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January 1, 2026International Journal of Business Intelligence and Data MiningOpen Access

A deep learning approach to classifying ECG signals under motion artefacts

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Key Points

AI-based classification improves ECG signal interpretation, especially when motion artefacts are present.
Key evidence includes the efficiency of the deep learning model in extracting relevant features from distorted signals.
Analysis employs deep learning algorithms to effectively categorize ECG data influenced by motion.
High accuracy in classifying ECG signals suggests potential for better diagnostic tools in real-world situations.

Implication

Observational analysis shows AI enhances ECG signal classification under motion artefacts, indicating improved accuracy.

Abstract

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Cite This Study

Long Xu (2026) studied this question.

synapsesocial.com/papers/69a7611ec6e9836116a2ebea https://doi.org/10.1504/ijbidm.2026.10076364

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Enhancing the Reliability of Resting ECGs via Deep Learning–Driven Motion Artifact Detection2025
2ARRHYTHMIA CLASSIFICATION USING DEEP LEARNINGother · 2020
3An Intelligent Framework for Forecasting Cardiovascular Disease using Bio-inspired Optimization and MLOriginal Research · 2026
4Deep Learning-Based ECG Classification for Arterial Fibrillation Detectionother · 2024 · 5 citations
5Deep Learning-Based Approaches for ECG Signal Arrhythmia: A Comprehensive Reviewreview · 2023 · 3 citations

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ECG Arrhythmia Classification using Deep Learning2024
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