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Heart disease is a major health issue, and accurate diagnosis of irregular heartbeats and heart failure is crucial. Current diagnostic processes can be time-consuming, requiring significant effort from clinicians. An effective classifier, ADCGNet: Attention-based Dual Channel Gabor Network is proposed to address this challenge by accurately classifying anomalies. ADCGNet involves pre-processing every ECG beat into two-dimensional images using Analytical Morlet transform and then applying thirty-two Gabor filters and Sobel edge detection to enhance features. ADCGNet comprises three blocks, with the first block using dual channels to extract essential features in the images efficiently. The second block includes a multi-head attention mechanism to focus on relevant features, and the third block uses a SoftMax activation function to perform classification tasks. Extensive experiments with public datasets from PhysioNet, and comparison with several state-of-the-art classifiers indicate ADCGNet is superior. Specifically, ADCGNet achieved an accuracy of 99.17%, 98.98% in precision, a recall of 98.87%, an F1-score of 98.82% and AUC, 98.75% with optimal hyperparameters. Further, a GRAD-CAM visualization of activated areas on the test samples gives graphical insight into the performance of ADCGNet. The proposed ADCGNet classifier has promising potential for enhancing the diagnosis of heart disease, and we believe it will be of much interest to the medical community. [ABSTRACT FROM AUTHOR]

Junctional ectopic tachycardia(JET) is a rare childhood arrhythmia originating from the area adjacent to the atrioventricular(AV) node. It often occurs after surgical procedures like repair of Tetralogy of Fallot, atrioventricular septal defect and ventricular septal defect, which are all performed in that area. While AV block (AVB) can occur after JET, it is very rare for late JET occurring after early postoperative AVB to be followed by normal sinus rhythm (NSR). There is no information in the literature related to the pathophysiology of this phenomenon. In this text, we present 4 patients who developed complete AV block(CAVB) in the early postoperative period (within the first 24h) after JET in late period (>72h) and returned to NSR with first-degree AV block and then NSR during follow-up. Based on these cases, we hypothesize that there is a link between late JET after early postoperative CAVB and return to NSR. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is considered as an essential diagnostic tool to investigate life-threatening cardiac abnormalities, such as arrhythmia and congestive heart failure. It is observed that the atrial arrhythmias and congestive heart failure are closely related, wherein, one promotes the other and their co-existence can increase the mortality rate. Timely diagnosis of these diseases is essential to prevent sudden cardiac failure. In this work, we employ a two-fold approach to classify arrhythmia, congestive heart failure, and normal sinus rhythm using ECG fragments. First, we use a traditional hand-crafted feature based model which involves extraction of a number of linear and non-linear features from the ECG fragments. The linear features capture the time-varying and scale of variability information, whereas the non-linear features help to extract the hidden complexity and quantify the uncertainty of the non-stationary signal. Second, an automatic feature learning based approach is employed which uses a pre-trained deep learning network to automatically extract the relevant detailed information from the ECG time–frequency representations. Finally, we explored the combined effect of the two approaches to diagnose the arrhythmias and congestive heart failure patterns. Additionally, this study makes novel use of the subject-level ECG classification. This work on Physionet database shows that the proposed combined system gives an accuracy, sensitivity, specificity, and precision of 99.06%, 99.14%, 99.68%, and 99.32%, respectively, which are better than the state-of-the-art systems. For subject-specific experiment, a further improvement in these performance metrics is obtained using the voting procedure. • The impact of arrhythmia and congestive heart failure on mortality is discussed. • A fragment-level, subject-oriented machine learning framework is proposed. • This work ensemble the hand-crafted features and transfer learning embedding of ECG. • The highest accuracy of 99.06% is obtained using the proposed framework. • Proposed method shows improvement in performance over state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is considered as an essential diagnostic tool to investigate life-threatening cardiac abnormalities, such as arrhythmia and congestive heart failure. It is observed that the atrial arrhythmias and congestive heart failure are closely related, wherein, one promotes the other and their co-existence can increase the mortality rate. Timely diagnosis of these diseases is essential to prevent sudden cardiac failure. In this work, we employ a two-fold approach to classify arrhythmia, congestive heart failure, and normal sinus rhythm using ECG fragments. First, we use a traditional hand-crafted feature based model which involves extraction of a number of linear and non-linear features from the ECG fragments. The linear features capture the time-varying and scale of variability information, whereas the non-linear features help to extract the hidden complexity and quantify the uncertainty of the non-stationary signal. Second, an automatic feature learning based approach is employed which uses a pre-trained deep learning network to automatically extract the relevant detailed information from the ECG time–frequency representations. Finally, we explored the combined effect of the two approaches to diagnose the arrhythmias and congestive heart failure patterns. Additionally, this study makes novel use of the subject-level ECG classification. This work on Physionet database shows that the proposed combined system gives an accuracy, sensitivity, specificity, and precision of 99.06%, 99.14%, 99.68%, and 99.32%, respectively, which are better than the state-of-the-art systems. For subject-specific experiment, a further improvement in these performance metrics is obtained using the voting procedure. • The impact of arrhythmia and congestive heart failure on mortality is discussed. • A fragment-level, subject-oriented machine learning framework is proposed. • This work ensemble the hand-crafted features and transfer learning embedding of ECG. • The highest accuracy of 99.06% is obtained using the proposed framework. • Proposed method shows improvement in performance over state-of-the-art systems. [ABSTRACT FROM AUTHOR]