ArticleIEEE journal of biomedical and health informatics2022
Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks.
Article in IEEE journal of biomedical and health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A three-level risk stratification algorithm for arrhythmia based on CNN-LSTM palindromic structure.BioData mining · 2026Article
- MSCA-TNet based deep learning method for ECG arrhythmia classification.Scientific reports · 2026Article
- pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients.Journal of medical systems · 2026Article
- Higher-dimensional embedding of time-series data for machine learning.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks.Scientific reports · 2025Article
- A knowledge embedded multimodal pseudo-siamese model for atrial fibrillation detection.Scientific reports · 2025Article
- Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram.PloS one · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals.Scientific reports · 2024Article
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms.Bioengineering (Basel, Switzerland) · 2023Article
- Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming.Sensors (Basel, Switzerland) · 2022Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Electrocardiogram classification using TSST-based spectrogram and ConViT.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.
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