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

TLDR

This paper introduces a novel deep‑learning framework for inter‑patient ECG heartbeat classification. The framework employs a specially designed ECG symbolization that jointly encodes morphology and rhythm, then feeds the symbolic representation into a multi‑perspective convolutional neural network to automatically learn features and classify beats, evaluated on the MIT‑BIH arrhythmia dataset. The method attains 96.4 % overall accuracy with F1 scores of 76.6 % for SVEB and 89.7 % for VEB, surpassing state‑of‑the‑art baselines, and ablation studies confirm that the symbolization and joint representation enhance generalization to unseen patients and enable adaptation to other ECG classification tasks.

Abstract

This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed for ECG is introduced, which can jointly represent the morphology and rhythm of the heartbeat and alleviate the influence of inter-patient variation through baseline correction. The symbolic representation of the heartbeat is used by a multi-perspective convolutional neural network (MPCNN) to learn features automatically and classify the heartbeat. We evaluate our method for the detection of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) on MIT-BIH arrhythmia dataset. Compared with the state-of-the-art methods based on manual features or deep learning models, our method shows superior performance: the overall accuracy of 96.4%, F1 scores for SVEB and VEB of 76.6% and 89.7%, respectively. The ablation study on our method validates the effectiveness of the proposed symbolization approach and joint representation architecture, which can help the deep learning model to learn more general features and improve the ability of generalization for unseen patients. Because our method achieves a competitive inter-patient heartbeat classification performance without complex handcrafted features or the intervention of the human expert, it can also be adjusted to handle various other tasks relative to ECG classification.