OBJECTIVE: This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data. Approach: We propose a Feature Fusion Neural Network enhanced by a Dynamic Minority-Biased Batch Weighting Loss Function. This network comprises three specialized branches: the Complete ECG Data Branch for a comprehensive view of ECG signals, the Local QRS Wave Branch for detailed features of the QRS complex, and the R Wave Information Branch to analyze R wave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network\'s learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network\'s ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification. Main Results: The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value (PPV) for Supraventricular ectopic beat were 99.63%, 93.62%, 99.81%, and 92.98%, respectively, and for Fusion beat were 99.76%, 85.56%, 99.87%, and 84.16%, respectively. Under the inter-patient paradigm, these metrics were 96.56%, 89.16%, 96.84%, and 51.99% for Supraventricular ectopic beat, and 96.10%, 77.06%, 96.25%, and 13.92% for Fusion beat, respectively. Significance: This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions. .

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.

UNASSIGNED: Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature.
UNASSIGNED: This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases.
UNASSIGNED: Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database.
UNASSIGNED: The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters.
UNASSIGNED: The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI\'s utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.

In this paper, we develop the so-called variable projection support vector machine (VP-SVM) algorithm that is a generalization of the classical SVM. In fact, the VP block serves as an automatic feature extractor to the SVM, which are trained simultaneously. We consider the primal form of the arising optimization task and investigate the use of nonlinear kernels. We show that by choosing the so-called adaptive Hermite function system as the basis of the orthogonal projections in our classification scheme, several real-world signal processing problems can be successfully solved. In particular, we test the effectiveness of our method in two case studies corresponding to anomaly detection. First, we consider the detection of abnormal peaks in accelerometer data caused by sensor malfunction. Then, we show that the proposed classification algorithm can be used to detect abnormalities in ECG data. Our experiments show that the proposed method produces comparable results to the state-of-the-art while retaining desired properties of SVM classification such as light weight architecture and interpretability. We implement the proposed method on a microcontroller and demonstrate its ability to be used for real-time applications. To further minimize computational cost, discrete orthogonal adaptive Hermite functions are introduced for the first time.

Atrial fibrillation (AFIB) and ventricular fibrillation (VFIB) are two common cardiovascular diseases that cause numerous deaths worldwide. Medical staff usually adopt long-term ECGs as a tool to diagnose AFIB and VFIB. However, since ECG changes are occasionally subtle and similar, visual observation of ECG changes is challenging. To address this issue, we proposed a multi-angle dual-channel fusion network (MDF-Net) to automatically recognize AFIB and VFIB heartbeats in this work. MDF-Net can be seen as the fusion of a task-related component analysis (TRCA)-principal component analysis (PCA) network (TRPC-Net), a canonical correlation analysis (CCA)-PCA network (CPC-Net), and the linear support vector machine-weighted softmax with average (LS-WSA) method. TRPC-Net and CPC-Net are employed to extract deep task-related and correlation features, respectively, from two-lead ECGs, by which multi-angle feature-level information fusion is realized. Since the convolution kernels of the above methods can be directly extracted through TRCA, CCA and PCA technologies, their training time is faster than that of convolutional neural networks. Finally, LS-WSA is employed to fuse the above features at the decision level, by which the classification results are obtained. In distinguishing AFIB and VFIB heartbeats, the proposed method achieved accuracies of 99.39 % and 97.17 % in intra- and inter-patient experiments, respectively. In addition, this method performed well on noisy data and extremely imbalanced data, in which abnormal heatbeats are much less than normal heartbeats. Our proposed method has the potential to be used as a diagnostic tool in the clinic.

Objective. Detecting atrial fibrillation (AF) using electrocardiogram (ECG) recordings from wearable devices has been challenging due to factors such as low signal-to-noise ratio and the use of only one lead. The use of deep learning has become a popular approach to tackle this task. However, it has been observed that current methods based on deep neural networks tend to favor raw signals as input, disregarding the valuable clinical experience in ECG diagnosis.Approach.In this study, we proposed a novel feature extraction method that generates a pseudo QRS complex signal and a pseudo T, P wave signal for each raw ECG signal using a temporal mask built upon R peak detection. Then a novel dilated residual neural network was trained on the decomposed signal.Main results.We evaluated the performance of our method on the dataset of PhysioNet/CinC 2017 Challenge, achieving an averageF1¯score of 0.843. The method was further tested on MIT-BIH Atrial Fibrillation Database, and an averageF1¯score of 0.984 was obtained.Significance.Our proposed ECG signal decomposition technique introduces simple and reliable domain knowledge into deep neural networks, and the dilated residual network provides large and flexible receptive fields, thereby enhancing the performance in the detection of AF. Our method can be extended to many other tasks involving ECG signals.

Electrocardiogram (ECG) signal analysis has become significant in recent years as cardiac arrhythmia shares a major portion of all mortality worldwide. To detect these arrhythmias, computer-assisted algorithms play a pivotal role as beat-by-beat monitoring of holter ECG signals is required. In this paper, a morphological arrhythmia classification algorithm has been proposed to classify seven different ECG beats, namely Normal Beat (N), Left Bundle Branch Block Beat (L), Right Bundle Branch Block Beat (R), Atrial Premature Contraction Beat (A), Premature Ventricular Contraction Beat (V), Fusion of Normal and Ventricle Beat (F) and Pace Beat (P). A novel feature set of 25 attributes has been extracted from each ECG beat and ranked using the Fuzzy Entropy-based feature selection (FEBFS) technique. In addition, two distinct classifiers, support vector machine with radial basis function as the kernel (SVM-RBF) and weighted K-nearest neighbor (WKNN), are used to categorize ECG beats, and their performances are also evaluated after adjusting vital parameters. The performance of classifiers is compared for four different ECG beat segmentation approaches and further analyzed using three similarity measurement techniques and two fuzzy entropy methods while feature selection. The classifier results are also cross-validated using a 10-fold cross-validation scheme, and the MIT-BIH Arrhythmia Database has been used to validate the proposed work. After selecting 21 highly ranked features, WKNN achieves the best results with the nearest neighbor value K = 3 and cityblock distance metrics, with Average Sensitivity (Sen) = 94.89%, Positive Predictivity (Ppre) = 97.13%, Specificity (Spe) = 99.72%, F1 Score = 95.95%, and Overall Accuracy (Acc) = 99.15%. The novelty of this work relies on formulating a unique feature set, including proposed symbolic features, followed by the FEBFS technique making this algorithm efficient and reliable for morphological arrhythmia classification. The above results demonstrate that the proposed algorithm performs better than many existing state-of-the-art works.

Electrocardiograms (ECG) analysis is one of the most important ways to diagnose heart disease. This paper proposes an efficient ECG classification method based on Wasserstein scalar curvature to comprehend the connection between heart disease and the mathematical characteristics of ECG. The newly proposed method converts an ECG into a point cloud on the family of Gaussian distribution, where the pathological characteristics of ECG will be extracted by the Wasserstein geometric structure of the statistical manifold. Technically, this paper defines the histogram dispersion of Wasserstein scalar curvature, which can accurately describe the divergence between different heart diseases. By combining medical experience with mathematical ideas from geometry and data science, this paper provides a feasible algorithm for the new method, and the theoretical analysis of the algorithm is carried out. Digital experiments on the classical database with large samples show the new algorithm\'s accuracy and efficiency when dealing with the classification of heart disease.