Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks

Area of Science:

• Cardiology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Inter-patient variability in electrocardiogram (ECG) data poses challenges for accurate heartbeat classification.
• Existing deep learning models often struggle with generalizing to unseen patients due to variations in ECG morphology and rhythm.

Purpose of the Study:

• To develop a novel deep learning framework for robust inter-patient ECG heartbeat classification.
• To introduce a new symbolization approach for ECG that captures both morphology and rhythm while mitigating inter-patient variations.

Main Methods:

• A novel ECG-specific symbolization approach is proposed for baseline correction and joint representation of heartbeat morphology and rhythm.
• A multi-perspective convolutional neural network (MPCNN) utilizes the symbolic representation for automated feature learning and classification.
• The framework is evaluated on the MIT-BIH arrhythmia dataset for supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) detection.

Main Results:

• The proposed method achieved an overall accuracy of 96.4% on the MIT-BIH arrhythmia dataset.
• Excellent F1 scores were obtained for SVEB (76.6%) and VEB (89.7%) detection.
• Ablation studies confirmed the effectiveness of the symbolization approach and the joint representation architecture in enhancing generalization.

Conclusions:

• The novel deep learning framework with its unique symbolization approach significantly improves inter-patient ECG heartbeat classification performance.
• The method demonstrates superior generalization capabilities for unseen patients, outperforming state-of-the-art techniques.
• This adaptable framework holds potential for various other ECG classification tasks, reducing reliance on handcrafted features and expert intervention.