Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification...

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Automated electrocardiogram (ECG)-based arrhythmia detection is crucial for early cardiac disease diagnosis.
• Deep learning models show promise but are hindered by limited labeled ECG data and suboptimal classification accuracy.
• Effective arrhythmia classification requires robust feature extraction and accurate diagnostic strategies.

Purpose of the Study:

• To enhance deep learning applications for arrhythmia classification by addressing data scarcity and accuracy limitations.
• To propose a novel feature extraction and classification strategy integrating generative adversarial network (GAN) data augmentation and model fusion.
• To improve the accuracy and clinical applicability of automated arrhythmia detection systems.

Main Methods:

• Utilized generative adversarial networks (GANs) to augment sparse arrhythmia data.
• Developed a spatial information fusion model (ResNet) and a temporal information fusion model (BiLSTM) for long-term ECG analysis.
• Integrated an attention mechanism to enhance the extraction of key features for final classification.

Main Results:

• The proposed classification technique achieved a 99.4% enhancement in arrhythmia diagnostic accuracy.
• The model demonstrated high recognition performance on the enhanced MIT-BIH arrhythmia database.
• The algorithm effectively fuses spatial and temporal information for robust arrhythmia identification.

Conclusions:

• The developed method significantly improves automated arrhythmia detection accuracy and performance.
• The fusion of GAN data augmentation, ResNet, BiLSTM, and attention mechanisms offers a powerful approach for ECG analysis.
• The algorithm holds substantial clinical value for early cardiac disease prevention and diagnosis.