A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Signal Processing

Background:

• Noise in long-term monitoring (LTM) electrocardiograms (ECG) compromises diagnostic accuracy and analysis efficiency.
• Clinical severity of ECG noise, based on interpretability, presents a challenge distinct from traditional quantitative measures.
• Previous work established Machine Learning (ML) models for ECG noise classification using clinical severity labels.

Purpose of the Study:

• To explore Deep Learning (DL) models for classifying clinical noise in LTM ECG recordings.
• To develop explainable DL architectures for enhanced decision-making transparency.
• To compare DL performance against prior ML approaches and assess the utility of interpretable models.

Main Methods:

• Developed two sets of Convolutional Neural Networks (CNNs): a novel 1-D CNN and fine-tuned pre-trained 2-D CNNs via transfer learning.
• Designed two Autoencoder (AE) architectures to enable model interpretability through latent space data regionalization.
• Employed patient separation in the test set to mitigate intra-patient overfitting.

Main Results:

• DL systems significantly outperformed previous ML approaches in ECG noise classification, achieving an F1-score up to 0.84.
• Interpretable DL architectures demonstrated comparable performance to non-interpretable models, offering qualitative explanations.
• The developed systems successfully classified clinical noise in LTM ECG data.

Conclusions:

• Integrated DL and interpretable systems are highly effective for classifying clinical noise in LTM ECGs.
• This approach enhances clinician confidence in AI-driven decision support systems, facilitating technology transfer.
• The systems assist healthcare professionals in discerning diagnostically relevant portions of ECG signals.