Title: A deep learning approach to classifying ECG signals under motion artefacts

Authors: Long Xu

Addresses: Qiqihar University, Qiqihar, China

Abstract: Wearable electrocardiogram (ECG) monitoring is essential for health assessment during physical activity; however, motion artifacts remain a major challenge, severely compromising signal quality. Traditional filtering techniques often lack adaptability to dynamic noise variations, and deep learning models typically overlook motion-induced signal degradation. To overcome these limitations, we propose an acceleration-assisted framework for ECG denoising and activity classification. Specifically, motion intensity is calculated from chest acceleration data, guiding the adaptive removal of intrinsic mode functions (IMFs) via empirical mode decomposition (EMD). The denoised ECG signals are subsequently classified using a one-dimensional convolutional neural network (1D-CNN). Experiments conducted on the publicly available MHEALTH dataset demonstrate the effectiveness of the proposed method, yielding classification metrics of 0.485 (accuracy), 0.492 (precision), 0.486 (recall), and 0.476 (F1-score). The framework effectively mitigates motion artifacts while preserving essential ECG waveform features, ensuring more robust performance across various physical activities.

Keywords: electrocardiograph signal; ECG; motion artefact; empirical mode decomposition; EMD; acceleration signal; 1D convolutional neural network; activity recognition.

DOI: 10.1504/IJBIDM.2026.152487

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.2/3, pp.215 - 229

Received: 01 Jun 2025
Accepted: 25 Sep 2025
Published online: 23 Mar 2026 *

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