A deep learning approach to classifying ECG signals under motion artefacts

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.

Abstract