ArticleJournal of healthcare engineering2019
An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.
Article in Journal of healthcare engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Learning-efficient spiking neural networks with multi-compartment spatio-temporal backpropagation.iScience · 2025Article
- Efficient pretraining of ECG scalogram images using masked autoencoders for cardiovascular disease diagnosis.Scientific reports · 2025Article
- Clinically interpretable multiclass neural network for discriminating cardiac diseases.Heliyon · 2025Article
- Deep Learning-Based Detection of Arrhythmia Using ECG Signals - A Comprehensive Review.Vascular health and risk management · 2025Review
- Trustworthy diagnosis of Electrocardiography signals based on out-of-distribution detection.PloS one · 2025Article
- Revolutionizing electrocardiography: the role of artificial intelligence in modern cardiac diagnostics.Annals of medicine and surgery (2012) · 2025Review
- Research on a Lightweight Arrhythmia Classification Model Based on Knowledge Distillation for Wearable Single-Lead ECG Monitoring Systems.Sensors (Basel, Switzerland) · 2024Article
- Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.BMC medical research methodology · 2024Article
- BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.IEEE open journal of engineering in medicine and biology · 2024Article
- Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet.Frontiers in physiology · 2024Article
- Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset.Nature communications · 2023Article
- Bimodal CNN for cardiovascular disease classification by co-training ECG grayscale images and scalograms.Scientific reports · 2023Article
- Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases.Journal of personalized medicine · 2023Article
- Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms.Bioengineering (Basel, Switzerland) · 2023Article
- Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review.Heliyon · 2023Review
- A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques.Bioinformatics and biology insights · 2023Review
- Arrhythmia Disease Diagnosis Based on ECG Time-Frequency Domain Fusion and Convolutional Neural Network.IEEE journal of translational engineering in health and medicine · 2023Article
- Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- A multiclass CNN cascade model for the clinical detection support of cardiac arrhythmia based on subject-exclusive ECG dataset.Biomedical engineering letters · 2022Article
- Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.
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