% and 74.2% for control and test population respectively. The decreased HRV in the test population indicates the presence of autonomic dysregulation in children with ASD when compared to their control peers. [ABSTRACT FROM AUTHOR]

Exercise games (exergames) that combine both exercise and video gaming train people in a fun and competitive manner to lead a healthy lifestyle. Exergames promote more physical exertion and help users exercise more easily and independently in any place. Many studies have been conducted to evaluate the positive effects of exergames. However, in most studies, heart rate was mainly used to measure the effect of exercise. In this study, we evaluate the effects of exercise according to the exercise type (rest, walking, tennis, and running) and gameplay mode (single, competition, and cooperation) of exergaming via quantitative measurements using electrocardiogram (ECG) and Kinect. The multiple comparison results reveal that physical activity measured with Kinect was statistically significant even in exergames that did not show statistically significant differences according to ECG. Running was statistically significant compared to other exercise types, and there was a significant difference in competition compared to other gameplay modes. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting. [ABSTRACT FROM AUTHOR]

Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager–Kaiser energy (TKE) operator to preprocess the SKNA signal, and then candidates of burst areas were segmented by an envelope-based method. Since the burst of SKNA can also be discriminated by the high-frequency component in QRS complexes of electrocardiogram (ECG), a strategy was designed to reject their influence. Finally, a feature of the SKNA energy ratio (SKNAER) was proposed for quantifying the SKNA. The method was verified by both sympathetic nerve stimulation and hemodialysis experiments compared with traditional heart rate variability (HRV) and a recently developed integral skin sympathetic nerve activity (iSKNA) method. The results showed that SKNAER correlated well with HRV features (r = 0.60 with the standard deviation of NN intervals, 0.67 with low frequency/high frequency, 0.47 with very low frequency) and the average of iSKNA (r = 0.67). SKNAER improved the detection accuracy for the burst of SKNA, with 98.2% for detection rate and 91.9% for precision, inducing increases of 3.7% and 29.1% compared with iSKNA (detection rate: 94.5% (p < 0.01), precision: 62.8% (p < 0.001)). The results from the hemodialysis experiment showed that SKNAER had more significant differences than aSKNA in the long-term SNA evaluation (p < 0.001 vs. p = 0.07 in the fourth period, p < 0.01 vs. p = 0.11 in the sixth period). The newly developed feature may play an important role in continuously monitoring SNA and keeping potential for further clinical tests. [ABSTRACT FROM AUTHOR]

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This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats’ signal waveform. Additionally, we are able to train the model in the presence of label noise. The model’s performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) has long been a common method of assessing and diagnosing cardiovascular problems (CaDs). The heart's electrical activity is visualized in the form of a waveform and its analysis can turn out to provide valuable insights about the functioning and normalcy of a healthy heart or detect a wide range of possible heart risks. This article deals with the Internet of Medical Things (IoMT) based real time healthcare monitoring system which diagnoses the ECG signals remotely and hence manifests essential heart conditions. An electrocardiograph machine captures medical-grade patient heart data. The outcome of the project includes detecting the heart rate, the various interval and segment analysis of the signal, cardiac axis detection as well as prompting the various possible conditions in case of anomalies in the heart activities. In such cases, the system can send an automatic ECG report from the patient to the doctor safely by encrypting this report. It can be further modified to generate a call in case of critical condition and provide urgent medical assistance. During the ECG analysis, the article presented clearly results represented by finding different values for different patients starting from QRS width 96, PR Interval 133, and P amplitude 2.81, and ending last patient test QRS width 93, PR Interval 112, and P amplitude 1.57. [ABSTRACT FROM AUTHOR]

This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat abnormalities. The physiological detection using electrocardiogram (ECG) signals has been the most popular means and widely accepted automated detection system to monitor heart health. Additionally, arrhythmia beat classification plays a prominent role in electrocardiogram (ECG) analysis dedicated elucidate cardiac health status while analyzing heart rhythm. The authors aim to classify ECG samples into major arrhythmia classes precisely by removing the inherent noise of ECG signals in preprocessing phase using discrete wavelet transformation (DWT). The QRS complex plays a crucial role in ECG signal identification. Therefore, the position and amplitude of R-peaks are determined to detect the QRS complex. The feature vectors of the QRS complex are further optimized with cuckoo search (CS) optimization algorithm in addition to denoising signals using DWT to select the most relevant set of features. The Support vector machine (SVM)-trained support vector contains the best training information used to train feed-forward back-propagation neural network (FFBPNN) to propose the variant DWT + CS + SVM-FFBPNN to classify signals among five classes. MIT-BIH arrhythmia database is utilized for different types of heartbeats. The classification analysis based on a variant with optimized feature vector using cuckoo search algorithm and SVM-FFBPNN determines heart rate with an accuracy of 98.319%. In contrast, the variant FFBPNN without optimization obtains 97.95% accuracy. The improved performance of the novel combination of classifiers resulted in overall classification accuracy of 98.53% with precision and recall of 98.247% and 95.68%, respectively. The simulation analysis comprising 3600 samples and 1160 heartbeats also outperformed the existing arrhythmia classifications performed based on neural networks. This illustrates the success of the proposed ECG classification model in accurately categorizing ECG signals for arrhythmia classification. [ABSTRACT FROM AUTHOR]

Heart attack else wise termed as myocardial infarction (MI) causes irreparable death of cardiac muscles yielding the focal reason for most casualties among all cardiovascular diseases (CVDs’). A 12-lead electrocardiogram (ECG) generally depicts cardiac abnormalities and so customary deep learning (DL) approaches use the whole signal for binary detection purposes, that is separating healthy control (HC), and MI classes. This paper proposes an alternative approach where 21 temporal features in lieu of the temporal signal are collected from the 12 lead data to reduce redundancy and class imbalance keeping the vital information intact. Then these extracted features are fed into a detection model consisting of a one dimensional (1-D) convolutional neural network (CNN) and a bidirectional long short-term memory (bi-LSTM) layer which classifies into three classes, namely: HC, MI, and non-myocardial infarction (non-MI) subjects for a realistic and reliable assessment. The model’s performance is evaluated using 517 records acquired from the Physikalisch-Technische Bundesanstalt (PTB) database and a state-of-art overall accuracy of 99.246%, kappa of 0.983, and macro averaged F1 score of 98.86% were achieved using stratified 5-fold cross-validation. DL methods suffer to make unbiased decisions in the case of class imbalance due to an insufficient amount of data for a particular class and thus temporal features are employed to inherently reduce this problem. The successful performance of the extracted features depends on the precise detection of fiducial points, and so multiple novel algorithms have been introduced in this paper. [ABSTRACT FROM AUTHOR]

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Autonomic nervous system (ANS) can maintain homeostasis through the coordination of different organs including heart. The change of blood glucose (BG) level can stimulate the ANS, which will lead to the variation of Electrocardiogram (ECG). Considering that the monitoring of different BG ranges is significant for diabetes care, in this paper, an ECG-based technique was proposed to achieve non-invasive monitoring with three BG ranges: low glucose level, moderate glucose level, and high glucose level. For this purpose, multiple experiments that included fasting tests and oral glucose tolerance tests were conducted, and the ECG signals from 21 adults were recorded continuously. Furthermore, an approach of fusing density-based spatial clustering of applications with noise and convolution neural networks (DBSCAN-CNN) was presented for ECG preprocessing of outliers and classification of BG ranges based ECG. Also, ECG's important information, which was related to different BG ranges, was graphically visualized. The result showed that the percentages of accurate classification were 87.94% in low glucose level, 69.36% in moderate glucose level, and 86.39% in high glucose level. Moreover, the visualization results revealed that the highlights of ECG for the different BG ranges were different. In addition, the sensitivity of prediabetes/diabetes screening based on ECG was up to 98.48%, and the specificity was 76.75%. Therefore, we conclude that the proposed approach for BG range monitoring and prediabetes/diabetes screening has potentials in practical applications. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) has attracted intense research interests and contests due to its great value in biometric applications, especially with the technological progress of smart instrumentation and artificial intelligence nowadays. For the issue of ECG-based human identification, distance measure in a suitable data space can be an effective solution. Almost all conventional distance measures rely on the independent data samples for classification. However, such measuring mechanism is vulnerable to the variation and bias of data distribution, which are easily caused by the noisy artifacts in this issue. To tackle this problem, we suggest doing distance measurement at the level of multiple-set bundle that consists of multiple sample sets obtained under different conditions. More specifically, we propose a novel method, “Collaborative-Set Measurement (CSM),” which creatively extends distance measure from the sample level through the set level to the bundle level. CSM not only captures the representative information from within-set distribution but also exploits the discriminative information from between-set collaboration, and meanwhile incorporates the robust information from in-bundle fusion. Attributing to these information, this method can largely enhance the identification performance in this issue despite the serious noisy artifacts influencing the data samples. The experimental results have demonstrated the capability of CSM for ECG-based human identification, by means of the public challenging database, DREAMER, on which the proposed method produces an accuracy of 91.30%. In summary, this proposal has initially brought forward a new thought of “collaborative sets.” This thought may provide inspirations for further researches on biometric recognition and general signal classification topics. [ABSTRACT FROM AUTHOR]

Methamphetamine abuse is getting worse amongst the younger population. While there is methadone or buprenorphine harm-reduction treatment for heroin addicts, there is no drug treatment for addicts with methamphetamine use disorder (MUD). Recently, non-medication treatment, such as the cue-elicited craving method integrated with biofeedback, has been widely used. Further, virtual reality (VR) is proposed to simulate an immersive virtual environment for cue-elicited craving in therapy. In this study, we developed a VR system equipped with flavor simulation for the purpose of inducing cravings for MUD patients in therapy. The VR system was integrated with multi-model sensors, such as an electrocardiogram (ECG), galvanic skin response (GSR) and eye tracking to measure various physiological responses from MUD patients in the virtual environment. The goal of the study was to validate the effectiveness of the proposed VR system in inducing the craving of MUD patients via the physiological data. Clinical trials were performed with 20 MUD patients and 11 healthy subjects. VR stimulation was applied to each subject and the physiological data was measured at the time of pre-VR stimulation and post-VR stimulation. A variety of features were extracted from the raw data of heart rate variability (HRV), GSR and eye tracking. The results of statistical analysis found that quite a few features of HRV, GSR and eye tracking had significant differences between pre-VR stimulation and post-VR stimulation in MUD patients but not in healthy subjects. Also, the data of post-VR stimulation showed a significant difference between MUD patients and healthy subjects. Correlation analysis was made and several features between HRV and GSR were found to be correlated. Further, several machine learning methods were applied and showed that the classification accuracy between MUD and healthy subjects at post-VR stimulation attained to 89.8%. In conclusion, the proposed VR system was validated to effectively induce the drug craving in MUD patients. [ABSTRACT FROM AUTHOR]

Background: The assessment of newborns' heart rate (HR) in the delivery room is one of the important steps to ascertain the need for initiation and continuation of resuscitation. At present, ECG is the "gold standard" to monitor neonatal HR in the delivery room. However, various limitations with the use of ECG exist. Furthermore, in developing countries, ECG may not be universally available in delivery rooms.Objective: To compare the accuracy and HR acquisition time of portable Doppler ultrasound (PDU) versus electrocardiogram (ECG) in newborns.Methods: This multicenter, prospective, observational study across five centers in India between January and September 2017 included neonates more than 34 weeks of gestation (n = 131) delivered by cesarean section. The accuracy of HR recorded by PDU (HRPDU) versus that by ECG (HRECG) was the primary outcome. Secondary outcomes included time to acquisition of an audible and/or visible signal and device application.Results: Mean (±SD) gestational age and birthweight were 37.7 (±1.2) weeks and 2954 (±457) g, respectively. The mean (±SD) visible HRPDU was 158 (±21) bpm versus HRECG of 161.3 (±20) bpm (p = .07) which were comparable. The median (1st, third quartile) time to acquisition of audible HRPDU (76 [51, 91] s), was significantly shorter than that of HRECG (96.5 [74.2, 118] s; p < .001).Conclusion: Portable Doppler has similar accuracy to ECG and is faster in acquiring the signal. [ABSTRACT FROM AUTHOR]

This article presents a novel multidimensional feature extraction and selection method for electrocardiogram (ECG) arrhythmias classification. First, a novel multi-interval symmetrized dot pattern method (MSDP) is proposed to extract image shape features, which are combined with time-frequency features, morphological features and RR interval features to form a new feature set. Then a novel average weight based ReliefF method (WReliefF) is proposed to integrate with genetic algorithm (GA) and support vector machine (SVM) and form a feature selection method named as WReliefF-GA-SVM. The WReliefF-GA-SVM is a hybrid type feature selection method, and it can be divided into Filter stage and Wrapper stage to select features. In Filter stage, the algorithm uses WReliefF to sort the features according to the average weight. In Wrapper stage, GA and SVM are combined, and the classification performance of SVM is used as the fitness function to search the best feature subsets. Finally, the five types of ECG beats from the MIT-BIH arrhythmia database are classified by a one against all (OAA) SVM model. The results show that the proposed method achieves average sensitivity, specificity and accuracy of 99.42%, 99.83%, and 99.74%, respectively. [ABSTRACT FROM AUTHOR]

Ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) of a heart rate over 180 beats min−1, commonly known as shockable rhythms (ShR), are life-threatening ventricular arrhythmias. This study was to assess the feasibility of feeding two-dimensional (2D) time-frequency maps of electrocardiogram (ECG) segment into deep convolutional neural network (CNN) to automatically detect ShR with emphases on optimizing the CNN model and shortening the analysis segment. A total of 115 single-lead surface ECG records collected from four publicly accessible ECG databases were divided into non-overlapping 3-second segments as well as 5-, 8-, and 10-second segments for comparisons. For each one-dimension (1D) ECG segment, its 2D time-frequency maps was obtained by employing a continuous wavelet transform (CWT). Moreover, the performance of automated algorithms with eight different CNN models was evaluated for ShR detection against annotations labeled in the databases. A total of 5,795 3-second maps of ShR and 48,648 maps of non-shockable rhythms (NSR) were analyzed successfully, and a promising performance of the automated algorithm with a twelve-layer CNN model was achieved on this segment length in terms of accuracy (98.82%), sensitivity (95.05%), and specificity (99.43%). Compared with current approaches imposing priority either only on highly real-time or high performance, the presented solution suggested that the deep learning based algorithm together with ECG time-frequency maps may offer the possibility of providing an intelligent and efficient detection of ShR and NSR in short segments especially for public access defibrillation. [ABSTRACT FROM AUTHOR]

Fatigue detection based on electrocardiogram (ECG) from wearable devices is a convenient approach during exercise. However, how to model the data variations of ECG time series and capture effective information for accurate fatigue detection is still challenging. In this study, we propose a novel fatigue assessment architecture designed to adaptively extract and integrate both the ECG and heart rate variability (HRV) features, so as to obtain rich contextual information from ECG sequences and improve exercise-induced fatigue prediction performance. Inspired by the ECG observation process of human experts, the hierarchical transformer framework is designed to model the long-range dependencies of ECG time series and obtain multi-scale ECG representations. To reduce the computation cost, we also devise a sampling-efficient multi-head attention (SMA) mechanism. Finally, a novel gated feature fusion layer is developed to adaptively learn the weights of both the ECG and HRV features for fatigue prediction. The proposed fatigue prediction system is validated with real fatigue datasets which were collected from healthy participants. The experimental results show that the proposed approach outperforms other recent state-of-the-art systems in terms of accuracy 94.0%, specificity 99.0%, recall 89.3%, F1-score 93.7%, G-mean score 91.7%, MCC 0.837, and Cohen's Kappa 0.847, based on the ECG time series with duration 20s. This demonstrates that the proposed system can be used to accurately detect fatigue in real time during exercise, such that assisting coaches or athletes to monitor and adjust their exercise session for preventing overexertion and fatigue-induced injury. [ABSTRACT FROM AUTHOR]

Timely detection of cardiac abnormalities from an Electrocardiogram (ECG) signal is very essential. This requires its appropriate and efficient processing. In the literature, most of the researchers focussed on linear techniques that were applied on filtered ECG datasets leaving an ample scope for exploring the use of non-linear techniques in the presence and absence of natural noise-processes. Therefore, there is a need of supplementing the existing research on ECG signal interpretation by using non-linear techniques on noisy ECG data. Non-linear techniques are expected to yield supplementary clues about the non-linearities in the underlying cardiovascular system. One such promising non-linear technique, known as chaos theory (analysis), has been considered here to estimate reliable and robust thresholds for R-peak detection using fractal dimension, Approximate Entropy, Sample Entropy, correlation dimension, and Lyapunov exponent based on time-delay dimension (embedding). Also, time–frequency analysis techniques have shown their effectiveness for analyzing such types of non-linear and non-stationary signals due to simultaneous interpretation of the signal in both time and frequency domain. Among existing TFA techniques such as wavelet transform, short time Fourier transform, Hilbert transform, Auto-regressive Time Frequency Analysis (ARTFA) offers good time–frequency resolution. Therefore, Chaos theory and ARTFA have been considered in this paper. First, raw ECG signal was filtered using Savitzky Golay Digital Filtering (SGDF) because it retains all important signal features after filtering. Second, a novel optimal trajectory detection step was proposed on the basis of phase space reconstruction (attractors) in chaos theory. Third, ARTFA has been used to find the spectral components of the extracted features using chaos theory. Here, ARTFA has been used for finding the autoregressive coefficients in the first step and time–frequency description in the second step. Burg method has been considered for Auto-regressive modeling to fit an ARTFA model for analyzing ECG signal by minimizing (least squares) the forward and backward prediction errors. MIT-BIH arrhythmia database has been considered for validating the present research effort. Some real time signals were also tested to explore direct usage of the proposed technique in practical applications. The obtained results show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias. Computational cost of the proposed technique is reduced to a great extent by using the chaos theory (analysis) yielding efficient detection performance. All results have been obtained in MATLAB environment R2011a. Improved values viz. 99.96% SE, 99.97% PPV, and 99.93% ACC are obtained. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a type of paroxysmal cardiac disease that presents no obvious symptoms during onset, and even the electrocardiograms (ECG) results of patients with AF appear normal under a premorbid status, rendering AF difficult to detect and diagnose. However, it can result in deterioration and increased risk of stroke if not detected and treated early. This study used the ECG database provided by the Physionet website (), filtered data, and employed parameter-extraction methods to identify parameters that signify ECG features. A total of 31 parameters were obtained, consisting of P-wave morphology parameters and heart rate variability parameters, and the data were further examined by implementing a decision tree, of which the topmost node indicated a significant causal relationship. The experiment results verified that the P-wave morphology parameters significantly affected the ECG results of patients with AF. [ABSTRACT FROM AUTHOR]

This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy. [ABSTRACT FROM AUTHOR]

Sudden cardiac arrest (SCA) prediction using electrocardiogram (ECG) and heart rate variability (HRV) signals has received the attention of researchers in recent years. Ventricular fibrillation (VF) is one of the most common identifiers for SCA. This work aims to investigate the ECG morphological feature, R peak to T-end (R–Tend), to foresee the imminent SCA 5 min before VF onset. ECG signals for SCA and Normal Controls from The MIT-BIH databases are divided into 1-min duration and is used to predict the onset of VF. Four nonlinear features, the Largest Lyapunov Exponent, Hurst exponent, sample entropy, and approximate entropy were extracted from the R–Tend beats and classified using three classifiers: support vector machine, subtractive fuzzy clustering, and neuro-fuzzy classifier. The performance of the proposed methodology confirms that the sample entropy features efficiently predict the SCA five min before VF onset using SVM classifier and produces the maximum mean classification rate of 100% compared to other classifiers. The proposed algorithm using R–Tend beats predicts the onset of SCA better than HRV signals and is computationally efficient. The proposed marker based on ECG morphological characteristics can be used as a tool to predict SCA for smarter healthcare management. [ABSTRACT FROM AUTHOR]

Irregular heartbeat results in heart diseases. Cardiac deaths are most seen across the globe. Detecting the heart problems in early stage can reduce the death rate. Electrocardiogram (ECG) is one of the most popular method for diagnosing different arrhythmias. Arrhythmia means irregular activity of heart or abnormal heart rhythm. In this paper, cardiac signal peaks P-wave, QRS complex and T-wave are detected for classifying the type of arrhythmia. These are the main components of ECG signal. P-wave is of very small duration, it is ex-plains about the atrial depolarization. The QRS complex may include combination of Q-wave, R-wave, and S-wave. But every QRS complex may not contain Q-R-S waves. It explains about ventricular depolarization. Whereas T wave is about ventricular re-polarization. S-Golay filter is used for denoising. This is used for smoothing the data which thereby, increases the precision of data without distortion of signal tendency. The patient data is collected from MIT-BIH Arrhythmia database for analysis. The simulation is done in Matlab software. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG beat classification is essential to detect life-threatening cardiac arrhythmias. The major limitations of the traditional ECG beat classification systems are the requirement of an extensive training dataset to train the model and inconsistent performance for the detection of ventricular and supraventricular ectopic (V and S) beats. To overcome these limitations, a system denoted as SpEC is proposed in this work based on Stockwell transform (ST) and two-dimensional residual network (2D-ResNet) for improvement of ECG beat classification technique with a limited amount of training data. ST, which is used to represent the ECG signal into a time-frequency domain, provides frequency invariant amplitude response and dynamic resolution. The resultant ST images are applied as input to the proposed 2D-ResNet to classify five different types of ECG beats in a patient-specific way as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed SpEC system achieved an overall accuracy (Acc) of 99.73%, sensitivity (Sen) = 98.84%, Specificity (Spe) = 99.50%, Positive predictivity (Ppr) = 98.20% on MIT-BIH arrhythmia database, and shows an overall Acc of 89.87% on real-time acquired ECG dataset with classification time of single ECG beat image = 0.2365 (s) in detecting of five arrhythmia classes. The proposed method shows better performance on both the database compared to the earlier reported state-of-art techniques. [ABSTRACT FROM AUTHOR]

Securing personal data in wearable devices is becoming a crucial necessity as wearable devices are being deployed ubiquitously, which inadvertently exposes them to more sophisticated adversarial attacks. Although authentication systems using a single-entropy source, such as fingerprint or iris, are being used widely, successful spoofing attacks have been made, which show such systems’ vulnerability. To mitigate these issues, new biometric modalities [e.g., electrocardiogram (ECG) and photoplethysmogram (PPG)], as well as multifactor authentication/security engine designs, are being investigated. In this work, we present a new smart hardware security engine that combines three different sources of entropy, ECG, heart rate variability (HRV), and SRAM-based physical unclonable function (PUF) to perform real-time authentication and generate unique/random signatures. Such hybrid signatures vary person-to-person, device-to-device, and over time, which significantly reduces the scope of an attack and enables secure personal device authentication as well as secret random key generation. The prototype chip fabricated in 65-nm LP CMOS consumes $4.04~\mu \text{W}$ at 0.6 V for real-time authentication. Compared with ECG-only authentication, the average equal error rate of multi-source authentication is reduced by $7\times $ down to 0.2375% for a 741-subject in-house ECG database. The generalization capability of the hardware was also tested by evaluating equal error rate (EER) values using other ECG databases available online. Also, 256-bit keys generated by optimally combining ECG, HRV, and PUF values fully pass nine NIST randomness tests. [ABSTRACT FROM AUTHOR]

Noninvasive sensors capable of measuring weak biopotential signals, such as electrocardiogram (ECG) and EEG, and communicating results wirelessly to a host computer are developing rapidly. Some of them utilize capacitively coupled electrodes in an attempt to place the sensor at a distance from the skin. This article demonstrated the fabrication and development of a capacitively coupled ECG electrode prototype using custom high specific capacitance electrodes and custom high-performance electronics. Two ultrathin capacitive electrodes were fabricated on a flexible polyimide substrate ($2\times2$ in) protected by a guard ring to reduce noise. The detection and amplification circuitry consisted of operational amplifiers (OpAmps) that filtered and conditioned the ECG signal. R-peaks in the ECG were readily detected and quantified using both simulated signals from ECG databases and real signals from human subjects. Heart rates and heart rate variability calculated from our monitor measurements were comparable with commercial rigid wearable sensors, including a smartwatch and an ECG monitor that uses standard clinical ionic electrodes. The prototype monitor was tested on human subjects during rest and moderate exercise and showed appropriate responses. The challenge of high-gain low-noise amplification was met by the development of highly thinned OpAmps whose operation was shown to be equivalent to commercially available rigidly packaged OpAmps, demonstrating that high-performance Si-electronics can be used to produce high-fidelity signals from weak biopotentials. [ABSTRACT FROM AUTHOR]

Continuous measurement of electrocardiogram (ECG) signal is required for detecting various cardiac abnormalities, such as arrhythmia. Wearable devices have become ubiquitous as continuous monitoring devices. Due to power and memory restrictions in wearable devices, signal acquisition may need to be done in smaller segments using compressive sensing (CS) techniques. However such acquisitions may lead to poor recovery of compressed measurements. A Kronecker-based novel recovery technique has been recently proposed to improve the recovery of compressed signals where the recovery is achieved through a single recovery of concatenated compressed signal segments. In this article, a mathematical reasoning for improvement using the Kronecker-based recovery over standard CS recovery procedure is presented. A detailed investigation of Kronecker-based recovery technique of compressed ECG signal is presented using ECG signals from MIT-BIH Arrhythmia Database. As a part of this investigation, the quality of recovery with random and deterministic sensing of ECG signals and impact of choice of sparsifying dictionaries under various compression ratios (CRs) are considered. Deterministic sensing with deterministic binary block diagonal (DBBD) matrix and discrete cosine transform (DCT) as sparsifying basis is seen to provide the best recovery for ECG signals. Kronecker-based recovery of noisy ECG signals is possible with DBBD measurement matrix. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are identified as one of the most dangerous diseases and the major causes of the death around the world. One of the most common cardiac arrhythmias that have always been a concern for cardiologists is premature ventricular contractions. Regarding its abundance among all ages, prediction and diagnosis of this type of arrhythmia has particular importance. One of the most common, most non-invasive; and the least costly method for investigation of heart diseases is to record and analyze the electrocardiogram (ECG) signals. The purpose of this study is to analyze the ECG in order to classify premature ventricular contraction heartbeats. Having proposed a new technique based on evolutionary optimization for R peak detection, several methodologies, such as morphological assessment, polynomial curve fitting, discrete wavelet transform, and nonlinear analysis, are employed to extract features from ECG signal. Support vector machine (SVM) classifier with a linear kernel is used to detect the normal and PVC heart rates. In order to evaluate the proposed method, in addition to the MIT-BIH database, the experimental data is used and the methodology performance is proved for both databases. Finally, using different feature selection criteria such as fisher distinction, minimal-redundancy maximum-relevance, and SVM-based recursive feature elimination with correlation bias reduction, six features are introduced as best ones. The proposed PVC detection algorithm acquires the overall detection accuracy of 99.78%, with the sensitivity of 99.91% and specificity of 99.37%, for MIT-BIH dataset. [ABSTRACT FROM AUTHOR]

This review is focusing on the understanding of various factors and components governing and controlling the occurrence of ventricular arrhythmias including (i) the role of various ion channel-related changes in the action potential (AP), (ii) electrocardiograms (ECGs), (iii) some important arrhythmogenic mediators of reperfusion, and pharmacological approaches to their attenuation. The transmembrane potential in myocardial cells is depending on the cellular concentrations of several ions including sodium, calcium, and potassium on both sides of the cell membrane and active or inactive stages of ion channels. The movements of Na+, K+, and Ca2+ via cell membranes produce various currents that provoke AP, determining the cardiac cycle and heart function. A specific channel has its own type of gate, and it is opening and closing under specific transmembrane voltage, ionic, or metabolic conditions. APs of sinoatrial (SA) node, atrioventricular (AV) node, and Purkinje cells determine the pacemaker activity (depolarization phase 4) of the heart, leading to the surface manifestation, registration, and evaluation of ECG waves in both animal models and humans. AP and ECG changes are key factors in arrhythmogenesis, and the analysis of these changes serve for the clarification of the mechanisms of antiarrhythmic drugs. The classification of antiarrhythmic drugs may be based on their electrophysiological properties emphasizing the connection between basic electrophysiological activities and antiarrhythmic properties. The review also summarizes some important mechanisms of ventricular arrhythmias in the ischemic/reperfused myocardium and permits an assessment of antiarrhythmic potential of drugs used for pharmacotherapy under experimental and clinical conditions. [ABSTRACT FROM AUTHOR]

In long-term electrocardiogram (ECG) recording for arrhythmia monitoring, using a uniform compression strategy throughout the entire data to achieve high compression efficiency may result in unacceptable distortion of abnormal beats. The presented work addressed a solution to this problem, rarely discussed in published research. A support vector machine (SVM)-based binary classifier was implemented to identify the abnormal beats, achieving a classifier sensitivity (SE) and negative predictive value (NPV) of 99.89% and 0.003%, respectively with 34 records from MIT-BIH Arrhythmia database (mitdb). A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio (CR) of ECG data. A wavelet-based compression for the abnormal beats was implemented, while the consecutive normal beats were compressed in groups using a hybrid encoder, employing a combination of wavelet and principal component analysis. Finally, a neural network-based intelligent model was used, which was offline tuned by a particle swarm optimization (PSO) technique, to allocate optimal quantization level of transform domain coefficients generated from the hybrid encoder. The proposed technique was evaluated with four types of morphology tags, “A,” “F,” “L,” and “V,” from mitdb database, achieving less than 2% PRDN and less than 1% in two diagnostic distortion measures for abnormal beats. Overall, an average CR of 19.78 and PRDN of 3.34% was obtained. A useful outcome of the proposed technique is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is relatively easy to acquire and has been used for reliable biometric authentication. Despite growing interest in ECG authentication, there are still two main problems that need to be tackled, i.e., the accuracy and processing speed. Therefore, this paper proposed a fast and accurate ECG authentication utilizing only two stages, i.e., ECG beat detection and classification. By minimizing time-consuming ECG signal pre-processing and feature extraction, our proposed two-stage algorithm can authenticate the ECG signal around 660 μs. Hamilton's method was used for ECG beat detection, while the Residual Depthwise Separable Convolutional Neural Network (RDSCNN) algorithm was used for classification. It was found that between six and eight ECG beats were required for authentication of different databases. Results showed that our proposed algorithm achieved 100% accuracy when evaluated with 48 patients in the MIT-BIH database and 90 people in the ECG ID database. These results showed that our proposed algorithm outperformed other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel deep learning-based algorithm that integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for electrocardiogram (ECG) arrhythmias classification. The LSTM-based AE network (LSTM-AE) is used to learn the features from ECG arrhythmias signals, and the SVM is used to classify those signals from the learned features. The LSTM-AE consists of an encoder model, which extracts high-level feature information from ECG arrhythmias signals through LSTM network, and a decoder model which outputs reconstruct ECG arrhythmias signals from high-level features through LSTM network. Experiments show that the proposed method can learn better features than the traditional method without any prior knowledge, presenting a good potential for the ECG arrhythmias classification. In the classification of five heartbeats types, including normal, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complexes (APC), premature ventricular contractions (PVC), the proposed method achieved average accuracy, sensitivity, and specificity of 99.74%, 99.35%, and 99.84%, respectively, in the beat-based cross-validation approach, and 85.20%, 62.99%, and 90.75%, respectively, in the record-based cross-validation approach, in public MIT-BIH Arrhythmia Database. While based on the Advancement of Medical Instrumentation (AAMI) standards, the proposed method achieved average accuracy, sensitivity, and specificity of 99.45%, 98.63%, and 99.66%, respectively, in the beat-based cross-validation approach. [ABSTRACT FROM AUTHOR]

Clinically, electrocardiogram (ECG) is a powerful tool for determining the health and functioning of the human heart. Faster detection and diagnosis of heart functioning would aid cardiologists to provide appropriate treatment to the patients (subjects). In this paper, the concept of fractional-order calculus is employed for noise cancellation and artifacts removal in ECG signal as fractional-order differentiator proved to provide more peculiar details about signals than an integer-order differentiator. In the proposed method, R-peaks are detected using Riesz fractional-order digital differentiator (RFODD) based on the differencing method. The differentiation operation enhances the high-frequency components of the signal. So, QRS complex which is a high-frequency component in ECG is accentuated and the R-peaks are detected using appropriate threshold technique. The proposed method is tested on Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, and the experimental results of the proposed method have achieved a sensitivity of 99.95%, positive predictivity of 99.949% and an error rate of 0.095%. ECG waveforms are analyzed on various fractional orders of RFODD, and their performance parameters, i.e., sensitivity, positive predictivity and error rate, are also calculated. [ABSTRACT FROM AUTHOR]

With age, our blood vessels are prone to aging, which induces cardiovascular disease. As an important basis for diagnosing heart disease and evaluating heart function, the electrocardiogram (ECG) records cardiac physiological electrical activity. Abnormalities in cardiac physiological activity are directly reflected in the ECG. Thus, ECG research is conducive to heart disease diagnosis. Considering the complexity of arrhythmia detection, we present an improved convolutional neural network (CNN) model for accurate classification. Compared with the traditional machine learning methods, CNN requires no additional feature extraction steps due to the automatic feature processing layers. In this paper, an improved CNN is proposed to automatically classify the heartbeat of arrhythmia. Firstly, all the heartbeats are divided from the original signals. After segmentation, the ECG heartbeats can be inputted into the first convolutional layers. In the proposed structure, kernels with different sizes are used in each convolution layer, which takes full advantage of the features in different scales. Then a max-pooling layer followed. The outputs of the last pooling layer are merged and as the input to fully-connected layers. Our experiment is in accordance with the AAMI inter-patient standard, which included normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). For verification, the MIT arrhythmia database is introduced to confirm the accuracy of the proposed method, then, comparative experiments are conducted. The experiment demonstrates that our proposed method has high performance for arrhythmia detection, the accuracy is 99.06%. When properly trained, the proposed improved CNN model can be employed as a tool to automatically detect different kinds of arrhythmia from ECG. [ABSTRACT FROM AUTHOR]

Intensive and prolonged practice of sport can lead to cardiovascular and electrocardiographic changes. The purpose of this study was to describe the electrocardiographic changes in some young black Senegalese players practicing competitive basketball. We conducted a prospective descriptive analysis of surface electrocardiogram (ECG) findings related to young Senegalese Black players practicing competitive basketball. The study involved 40 young basketball players, 20 girls and 20 boys, whose average age was 17 ± 0.86 years (ranging from 17 to 19 years) and 15 ± 1.56 years (ranging from 13 to 18 years) respectively. Heart rate was lower among boys, 59 beats ± 9 beats (ranging from 42 to 85) than among girls 73 beats/min ± 11 beats (ranging from 50 to 95) (p = 0.0004). The following features have been observed: repolarization abnormalities such as T-wave inversion V1-V4 in 3 cases (7.5%), right ventricular hypertrophy in 1 case (2.5%), right axis deviation (QRS axis) in 1 case (2.5%). Intensive and prolonged practice of basketball leads to electrocardiographic changes in the young black Senegalese players [ABSTRACT FROM AUTHOR]

• For the first time, we conducted a study on the automatic diagnosis of pediatric ECGs in high-altitude areas. • We developed a model that combines a GA and the stacked ensemble method, capable of differentiating between normal and abnormal ECGs and classify four types of rhythm. • We incorporated not only popular ML models but also the most successful deep learning models for tabular data tasks in recent years into the baseline models, enabling us to demonstrate the advantages of the developed model. • We conducted a characteristic importance analysis, revealing important factors contributing to ECG abnormalities in children living in high-altitude regions. Electrocardiogram (ECG) is a commonly used diagnostic tool in clinical practice that plays a vital role in the diagnosis and treatment of various heart diseases. Previous studies have indicated that the incidence of heart disease in children living in high-altitude areas is significantly higher than those in low-altitude areas, which increases the demand for ECG examinations in these regions. However, there is a lack of research and technology that focus on automatic ECG diagnosis for children in high-altitude areas. This study utilized electronic medical records of pediatric electrocardiograms (ECGs) from high-altitude areas in Yunnan Province, China as research data. An improved genetic algorithm (GA) was employed to find the best combination of base classifiers, and a stacked ensemble approach was utilized to develop a reliable model for automatic diagnosis of pediatric ECGs. The developed model is capable of identifying abnormal ECGs and classifying heart rhythm types into four categories: normal sinus rhythm, sinus tachycardia, sinus bradycardia, and other arrhythmias. The model was tested and compared with commonly used classification methods. The results show that the model developed in this paper exhibits better performance in terms of accuracy, recall, and F1 score. The model's classification effectiveness was further demonstrated by creating a receiver operating characteristic (ROC) curve. Finally, feature importance analysis highlighted the significance heart rate, developmental stage, and QRS complex axis deviation angle in the model. [ABSTRACT FROM AUTHOR]