ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2019
Inter- and intra-patient ECG heartbeat classification for arrhythmia detection: A sequence to sequence deep learning approach.
Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Clinically-aligned explainable AI for atrial fibrillation detection: A U-Net inspired multi-lead ECG analysis framework.Computer methods and programs in biomedicine · 2026Article
- A Robust Multivariate Thresholding Function for Sparse and Biomedical Signal Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- Towards Feasible Home ECG Monitoring: AI-Driven Detection of Clinically Critical Arrhythmias Using Single-Lead Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis.NPJ digital medicine · 2025Article
- Efficient Keyset Design for Neural Networks Using Homomorphic Encryption.Sensors (Basel, Switzerland) · 2025Article
- Article
- Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.Sensors (Basel, Switzerland) · 2024Article
- Deep residual 2D convolutional neural network for cardiovascular disease classification.Scientific reports · 2024Article
- End-to-End Signal Classification in Signed Cumulative Distribution Transform Space.IEEE transactions on pattern analysis and machine intelligence · 2024Article
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology.IEEE transactions on neural networks and learning systems · 2023Review
- A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG.Scientific reports · 2023Article
- Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination.Frontiers in physiology · 2023Article
- An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem.Sensors (Basel, Switzerland) · 2022Article
- Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks.IEEE journal of biomedical and health informatics · 2022Article
- Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label.IEEE journal of translational engineering in health and medicine · 2022Article
- A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification.Computational and mathematical methods in medicine · 2022Article
- Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data.JMIR medical informatics · 2021Article
- ECG Language processing (ELP): A new technique to analyze ECG signals.Computer methods and programs in biomedicine · 2021Article
- Transfer learning for ECG classification.Scientific reports · 2021Article
- A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection.Computers in biology and medicine · 2021Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models. We evaluated the proposed method on the MIT-BIH arrhythmia database, considering the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results for both paradigms show that our method achieves the best performance in the literature (a positive predictive value of 96.46% and sensitivity of 100% for the category S, and a positive predictive value of 98.68% and sensitivity of 97.40% for the category F for the intra-patient scheme; a positive predictive value of 92.57% and sensitivity of 88.94% for the category S, and a positive predictive value of 99.50% and sensitivity of 99.94% for the category V for the inter-patient scheme.).
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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.