ArticleSensors (Basel, Switzerland)2024
An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- An Intelligent IoT-Compatible Arrhythmia Detection System Using a Hybrid Vision Transformer-LSTM Framework.Cardiovascular engineering and technology · 2026Article
- CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.Scientific reports · 2026Article
- ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.Scientific reports · 2026Article
- Real-Time Cardiac Arrhythmia Classification Using TinyML on Ultra-Low-Cost Microcontrollers: A Feasibility Study for Resource-Constrained Environments.Bioengineering (Basel, Switzerland) · 2026Article
- A joint CNN-Bi-LSTM-transformer architecture with SHAP explanations for multi-label arrhythmia detection from 12-lead ECGs.Scientific reports · 2026Article
- An efficient deep CNN based BiLSTM framework with RanA optimization for accurate cardiac arrhythmia detection.Scientific reports · 2026Article
- Explainable hybrid deep learning framework with Grad-CAM for heartbeat-level arrhythmia classification.Frontiers in physiology · 2026Article
- Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis.NPJ digital medicine · 2025Article
- Ecg-based arrhythmia classification using discrete wavelet transform and attention-enhanced CNN-BiGRU model.Physical and engineering sciences in medicine · 2025Article
- Real-Time Heartbeat Classification on Distributed Edge Devices: A Performance and Resource Utilization Study.Sensors (Basel, Switzerland) · 2025Article
- MAK-Net: A Multi-Scale Attentive Kolmogorov-Arnold Network with BiGRU for Imbalanced ECG Arrhythmia Classification.Sensors (Basel, Switzerland) · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Predicting Ventricular Arrhythmia in Myocardial Ischemia Using Machine Learning.Computing in cardiology · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.