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Background 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. Objective 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. Methods 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. Results 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.

OBJECTIVE Based on cybernetics, a large system can be divided into subsystems, and the stability of each can determine the overall properties of the system. However, this stability analysis perspective has not yet been employed in electrocardiogram (ECG) signals. This is the first study to attempt to evaluate whether the stability of decomposed ECG subsystems can be analyzed in order to effectively investigate the overall performance of ECG signals, and aid in disease diagnosis. METHODS We used seven different cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block, dysrhythmia, hypertrophy, myocarditis, and valvular heart disease) to illustrate our method. Dynamic mode decomposition (DMD) was first used to decompose ECG signals into dynamic modes (DMs) which can be regarded as ECG subsystems. Then, the features related to the DMs stabilities were extracted, and nine common classifiers were implemented for classification of these pathologies. RESULTS Most features were significant for differentiating the above-mentioned groups (value

Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.

Monitoring of cardiac signals is very important for patients with heart disease. The detection of the ECG signal that is carried out for twenty hours will help the doctor to diagnose heart disease. The purpose of this study was to develop a portable ECG monitoring system and cost as it is called a Holter monitor. The main design of ECG module consists of the AD8232, DS3231 RTC module, Arduino microcontroller, and SD card memory. ECG signals are collected from the body of a standard measurement based LEAD II .. To record the raw data from the ECG signal, SD card memory is used to store data for further data analysis. Calibration is performed using a phantom ECG. This is done to make the design results are in accordance with the standard ECG machine.

Electrocardiogram (ECG) is widely used in Myocardial infarction (MI) diagnosis. The automatic diagnosis of MI based on the 12-lead ECG needs to consider not only the waveform change features in multi-resolution time series, but also the spatial correlation information between the leads. To this end, this work proposed multiscale spatiotemporal feature extraction method based on Convolutional neural network (CNN) for MI automatic diagnosis. First, the 12-lead ECG is first transformed into an ECG image through wavelet decomposition and 3-dimensional space reconstruction. The MI-CNN model is then constructed to identify MI using 41368 ECG images. Finally, we develop the LL-CNN model, which is utilized only after the ECG signal is identified as an MI event by the MI-CNN model, to localize MI by employing transfer learning to overcome the limited data problem. The proposed method has achieved an accuracy of 99.51% on MI detection, and a macro-F1 of 99.14% on MI localization. Moreover, the features visualization shows that U-wave has significant diagnostic value for MI. The proposed method significantly improves the performance of MI detection and localization compared with other methods. It is promising to be used for MI monitoring and diagnosis.

Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems.

Anxiety is a complex emotion characterized by an unpleasant feeling of tension when people anticipate a threat or negative consequence. It is regarded as a comprehensive reflection of human thought processes, physiological arousal, and external stimuli. The actual state of emotion can be represented objectively by human physiological signals. This study aims to analyze the differences of ECG (electrocardiogram) characteristics for various types of drivers under anxiety. We used several methods to induce drivers’ mood states (calm and anxiety) and then conducted the real and virtual driving experiments to collect driver’s ECG signal data. Physiological changes in ECG during the experiments were recorded using the PSYLAB software. The independent sample t-test analysis was conducted to determine if there are significant differences in ECG characteristics for different types of drivers in anxious state during driving. The results show that there are significant differences in ECG signal characteristics of drivers by gender, age, and driving experience, in time domain, frequency domain, and waveform under anxiety. Our findings of this study contribute to the development of more intelligent and personalized driver warning system, which could improve road traffic safety.

In this article, we propose a health monitoring system, especially focused on detecting myocardial infarction chances and raising the alarm when required. The device would be able to identify malfunctioning of the heart using the ECG signals taken using ARDUINO-based portable and affordable sensors. Here, we have described the development of two major schemes of health monitoring. We have developed a method of identifying the maloperation of the heart by using the second lead of the ECG signal. This method is based on a modified Poincare analysis, which effectively distinguishes the diseased heart ECG from the normal heart ECG signals. The Poincare-based scheme is tested using five different diseased ECG signals from the PTB database, and a high disease identification efficiency of 82% is achieved. Here we further propose implementing this analysis within the ARDUINO-based health monitoring board so that patients would get an opportunity to monitor their heart condition at some regular intervals staying at home and follow up with a physician if any abnormality is detected. This entire scheme is more useful, especially under this COVID 19, pandemic situation when moving outside the home is a challenge in itself. Hence, the proposed device would be helpful during this global crisis, especially for people residing in rural and semi-urban regions. © 2021 by the authors.

The most popular defects that infect new born babies have close connection with heart. Approximately 1% percent of new born babies suffering from defects that are caused by heart. By observing Electrocardiogram "ECG" during gestation doctors can study the fetal heart activity that collected from mother abdominal and rectify number of defects which has been diagnosed in fetal heart. Many techniques like filters, BSS and even artificial intelligence used to extract and process fetal ECG signal. In case of twins gestation the problem would be more complicated because the recorded signal is a mixture of multi signals which are mother ECG, fetuses ECG and noises where every signal comes from different source in addition fetus have the same ECG signal features as well. In this study the concentration would be on BSS techniques specifically on Stone BSS method. A comparison has been made between Stone method and two other methods "EFICA and JADE". The results proved that Stone method has better performance comparing to the other BSS techniques

Today, while people’s satisfaction with materials is high, the pursuit of health has begun and sports are becoming increasingly important. Volleyball is a good physical and mental exercise, which helps improve the health of the body. However, excessive exercise usually leads to muscle strain and more serious accidents. Therefore, how to effectively prevent excessive fatigue and sports injuries becomes more and more important. In the past, some methods of exercise fatigue detection were mostly self-assessment through some indicators, which lacked real-time and accuracy. With the advancement of smart technology, in order to better detect sports fatigue, smart wearable technology and equipment are used in volleyball. Firstly, surface electromyography signals (sEMG) are collected through wearable technology and equipment. Secondly, the signal is preprocessed to extract features that are conducive to exercise fatigue assessment. Finally, a motion fatigue detection algorithm is designed to identify and classify features and evaluate the motion status in real-time. The simulation results show that it is feasible to collect ECG signals and EMG signals to detect exercise fatigue. The algorithm has good recognition performance, can evaluate exercise conditions in real-time, and prevent fatigue and injury during exercise.

The measurement of heart rate variability (HRV) is the preferred method for assessing the function of the autonomic nervous system (ANS). Traditional HRV detection requires an electrocardiogram (ECG) or photoelectric sensor. In this paper, we propose a new method for HRV measurement using a rear smartphone camera as a sensor. Video signals from the fingertips of 24 college students were acquired using the rear camera of an HTC M8d smartphone. ECG signals were simultaneously recorded as reference. The video signals were converted into single-frame image sequences over time. Each image frame was transformed into point data through superpositioning of pixel color attribute values and averaging according to space. The point data were sorted by time to obtain a photoplethysmogram (PPG). Finally, the Hilbert transform was used to extract the pulse-to-pulse interval and the R-to-R interval for the PPG and ECG, respectively. Sixteen HRV parameters (mean HR, max HR, min HR, SDNN, RMSSD, NN50, pNN50, VLF, LF, HF, TP, LFnu, HFnu, LF/HF, SD1, and SD2) were analyzed. All 16 HRV parameters were highly correlated (all rs > 0.95, ps

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.

Background Catheter ablation is a standard therapy for frequent premature ventricular complex (PVCs). Predicting their origin from a 12-lead electrocardiogram (ECG) is crucial but it requires specialized knowledge and experience. Objective The objective of the present study was to develop and evaluate machine learning algorithms that predicted PVC origins from an ECG. Methods We developed the algorithms utilizing a support vector machine (SVM) and a convolutional neural network (CNN). The training, validating, and testing data consisted of 116 PVCs from 111 patients who underwent catheter ablation. The ECG signals were labeled with the PVC origin, which was confirmed using a 3-dimensional electroanatomical mapping system. We classified the origins into 4 groups: right or left, outflow tract, or other sites. We trained and evaluated the model performance. The testing datasets were also evaluated by board-certified electrophysiologists and an existing classification algorithm. We also developed binary classification models that predicted whether the origin was on the right or left side of the heart. Results The weighted accuracies of the 4-class classification were as follows: SVM 0.85, CNN 0.80, electrophysiologists 0.73, and existing algorithm 0.86. The precision, recall, and F1 in the machine learning models marked better than physicians and comparable to the existing algorithm. The SVM model scored among the best accuracy in the binary classification (the accuracies were 0.94, 0.87, 0.79, and 0.90, respectively). Conclusion Artificial intelligence–enabled algorithms that predict the origin of PVCs achieved superior accuracy compared to the electrophysiologists and comparable accuracy to the existing algorithm.

The disease that causes the largest number of deaths in Indonesia to date is acute coronary syndrome or called the silence killer. Deaths due to coronary heart disease (CHD) reached 26.4%. Therefore, to prevent the high death rate from CHD, early detection of CHD can be done. One way that can be done is checking the ECG (electrocardiogram / electrocardiograph) record. Then by using the HRV (Heart Rate Variability) method, which is to analyze the ECG signal which can take measurements, physiological interpretation, and clinical use of the ECG signal so that it can find out an arrhythmia (a condition in which the heart beats erratically) a person's heart and take medical steps right. In this article using wavelet transform analysis to identify heart disease. The results are then compared with the fractal dimensions of the heart rate signal. FD identifies heart disease with more than 90% confidence Keywords : Fractal, Heart, Wavelet.

Interrupted blood flow to regions of the heart causes damage to heart muscles, resulting in myocardial infarction (MI). MI is a major source of death worldwide. Accurate and timely detection of MI facilitates initiation of emergency revascularization in acute MI and early secondary prevention therapy in established MI. In both acute and ambulatory settings, the electrocardiogram (ECG) is a standard data type for diagnosis. ECG abnormalities associated with MI can be subtle, and may escape detection upon clinical reading. Experience and training are required to visually extract salient information present in the ECG signals. This process of characterization is manually intensive, and prone to intra-and inter-observer-variability. The clinical problem can be posed as one of diagnostic classification of MI versus no MI on the ECG, which is amenable to computational solutions. Computer Aided Diagnosis (CAD) systems are designed to be automated, rapid, efficient, and ultimately cost-effective systems that can be employed to detect ECG abnormalities associated with MI. In this work, ECGs from 200 subjects were analyzed (52 normal and 148 MI). The proposed methodology involves pre-processing of signals and subsequent detection of R peaks using the Pan-Tompkins algorithm. Nonlinear features were extracted. The extracted features were ranked based on Student’s t-test and input to k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), and Decision Tree (DT) classifiers for distinguishing normal versus MI classes. This method yielded the highest accuracy 97.96%, sensitivity 98.89%, and specificity 93.80% using the SVM classifier.

The detection and delineation of QRS-complexes and T-waves in electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and discrete wavelet transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the modulus-maxima analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.

Many heart diseases can be identified and cured at an early stage by studying the changes in the features of electrocardiogram (ECG) signal. Myocardial Infarction (MI) is the serious cause of death worldwide. If MI can be detected early, the death rate will reduce. In this paper, an algorithm to detect MI in an ECG signal using Daubechies wavelet transform technique is developed. The ECG signal-denoising is performed by removing the corresponding wavelet coefficients at higher scale. After denoising, an important step towards identifying an arrhythmia is the feature extraction from the ECG. Feature extraction is carried out to detect the R peaks of the ECG signal. Since as R peak is having the highest amplitude, and therefore it is detected in the first round, subsequently location of other peaks are determined. Having completed the preprocessing and the feature extraction the MI is detected from the ECG based on inverted T wave logic and ST segment elevation. The algorithm was evaluated using MIT-BIH database and European database satisfactorily.

Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow or too fast. It occurs due to improper electrical impulses that coordinates the heart beats. Sudden cardiac death may occurs due to some dangerous arrhythmias conditions. Hence the main objective of the electrocardiogram (ECG) analysis is to detect the life-threatening arrhythmias accurately for appropriate treatment in order to save life. Since the last decades, several methods were reported for automatic ECG beat classifications. In this work, we present a systematic review of the current state-of-the-art methods used to detect cardiac arrhythmia using on ECG signals. It includes the signal decomposition, feature extraction and machine learning approaches used for automatic detection and decision making process. The articles covers the pre-processing, detection of QRS complex, feature extraction and classification of ECG beats. Based on the past studies, it is understood that the automated approach using computer-aided decision making process is highly required for real-time detection of cardiac arrhythmias. The advantages and limitations of different methods are discussed and also the future scopes is highlighted in the process of effective detection of cardiac arrhythmias. This study could be beneficial for researchers to analyze the existing state-of-art techniques used in detection of arrhythmia conditions.

Myocardial infarction (MI) is a lethal heart condition that occurs due to the lack of blood flow to the heart tissues. Based on the time from symptoms onset, it is categorized into three severity stages: early MI (EMI), acute MI (AMI), and chronic MI (CMI). Electrocardiogram (ECG) signals are often used to diagnose MI with pathological changes in its characteristics. In clinical practice, accurate diagnosis and risk-stratification are essential to optimize various treatment strategies, hence clinical outcome. However, most automated methods focus only on identifying MI patients from healthy controls (HC). Therefore, in this paper, we propose a novel multi-lead diagnostic attention-based recurrent neural network (MLDA-RNN) for automated diagnosis of the three MI severity stages from HC subjects. The method systematically processes the 12-lead ECGs to capture the multi-scale temporal dependencies from each ECG leads for improved classification. Specifically, we first employ the RNNs to encode the temporal variations in the 12-lead ECG signals. These encoded vectors are fed to the intra-lead attention module to summarize the within-lead discriminative vectors to obtain lead-attentive representations. Then, the inter-lead attention module aggregates these representative vectors based on their clinical relevance to obtain a high-level feature representation for a reliable diagnosis. Using 12-lead ECGs from the PTBDB and STAFF III datasets, we achieved an overall accuracy of 97.79% without compromising on the class-wise detection rates. With improved performance, the MLDA-RNN also shows promising results for model interpretability as the learned attention weights often correlate with clinicians' way of diagnosing MI severity stages.

Introduction Scuba diving likely has an impact on the autonomic nervous system (ANS). In the course of conducting trials of underwater ECG recording for measurement of heart rate variability, there was an unexpected stressful event; one participant's regulator iced and began to free-flow. Methods A custom-made, water- and pressure-tight aluminum housing was used to protect a portable Holter monitor. ECGs were recorded in three experienced divers who witnessed an unplanned moderately stressful incident during diving. The ECG signals were analysed for measures of heart rate variability (HRV). Results Analysis for different short-term HRV measures provided consistent results if periods of interest were appropriately time-aligned. There was improvement in sympatho-vagal balance. One diver unexpectedly exhibited an increase in both sympathetic and vagal activity shortly after the incident. Conclusions A conventional open-water dive affected the ANS of experienced recreational divers as measured by HRV which provides a global evaluation of the ANS and alterations in its two branches. The heart rate variability data gathered from several participating divers around the time of this event illustrate the potential utility of this variable in quantifying stress during diving. HRV data may be useful in addressing relevant diving related questions such as effects of cold, exercise or different breathing gases on ANS function.

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.

Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias.

Physical fitness indicates favorable statuses of well-being and health and, more specifically, the ability to perform sports and daily activities. In the paper, we studied heart rate variability with electrocardiogram (ECG) signals under levels of exercise intensities for subjects with different grades of cardiopulmonary fitness. Based on the targeted maximum heart rate, three graded upright biking exercise levels were defined for the experiment: light, moderate, and vigorous. ECG signals were accessed under the rest, at three exercise intensity levels, and in their retrieval periods subsequently. The subjects were in the top 20%, middle, and bottom 20% groups of cardiopulmonary fitness norms from university freshman. Firstly, the major of heart rate variability (HRV) indices performed by spectral and time-domain analyses from collected ECG signals were explored. Statistical analysis was then conducted to identify physiological signal indices with significant differences. All appeared significant differences between the three groups in the frequency domain HRV indices, including high-frequency power (HF), total power (TP), normalized high-frequency power (nHF), normalized low-frequency power (nLF), and LF/HF. The time domain HRV parameters, meanwhile, did not exhibit significant differences among the three groups throughout the graded exercises and in the recover periods. A multiple comparison test on the TP indicated significant differences in most between-group comparisons (except for between groups middle and bottom during moderate exercise, top and middle during recovery from moderate exercise, and middle and bottom during recovery from vigorous exercise). Significant differences were found in most between-group comparisons (except for between groups middle and bottom at rest and during recovery from light and medium exercise and between top and middle during recovery from vigorous exercise).

Background: Arrhythmia is a kind of heart disorder characterized by irregular heartbeats which can be detected with Electrocardiographic (ECG) signals. Accurate and early detection along with differentiation of arrhythmias is of great importance in a clinical setting. However, visual analysis of ECG signal is a challenging and timeconsuming work. We have developed an automatic arrhythmia detection model with deep learning framework to expedite the diagnosis of arrhythmia with a high degree of accuracy. Methods: We proposed a novel automatic arrhythmia detection model utilizing a combination of 1D convolutional neural network (1D-CNN) and Gated Recurrent Unit (GRU) network for the diagnosis of five different arrhythmia on ECG signals taken from the MITBIT arrhythmia physio bank database. Results: The proposed system showed a high classification performance in handling variable length ECG signal data, achieving an accuracy rate of 99.45%, sensitivity of 98.35% and specificity of 99.21% and a F1-Score of 98.95% using a five-fold cross validation strategy. Conclusions: Combining 1D-CNN and GRU networks yielded a higher degree of accuracy compared with other deep learning networks. Our proposed arrhythmia detection method may be a powerful tool to aid clinicians in accurately detecting common arrhythmias on routine ECG screening.

Electrocardiogram (ECG) technology plays a vital role in detecting arrhythmia. Numerous achievements have been marked in ECG-related research. Most methods first pre-process ECG signals, then extract features, and finally classify them. Most of the ECG signals used in the related studies were analyzed in specific time intervals or using a fixed number of samples. However, it is not always possible to see significant changes in a short term, and the symptoms of some patients are relatively short-lived. Misjudgments are possible because the ECG signal was not accurately extracted. This study proposes a computer-aided diagnosis (CAD) system for classification of Atrial Fibrillation and Normal Sinus Rhythm based on ECG signals through convolutional neural network. The proposed system considers a single heartbeat, rather than a specific number of seconds. This study eschews the one-dimensional digital ECG signal used in previous studies and uses convolutional neural networks to analyze two-dimensional ECG image. This study explores whether two-dimensional image ECG requires signal filtering. The final classification results in filtered ECG signals is accuracy of 99.23%, sensitivity of 99.71%, and specificity of 98.66%. The best result in non-filtered ECG signals achieves accuracy of 99.18%, sensitivity of 99.31%, and specificity of 99.03%. With no cumbersome artificial settings, the results of this study are comparable to the related studies. The proposed CAD system has high generalizability; it can help doctors to diagnose diseases effectively and reduce misdiagnosis.

An Electrocardiogram (ECG) is a typical method used to detect heartbeat, and an ECG signal analysis enables the detection of some heart diseases. However, the ECG-based heartbeat detection requires device attachment, which is not preferred for daily use. A Doppler sensor could be a device used to enable the non-contact heartbeat detection. In this paper, we propose a Doppler sensor-based ECG signal reconstruction method by a hybrid deep learning model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An ECG signal can be reconstructed by relating features of a heartbeat signal obtained by a Doppler sensor to those of the ECG signal. Thus, we construct the deep learning model that extracts the spatial and temporal features from the heartbeat signal by CNN and LSTM. Based on the extracted features, the ECG signal is reconstructed. We conducted experiments to observe heartbeat against 9 healthy subjects without heart disease. The experimental results showed that our method performed ECG signal reconstruction with a correlation coefficient of 0.86 between the reconstructed and actual ECG signals, even without attaching devices. The results indicate that it is possible to remotely reconstruct an ECG signal from a heartbeat signal via a Doppler sensor.

Electrocardiography (ECG) is the depiction of the hearts recorded electrical activity, obtained by attaching electrodes that are specifically designed to place at particular points of human body. The graphically represented signal has three main parts- P wave, QRS complex and thirdly T wave together giving normal ECG wave. Deviations in the normal graphical representation of ECG indicate types of cardiac disorders. An automatic system which involves MATLAB-based tool is being used to simulate synthetic ECG signals of cardiac pathology. The model of the pathological signals uses the real time clinical data of the pathologies and it is done with the help of Fourier series by mathematical modeling the ECG signal wave. This model is a useful tool that allows comparison with patient’s current ECG record collected by regular methods. Thus helps in the detection of pathologies that lead to serious cardiac arrhythmia.

This article presents a study on the efficiency of implementing classifiers for the detection of sleep apnea moments based on a minute-to-minute Electrocardiogram (ECG) signal, detailing the comparison of the accuracy for different classifiers. At each ECG signal, a Sgolay filter was applied to extract the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR) and they were used for the training, testing and validation of the classifiers. The same features were extended in a second phase in order to understand if all the classified features were important. According to the results obtained, the best accuracy was 82.12%, with a sensitivity and a specificity of 88.41% and 72.29%, respectively. This study shows the importance of choosing the right classifier for a specific problem as well as choosing and using the best features for a better accuracy. These promising early-stage results may lead to complementary studies to improve the classifiers for a possible real-world application. The performance of the proposed model was compared with other approaches used for the detection of sleep apnea.

In normal ECG, signals are not identify that patient suffering from what type of disease related to sleep apnea. An electrocardiogram (EKG or ECG) is a test that checks for issues with the electrical movement of your heart. An EKG demonstrates the heart’s electrical action as line tracings on paper. and whatever is left of the body. The aim of this project is to find the correct heart rate, oxygen levels in the blood and breathing patterns of newborn who is suffering from sleep apnea syndrome based on some data. As discussed in the literature review section, a healthy heart rate should be strong and between 120 and 160 beats per minute; anything higher and lower could be sign of troubles in newborns. With the help of electronic monitoring system either external or internal, this provides a continuous recording of newborn’s heart rate

A significant percent of all global deaths are caused by cardiovascular diseases (CVD). The diagnostic of the electrocardiogram (ECG) is a clinical practice widely adopted to evaluate the heart condition and identify CVD. For long-term ECG monitoring, a biopotential acquisition system integrated in common clothing is a viable solution for telemedicine. The electrodes and wires play a major role in the comfort and signal quality acquired from the patient. The paper presents a technical solution, where stainless steel yarn was used to create a Lead I Einthoven system consisting of 3 dry electrodes embroidered on a sports shirt. There are novel electrode materials and techniques that push further the state-of-the-art in ECG acquisition, but the authors focused on the currently available materials that are low-cost, widely available and easily integrable into common clothing, in order to seek a simple yet fully functional solution with the potential to become a truly ubiquitous ECG monitoring system.

Sleep apnea (SA) is a common sleep disorder that is not easy to detect among several patients. Recent studies have highlighted ECG analysis as a method of diagnosing SA. Because the changes caused by SA on the ECG are so subtle, the need for new methods in diagnosing the disease is required more than ever. Machine Learning (ML) techniques are recognized as one of the most successful methods of computer aided diagnosis. ML uses new methods to diagnose diseases using past clinical results. The purpose of this study is to evaluate studies using ML algorithms and based on ECG characteristics to evaluate people with SA patients. In this study, English articles written in English and indexed in PubMed, Scopus, Web of Science, and IEEE databases, were searched with no lower time limit and until October 2020, and were systematically reviewed. Finally, 48 articles were approved for review in this study. In Within the selected articles, different ML methods were used adopted for classification. All of these studies were binary, and SA was detected from the normal state based on a full ECG stripe (per record), or based on one-minute segments (per segment). Our studies analyses showed show that the most common features used in the studies were frequency, time series and statistical features. Support-Vector Machine (SVM) and deep learning-based neural network (i.e. CNN, DNN) performed best in full record data detection. The highest accuracy, sensitivity and specificity reported between the selected studies were 100%, which was obtained by an SVM. In another case study, the classification was conducted based on ECG segments, and accordingly, the highest classification accuracy was observed in the residual neural network algorithm (RNN). The accuracy, sensitivity and specificity of this algorithm were reported to be 99%. In general, it can be stated that ML techniques based on ECG characteristics have a high capability in diagnosing SA. This can increase the diagnosis of patients with SA or the detection of SA episodes on ECG record, and can potentially prevent complications of the disease at later stages.

Cardiovascular diseases are the leading cause of lethal issues worldwide. The most effective way to combat these diseases is the real-time monitoring of the electrocardiogram (ECG) that reflects the electrical signals generated by the heart cells. So far, the ECG is recorded by using the cutaneous conventional medical electrodes (Ag/AgCl). These electrodes are not adapted for long-term use as they are not fully integrated into underwear and generally can provoke skin irritation, because of the ionic gel that serves to reduce the impedance of the electrode/ skin interface. The first part of this paper concerns the design and realization by embroidery of three different patterns of conductive tracks that could be adopted as connection lines between an electronic module and ECG electrodes. In order to make these conductive tracks washable, two encapsulation methods have been investigated. The reliability of the developed conductive tracks and encapsulation methods has been analyzed. The second part of this study is focused on the development of washable embroidered ECG bands operating without any ionic gel as alternatives to medical electrodes (Ag/AgCl). The acquisition of ECG signals has been carried out on 3 female and 6 male healthy subjects aged between 25 and 55 years. In the last stage of this paper, we explore the ECG signal in static condition, the spectral power density of the ECG signal, the signal-to-noise ratio (SNR) of ECG electrodes before and after 50 washing cycles.

The occlusion in one of the coronary arteries of the heart leads to the cardiac ailment, myocardial infarction (MI). The localization of MI based on the investigation of the morphology of the multi-lead electrocardiogram (ECG) is the initial task for the diagnosis of this ailment. In this paper, the multiscale convolutional neural network is proposed for the automated localization of MI ailment from multi-lead electrocardiogram (ECG) beats. The Fourier-Bessel (FB) series expansion based empirical wavelet transform (EWT) with fixed order ranges is introduced for the multiscale analysis of multi-lead ECG beat. The FB spectrum of each lead ECG beat is segregated into contiguous segments using the fixed order ranges. Furthermore, the order ranges from these contiguous segments are used to design an empirical wavelet filter bank for the extraction of subband signals from each lead ECG beat. The convolutional neural network (CNN) is used for the classification of various categories of MI as anterior MI (AMI), anterio-lateral MI (ALMI), anterio-septal MI (ASMI), inferior MI (IMI), inferio-lateral MI (ILMI), inferio-posterio-lateral MI (IPLMI) and normal sinus rhythm (NSR). The experimental results reveal that the lower-order range subband signal coupled with CNN attains higher average accuracy values of 99.92%, 99.34%, 99.95%, 99.95%, 99.91%, and 99.86% respectively, for AMI, ALMI, ASMI, IMI, ILMI, and IPLMI classes. The subband signal of multi-lead ECG beats with order range of [1-26] is highly affected during various categories of MI heart disease, and this band signal has higher performance as compared to the existing MI localization approaches.

Myocardial contraction is triggered whenever a wave of electrical impulses passes through the heart. The pattern of electrical excitation spreads coordinately over the structure of the heart. This results in a measurable change in potential across the surface of the body of a subject. The recording of the resultant signal from specific body points is known as an electrocardiogram (ECG). Students can directly record an ECG signal from the headset input of a smartphone with an electric lead and a suitable sound app. This allows interdisciplinary teaching by applying physics concepts to physiology.

Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and represents more than 50% of the deaths from cardiovascular diseases. Since cardiovascular problems change the features in the electrical signal of the heart, if significant changes are found with respect to a reference signal (healthy), then it is possible to indicate in advance a possible SCD occurrence. This work proposes SCD identification using Electrocardiogram (ECG) signals and a sparse representation technique. Moreover, the use of fixed feature ranking is avoided by considering a dictionary as a flexible set of features where each sparse representation could be seen as a dynamic feature extraction process. In this way, the involved features may differ within the dictionary’s margin of similarity, which is better-suited to the large number of variations that an ECG signal contains. The experiments were carried out using the ECG signals from the MIT/BIH-SCDH and the MIT/BIH-NSR databases. The results show that it is possible to achieve a detection 30 min before the SCD event occurs, reaching an an accuracy of 95.3% under the common scheme, and 80.5% under the proposed multi-class scheme, thus being suitable for detecting a SCD episode in advance.

Arrhythmia is a life-threatening disease that leads to complex physical condition in patients if left untreated. Arrhythmia disorders should be diagnosed early enough to save people's life. Noninvasive and remote monitoring of cardiac arrhythmia problems is now possible to resolve due to the Internet of Things(IoT). IoT has the potential to simplify the healthcare domain. An IoT network for cardiovascular disease prediction retrieves the ECG signals, processes them, and warns physicians in an emergency using an IoT-enabled ECG telemetry structure. It is beneficial for the physician to detect heart failure at the earliest. This research work proposes an IoT-enabled ECG monitoring structure to analyze the ECG signals. The Pan Tompkins Q-R-S detection algorithm analyzes the signal to obtain the ECG signal's complex features. To get heart rate variability features, the expedient can be used to locate R-R intervals from an ECG signal. Dynamic Features and statistical are considered to categorize cardiac arrhythmia syndrome. People can monitor their cardiac disease regularly at home through ECG signals. The system is lightweight, so it needs less maintenance and is less expensive to run. It's beneficial for the surgeon to scrutinize the heart condition efficiently and consistently.

Atrial fibrillation (AF) is a disorder related to the heart. Irregularity of RR intervals and lack of P wave are the two main indicators of AF. Detection of AF using Electrocardiogram (ECG) remains one of the real challenges in the field of medical science. In this paper, we propose Discrete Wavelet Transform based method coupled with Deep Learning methods such as 2 layer Long Short Term Memory (LSTM) along with Gradient Recurrent Unit (GRU), 2 layer Bidirectional Long Short Term Memory (BiLSTM) along with Gradient Recurrent Unit (GRU) are used separately to classify the ECG signal into 3 classes namely: Normal, AF and other rhythms. Physionet challenge 2017 dataset is used for the study purpose. The results of LSTM and BiLSTM are compared with Support Vector Machine (SVM). The result indicated that LSTM provided improved performance compared to BiLSTM and SVM methods. The class specific accuracy of normal, AF and other rhythm are 96.92%, 97.36% and 96.39% respectively and Area Under the Curve (AUC) is 0.982. The overall accuracy of LSTM network is obtained as 96.94%. The developed technology has immense applications in medical devices.

Different from the traditional methods of assessing the cardiac activities through heart rhythm statistics or P-QRS-T complexes separately, this study demonstrates their interactive effects on the power density spectrum (PDS) of ECG signal with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases. Firstly, a mathematical model of the PDS of ECG signal with a random pacing pulse train (PPT) mimicking S-A node firings was derived. Secondly, an experimental PDS analysis was performed on clinical ECG signals from 49 STEMI patients and 42 healthy subjects in PTB Diagnostic Database. It was found that besides the interactive effects which are consistent between theoretical and experimental results, the ECG PDSs of STEMI patients exhibited consistently significant power shift towards lower frequency range in ST-elevated leads in comparison with those of reference leads and leads of health subjects with the highest median frequency shift ratios at 51.39 ± 12.94% found in anterior MI. Thirdly, the results of ECG simulation with systematic changes in PPT firing statistics over various lengths of ECG data ranging from 10 s to 60 mins revealed that the mean and median frequency parameters were less affected by the heart rhythm statistics and the data length but more depended on the alterations of P-QRS-T complexes, which were further confirmed on 33 more STEMI patients in European ST-T Database, demonstrating that the frequency indexes could be potentially used as alternative indicators for STEMI diagnosis even with ultra-short-term ECG recordings suitable for wearable and mobile health applications in living-free environments.

To the best of our knowledge, there is no annotated database of PPG signals recorded by smartphone publicly available. This article introduces Brno University of Technology Smartphone PPG Database (BUT PPG) which is an original database created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology, for the purpose of evaluating photoplethysmographic (PPG) signal quality and estimation of heart rate (HR). The data comprises 48 10-second recordings of PPGs and associated electrocardiographic (ECG) signals used for determination of reference HR. The data were collected from 12 subjects (6 female, 6 male) aged between 21 and 61. PPG data were collected by smartphone Xiaomi Mi9 with sampling frequency of 30 Hz. Reference ECG signals were recorded using a mobile ECG recorder (Bittium Faros 360) with a sampling frequency of 1,000 Hz. Each PPG signal includes annotation of quality created manually by biomedical experts and reference HR. PPG signal quality is indicated binary: 1 indicates good quality for HR estimation, 0 indicates signals where HR cannot be detected reliably, and thus, these signals are unsuitable for further analysis. As the only available database containing PPG signals recorded by smartphone, BUT PPG is a unique tool for the development of smart, user-friendly, cheap, on-the-spot, self-home-monitoring of heart rate with the potential of widespread using.

With the increase in the number of accidents, driver attentiveness is becoming more and more important, and driver attentiveness detection is a technology that is needed to save lives. This research presents preliminary results on the methods of detecting driver drowsiness through the analysis of electromyography (EMG), electroencephalography (EEG) electrocardiography (ECG) signals. EMG signals will be sensing the eye muscle movements, which indicates eye blink rate. Similarly, the sensing of ECG signals monitors the heart rate while driving. A three-lead electrode system was used for measuring EMG and ECG signals while participants were in a variety of scenarios. EEG signals monitor driver drowsiness. Scenarios include a restful state, a driving simulation, and in a moving vehicle. Filtering and correlation techniques were used to process different types of signals to determine heart rates, eye blink rates and brain activities. These rates were compared with rates indicating drowsiness, whether the participant was showing signs of drowsiness.

This study proposes the detection of ventricular fibrillation (VF) by wavelet transforms (WTs) and phase space reconstruction (PSR) from electrocardiogram (ECG) signals. A neural network with weighted fuzzy memberships (NEWFM) is used to detect VF as a classifier. In the first step, the WT was used to remove noise in ECG signals. In the second step, coordinates were mapped from the wavelet coefficients by the PSR. In the final step, NEWFM used the mapped coordinates-based features as inputs. The NEWFM has the bounded sum of weighted fuzzy memberships (BSWFM) that can easily appear the distinctness between the normal sinus rhythm (NSR) and VF in the graphical characteristics. The BSWFM can easily be set up in a portable automatic external defibrillator (AED) to detect VF in an emergency.

Electrocardiogram(ECG) is one of the most frequently used modality by cardiologists across the globe to detect any heart function abnormalities. In hospitals, ECG results are printed on paper by the ECG machines, which then is analysed by an expert. This work proposes a one-dimensional convolutional neural network(CNN) framework for automated myocardial infarction (MI) detection from multi-lead ECG signals extracted from ECG images. The model is developed using PTB diagnostic database consisting of 148 ECGs of (MI) cases. The results verify the efficacy of the proposed method with accuracy, sensitivity and precision of 86.21%, 89.19%, and 91.30%, respectively. The work is also compared with other state-of-the-art approaches for MI detection using ECG images.