ArticleSensors (Basel, Switzerland)2023
Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- HAT-ECG: Hybrid autoencoder-transformer architecture for ECG arrhythmia classification.Physiological reports · 2026Article
- LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.Physiological reports · 2024Article
- Tunable Neuromorphic Switching Dynamics via Porosity Control in Mesoporous Silica Diffusive Memristors.ACS applied materials & interfaces · 2024Article
- Automated myocardial infarction and angina detection using second derivative of photoplethysmography.Physical and engineering sciences in medicine · 2023Article
- Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.
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Registered trials
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.