An Efficient ECG Classification System Using Resource-Saving Architecture and Random Forest.

Multi-stream Bi-GRU network to extract a comprehensive feature set for ECG signal classification

Different types of cardiac arrhythmias may arise in healthy subjects during sleep. Whereas bradyarrhythmias (severe sinus bradycardia, sinus arrest, first and second degree A-V blocks) predominate in the young, elderly subjects more commonly present supraventricular and ventricular ectopic beats and tachyarrhythmias (supraventricular paroxysmal tachycardia, atrial flutter, etc.). A rare potentially life-threatening syndrome, characterised by sinus arrests accompanied by vagal overactivity, has been described in young healthy adults during REM sleep. A sudden death during sleep in apparent helthy Southeast Asian immigrants in the U.S., has been described, may be related to ventricular fibrillation. The frequency of episodes of paroxysmal supraventricular tachycardia appears to follow a circadian rhythm, presenting a minimum peak between 4 and 6 a. m. and two maximal peaks in the morning and afternoon. In contrast, the likelihood for development of an episode of paroxysmal atrial fibrillation (PAF) is the same in wakefulness, slow sleep and REM sleep. Power spectral analysis of heart rate variability has established that PAF episodes are always preceded by a shift in the sympathovagal balance towards the sympathetic limb. The influence of sleep on ventricular arrhythmias (ventricular premature beats (VPB) and ventricular tachycardia (VT)) is more controversial. Most studies show that sleep exerts an inhibitory effect on ventricular arrhythmias with a possible circadian trend (the lowest incidence occurring in the early morning hours). Our polygraphic studies failed to disclose any effect of sleep on ventricular arrhythmias. Obstructive sleep apnea syndrome (OSAS) is one of the most common causes of cardiac arrhythmias during sleep. Of these, the most frequent is brady-tachyarrhythmia. First and second degree atrioventral blocks, severe bradycardia and sinus arrests lasting over 2.5 s. (but also more than 10 s.) are also common. Even though these arrhythmias are severe, it has yet to be established whether they are a direct cause of sudden death in OSAS patients. In conclusion, even though many types of arrhythmias may arise in sleep, there is no evidence that sleep is a determinant factor. Some arrhythmias with a minimal incidence in the early hours of the morning appear to be more affected by a circadian rhythm, which are at least partly sleep-dependent. The only arrhythmias truly linked to sleep are sinus arrests in REM sleep and arrhythmias linked to obstructive sleep apneas.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN)that automatically learns the best features from the raw ECG signals. After that, the support vector machine (SVM) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the SVM classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.

The heart is one of the crucial parts of the human being. The graphical recording of the cardiac cycle produced by an Electrocardiograph is called an Electrocardiogram (ECG) signal. To predict the occurrence of an arrhythmia, an electrocardiogram (ECG) is generally used by doctors to identify the condition of the patient. Hence, to accurately detect the abnormalities of the heart in advance and classify those diseases without human involvement many machine learning algorithms are used. The MIT-BIH Arrhythmia database is being used to classify the beat classification performance. This paper presents the hardware implementation of a classifier using an Artificial Neural Network (ANN) to classify four abnormalities (Normal beat, Supraventricular ectopic beat, Ventricular ectopic beat, Fusion beat) of heartbeat with high accuracy. To an appropriate input vector for the classifier, several preprocessing stages have been applied. Discrete Wavelet Transform (DWT) is used to extract the features from the ECG signal. To implement this work, Xilinx Artix-7 NESYS 4 DDR FPGA board is used. This model got 86% testing accuracy in simulation and 85.6% in hardware.

The Rhythms of Aging: What New Do We Know from Holter Monitoring?

Over the last few years, various researches have reached the conclusion that cosmic ray variations and geomagnetic disturbances are related to the condition of the human physiological state. In this study, medical data concerning the number of incidents of different types of cardiac arrhythmias for the time period 1983–1992, which refer to 1902 patients in Tbilisi, Georgia, were used. The smoothing method and the Pearson r-coefficients were used to examine the possible effect of different solar and geomagnetic activity parameters and cosmic ray intensity variations on the different types of arrhythmias. The time interval under examination was separated into two different time periods, which coincided with the polarity reversal of the solar magnetic field occurred in the years 1989–1990, and as a result, a different behavior of all the above-mentioned parameters as well as of the different types of arrhythmias was noticed during the two time intervals. In addition, changing of polarity sign of the solar magnetic field was found to affect the sign of correlation between the incidence of arrhythmias and the aforementioned parameters. The primary and secondary maxima observed in the solar parameters during the solar cycle 22, also appeared in several types of arrhythmias with a time lag of about 5 months.

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.

Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.

Cardiac arrhythmias are heartbeat disorders in which the electrical impulses that coordinate the cardiac cycle malfunction. The heart’s electrical activity is recorded using electrocardiography (ECG), a non-invasive method that helps diagnose several cardiovascular diseases. However, interpretation of ECG signals can be difficult due to the presence of noise, the irregularity of the heartbeat, and their nonstationary nature. Hence, the use of computational systems is required to support the diagnosis of cardiac arrhythmias. The main challenge in developing AI-assisted ECG systems is achieving accuracies suitable for application in clinical settings. Therefore, this paper introduces a software tool for classifying cardiac arrhythmias in ECG recordings that uses filtering, segmentation, and feature extraction of the QRS interval. We use the MIT-BIH Arrhythmia Database, which has 48 records of five different types of arrhythmias. We evaluate the data using supervised machine learning techniques such as k-Nearest Neighbors (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and the Naive Bayesian classifier. This paper shows the impact of selecting and employing filtering and feature extraction methods on the performance of supervised machine learning algorithms compared with benchmark approaches.KeywordsElectrocardiogram (ECG)Cardiac arrhythmiaFeature extractionSupervised machine learningPerformance measures

Funding Acknowledgements Type of funding sources: None. Introduction Heart involvement in systemic sclerosis patients is frequent and represents a negative prognostic factor with around 25% of SSc patients dying from cardiovascular related causes. Cardiac arrhythmias represent 6% of the overall causes of death in SSc patients. Not only a plethora of conditions typical of the disease seem to favour the presence of an arrhythmic substrate such as microvascular disease or fibrosis but autoimmunity itself has been recognized as a pathogenic mechanism for cardiac arrhythmias. Purpose The aim of our study was to investigate the incidence of arrhythmias in patients with systemic sclerosis and identify potential predictors. Methods Prospective longitudinal study enrolling all consecutive patients with a diagnosis of SSc and no overt cardiac disease nor pulmonary hypertension. Echocardiographic parameters and GLS were obtained at baseline and at each follow up as well as 12 lead ECG. Presence of atrial fibrillation (AF), atrial tachycardia (AT), ventricular ectopic beats >1000/24 h (VEB), supraventricular ectopic beats (SVEB), bundle branch block (BBB) and atrioventricular block (AVB) was registered. Results 160 patients (144 females, 90%, mean age 59±14 years) were enrolled from June 2016 to December 2021. At enrolment, 11.3% of patients with SSc presented a previous history of supraventricular arrhythmia (5.6% supraventricular ectopic beats, 3.1% atrial fibrillation, 2.5% atrial flutter) and 5.6% a history of ventricular arrhythmia (5.0% ventricular ectopic beats, 0.6% ventricular tachycardia). After a median follow-up of 3.1 years (95% CI 1.4-4.8 years), five patients (3.1%) died of cardiovascular causes, of which three (1.8%) experienced a sudden cardiac death. During the same period, 16 patients (10%) presented a new diagnosis of supraventricular arrythmia (8.7% atrial fibrillation, 1.3% atrial flutter) and two patients (1.2%) a new diagnosis of ventricular tachycardia. PR interval significantly increased during the 3.1-year follow-up (154±27 vs. 178±32 ms; p=0.013), as well as corrected QT interval (420±23 vs. 436±19 ms; p=0.001). New diagnosis of complete right and left bundle branch block was made in 18 (11.2%) and two (1.2%) of all SSc patients. Diagnosis of pulmonary arterial hypertension was associated with a 4-fold and a 10-fold increased risk of supraventricular and ventricular arrhythmias, respectively (Figure 1). A diffuse SSc variant (p=0.049), indexed right (p=0.020) and left atrial volumes (p=0.035), and E/E’ ratios (p=0.016) were all associated with an increased risk of supraventricular arrhythmias. TAPSE (p=0.040), as well as right (p=0.030) global longitudinal strain were associated with an increased risk of ventricular arrhythmias. Conclusions Ssc patients are often by an arrhythmic profile with an increased risk of supraventricular and ventricular events as well as sudden cardiac death. A comprehensive cardiological work up may help in lowering the risk of arrhythmic complications.

Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters

Detection and treatment of arrhythmias has become one of the main goals in cardiac care diagnosis provided by general practitioners. Electrocardiogram (ECG) analysis is one of the most commonly used tools to test and diagnose heart problems. Classification of ECG heartbeats enables the identification of specific arrhythmia or other heart conditions. This paper presents and contrasts the results from two effective ECG arrhythmia classification schemes. The first scheme consists of a principal component analysis (PCA) step for feature reduction at the input vector to the classifier, combined with soft independent modelling of class analogy (SIMCA). The second method uses a multi-class support vector machine (MSVM) classifier to differentiate between four different types of arrhythmia from ECG beats. The four types of beats include Normal (N), Premature Ventricular Contraction (PVC), and Atrial premature contraction (APC) and Right Bundle Branch Block Beat (RBBB). The time domain features were obtained from the St Petersburg INCART 12-lead Arrhythmia Database (incartdb). Between 10 and 30 Principal Components (PCs) were selected for reconstructing individual ECG beats and create the input vector to the classifier. The average classification accuracy of the proposed scheme is 76.83% and 98.33% using MSVM and SIMCA classifier respectively. The SIMCA classification algorithm provided better performance than the MSVM classifiers.

Computer-aided detection and diagnosis in ECG signals for heart diseases are gaining increasing attention. However, developing and selecting the highly performing diagnostic model suitable for clinical implications is still challenging. In this paper, we proposed a combined network of convolutional neural network (CNN) and Recurrent Neural Network (RNN), designed for the classification of ECG heart signals for diagnostic purpose. The proposed network consists of 2 convolutional layers with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> kernels and ReLU activations, followed by 4 residual blocks, 2 bidirectional long short-term memory (biLSTM) layers, as well as 2 fully connected layers. Each residual block involved the structure of a Squeeze-and-Excitation Network (SENet) with lightweight property to recalibrate the feature map of the network. The last dense layer has 5 outputs, equivalent to the classes considered: non-ectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. To train and evaluate the combined CNN and RNN, we transferred the knowledge acquired on beat classification tasks in 2017 PhysioNet/CinC Challenge to that in PhysionNet’s MIT-BIH dataset. The developed network achieved a recognition sensitivity of 95.90%, accuracy = 95.90% and specificity = 96.34% with classification time of single sample = 6.23 s in detecting 5 ECG classes. A comparative analysis proved the high performance of the proposed combined CNN and RNN against previous methods, demonstrating the potential of our proposed network in the analysis of beat patterns. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate cardiac health with the highest precision.

A previously reported method for electrocardiographic (ECG) telemetry in water using frequency-modulated current was improved to obtain more stable ECGs. The ECGs of seven healthy men were monitored using the improved method during and after whole-body submersion or underwater swimming. Bradycardia and arrhythmias were observed during the submersion, and transient tachycardia was detected after the start of underwater swimming, followed by bradycardia with arrhythmias. Three different types of arrhythmias were observed: sinus arrhythmia (SA), supraventricular extrasystole (SE) and ventricular extrasystole (VE). SA and SE tended to develop during the latter half of the period of submersion or underwater swimming, and especially after the restart of breathing. VEs were detected in only one subject during submersion, whereas they occurred in most subjects during and after underwater swimming. Individual variations were found in development of arrhythmias, one subject showing no arrhythmia. Bradycardia, SA and SE could depend on vagal suppression in underwater conditions, and VE may be related to the effect of muscular movement on cardiac function in addition to vagal inhibition.

An automated patient-specific ECG beat classification using LSTM-based recurrent neural networks