Multi-Classification Model for PPG Signal Arrhythmia Based on Time–Frequency Dual-Domain Attention Fusion

TL;DR

This paper introduces a new deep learning model for detecting heart rhythm disorders using PPG signals, achieving high accuracy and supporting wearable health technologies.

Contribution

The novel Fusion-DMA-Net model uses time-frequency dual-domain attention fusion for improved arrhythmia classification from PPG signals.

Findings

The model achieved 99.05% overall accuracy in classifying four types of cardiac arrhythmias.

It outperformed existing methods with high precision and F1-score metrics.

The model demonstrates feasibility for wearable health technologies using single-channel PPG signals.

Abstract

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies.

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