Arrhythmia classification detection based on multiple electrocardiograms databases

A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals

Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascular conditions. Detecting these anomalies early through electrocardiogram (ECG) signal analysis is critical for preventive healthcare and effective treatment. However, the automatic classification of arrhythmias poses significant challenges, including class imbalance and noise interference in ECG signals. This paper introduces the Multi-Scale Convolutional LSTM Dense Network (MS-CLDNet) model, an advanced deep-learning model specifically designed to address these issues and improve arrhythmia classification accuracy and other relevant metrics. This paper aims to develop an efficient deep-learning model, MS-CLDNet, for accurately classifying cardiac arrhythmias from electrocardiogram (ECG) signals. Addressing challenges like class imbalance and noise interference, the model integrates bidirectional long short-term memory (LSTM) networks for temporal pattern recognition, Dense Blocks for feature refinement, and Multi-Scale Convolutional Neural Networks (CNNs) for robust feature extraction. To achieve accurate classification of different types of arrhythmias, the Classification Head refines these extracted features even further. Utilizing the MIT-BIH arrhythmia dataset, key pre-processing techniques such as wavelet-based denoising were employed to enhance signal clarity. Results indicate that the MS-CLDNet model achieves a classification accuracy of 98.22 %, outperforming baseline models with low average loss values (0.084). This research highlights how crucial it is to combine sophisticated neural network architectures with efficient pre-processing techniques to improve the precision and accuracy of automated cardiovascular diagnostic systems, which could have important healthcare applications for early and accurate arrhythmia detection.

Integrated proteomics and metabolomics to clarify the essential beneficial mechanisms of L-theanine in alleviating ISO-induced cardiac damage in mice

L-theanine (L-Thea), a bioactive amino acid found in tea, demonstrates remarkable nutraceutical properties. Isoproterenol (ISO) has been utilized as a reliable and potent agent to induce heart failure (HF) in murine models. The aim of this study was to explore the mechanisms through which L-Thea alleviates ISO-induced cardiac damage via the examination of proteomic and metabolomic data. Herein, we first successfully developed an ISO-induced cardiac injury model in mice. Then, the intervention with L-Thea demonstrated an improvement in cardiac performance and enhanced ventricular function. The results of histological assessments suggested that L-Thea has the potential to mitigate inflammatory infiltration, cardiomyocytes loss, and myocardial fibrosis in heart tissue affected by ISO-induced cardiac injuries in mice. Moreover, the proteomic data indicated that L-Thea led to a significant reduction in apoptosis, the p53 signaling pathway, and the IL-17 signaling pathway within cardiac tissue. Significantly, there were five KEGG pathways that were shown in both the proteome and metabolome, including apoptosis, purine metabolism, cAMP signaling pathway, ABC transporters and cytochrome P450. The western blot results further confirmed that L-Thea induced the downregulation of BAX (pro-apoptotic protein) and the upregulation of BCL-2 (anti-apoptotic protein), thereby suppressing apoptosis in the cardiac tissue of mice. Collectively, L-Thea possesses the capacity to function as a dietary supplement for the prevention or management of cardiac damage induced by ISO.

Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms

The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar. However, by adopting such representations, state-of-the-art approaches usually rely on the magnitude information alone, while the important phase information is often neglected. Motivated by these considerations, the focus of this paper is aimed at investigating the effect of fusing the magnitude and phase of the continuous wavelet transform (CWT), known as the scalogram and phasogram, respectively. Scalograms and phasograms are fused in a simple CNN-based architecture by using several fusion strategies, which fuse the information in the input layer, some intermediate layers, or in the output layer. Numerical results evaluated on the PhysioNet MIT-BIH Arrhythmia database show the effectiveness of the proposed ideas. Although a simple architecture is used, their competitiveness is high compared to other state-of-the-art approaches, by obtaining an overall accuracy of about 98.5% and sensitivity and specificity of 98.5% and 95.6%, respectively.

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.