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Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.
Xiangkui Wan1, Xiaoyu Mei1, Yunfan Chen1
1Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China.
Summary
This study introduces a new deep learning model for automatic arrhythmia classification, achieving high accuracy. The advanced model enhances feature extraction for improved cardiovascular disease diagnosis.
Area of Science:
- Cardiology
- Artificial Intelligence
- Signal Processing
Background:
- Deep neural networks (DNNs) are increasingly used for automatic arrhythmia classification.
- Current DNN models show limitations in classification accuracy.
- There is a need for improved methods to capture detailed temporal and inter-channel information.
Purpose of the Study:
- To develop a more effective approach for automatic arrhythmia classification.
- To enhance receptive field sizes for capturing multi-scale temporal information.
- To incorporate inter-channel correlations for improved feature extraction.
Main Methods:
- Proposed a pyramidal dense connectivity layer and bidirectional long short-term memory network (PDC-BiLSTM).
- Integrated efficient channel attention (ECA) for dynamic feature channel weighting.
- Evaluated the model on the MIT-BIH arrhythmia database.
Main Results:
- Achieved 99.82% overall accuracy in the intra-patient paradigm.
- Reported 99.64% positive predictive value, 97.61% sensitivity, and 98.60% F1 Score (intra-patient).
- Attained 96.30% overall accuracy in the inter-patient paradigm.
Conclusions:
- The proposed PDC-BiLSTM with ECA model outperforms existing methods in arrhythmia classification accuracy.
- Demonstrates significant potential for application in cardiovascular disease diagnostic devices.
- Highlights the effectiveness of combining multi-scale feature extraction with attention mechanisms.
