Interpatient Heartbeat Classification Using Modified Residual Attention Network With Two-Phase Training and Assistant Decision

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

Objective. The arrhythmia identification method based on the U-net has the potential for fast application. The RR-intervals have been proven to improve the performance of single-heartbeat identification methods. However, because both the heartbeats number and location in the input of the U-net are unfixed, the approach based on the U-net cannot use RR-intervals directly. To solve this problem, we proposed a novel method. The proposed method also can identify heartbeats of four classes, including non-ectopic (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), and fusion beat (F). Approach. Our method consists of the pre-processing and the two-stage identification framework. In the pre-processing part, we filtered input signals with a band-pass filter and created the auxiliary waveforms by RR-intervals. In the first stage of the framework, we designed a network to handle input signals and auxiliary waveforms. We proposed a masking operation to separate the input signal into two signals according to the result of the network. The first signal contains heartbeats of SVEB and VEB. The second signal includes heartbeats of N and F. The second stage consists of two networks and can further identify the heartbeats of SVEB, VEB, N, and F from these two signals. Main result. We validated our method on the MIT-BIH arrhythmia database with the inter-patient model. For classes N, SVEB, VEB, and F, our approach achieved F1 scores of 98.26, 68.61, 95.99, and 47.75, respectively. Significance. Our method not only can effectively utilize RR intervals but also can identify multiple arrhythmias.

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.

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

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

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

With the rapid development of the Internet of Things (IoT), it becomes convenient to use mobile devices to remotely monitor the physiological signals (e.g., Arrhythmia diseases) of patients with chronic diseases [e.g., cardiovascular diseases (CVDs)]. High classification accuracy of interpatient electrocardiograms is extremely important for diagnosing Arrhythmia. The Supraventricular ectopic beat (S) is especially difficult to be classified. It is often misclassified as Normal (N) or Ventricular ectopic beat (V). Class imbalance is another common and important problem in electronic health (eHealth), as abnormal samples (i.e., samples of specific diseases) are usually far less than normal samples. To solve these problems, we propose a sustainable deep learning-based heart beat classification system, called BeatClass. It contains three main components: two stacked bidirectional long short-term memory networks (Bi-LSTMs), called Rist and Morst, and a generative adversarial network (GAN), called MorphGAN. Rist first classifies the heartbeats into five common Arrhythmia classes. The heartbeats classified as S and V by Rist are further classified by Morst to improve the classification accuracy. MorphGAN is used to augment the morphological and contextual knowledge of heartbeats in infrequent classes. In the experiment, BeatClass is compared with several state-of-the-art works for interpatient arrhythmia classification. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -scores of classifying N, S, and V heartbeats are 0.6%, 16.0%, and 1.8% higher than the best baseline method. The experimental result demonstrates that taking multiple classification models to improve classification results step-by-step may significantly improve the classification performance. We also evaluate the classification sustainability of BeatClass. Based on different physical signal data sets, a trained BeatClass can be updated to classify heartbeats with different sampling rates. Finally, an engineering application indicates that BeatClass can promote the sustainable development of IoT-based eHealth.

Water immersion may cause adverse cardiovascular events, including arrhythmias in patients with damaged cardiac muscle, e.g. with cardiac failure. So far, there have been rather few reports on arrhythmia induced by water training in patients with coronary artery disease (CAD). The aim of the study was to assess the influence of exercise training in moderately cold water (28-30°C) on arrhythmia and physical capacity in stable CAD patients with preserved left ventricular (LV) function. Sixty-two post-myocardial infarction male patients, mean age 50.9 ± 7.9 years, participated in 16 water-based trainings (WBT), which lasted 55-min, twice a week in water at 28-30°C. Each subject underwent 24 h Holter on-land monitoring (Holter-24) once during the study and twice in-water Holter monitoring (Holter-W) during WBT. Before and after WBT cardiopulmonary exercise test (CPET) was performed. The following parameters were analyzed: peak oxygen consumption (peak VO2), mean number of ventricular ectopic beats (VEBs) and supraventricular ectopic beats (SVEBs) during Holter-24 and Holter-W, the percentage of men who developed arrhythmia during CPET vs. Holter-24 and vs. Holter-W. WBT significantly improved patients' physical capacity, and more often provoked arrhythmia, mainly SVEBs, than CPET or daily activity assessed during Holter-24. During WBT 58% men developed VEBs and 62% SVEBs. 1. WBT provoked arrhythmias significantly more often than did CPET and normal daily activity. 2. Owing to WBT patients improved their physical capacity which was still maintained at 1-year follow-up.

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.

FPGA-oriented HW/SW implementation of ECG beat detection and classification algorithm

An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k = 4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.

Cardiac Arrhythmias during Theophylline Toxicity: A Prospective Continuous Electrocardiographic Study

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.

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.

Predicting Post-Heart Transplant Composite Renal Outcome Risk in Adults: A Machine Learning Decision Tool