Key result
Deep Learning models, including interpretable architectures, achieved superior clinical noise classification performance compared to previous machine learning approaches, with an F1-score up to 0.84.
Why the study?
Noise significantly impacts ECG quality in long-term monitoring, and deep learning architectures that provide explainability for clinical noise classification were needed.
Deep learning models with interpretable architectures effectively classify clinical noise in long-term ECG recordings, potentially enhancing clinical decision support systems.
May enhance ECG noise classification; hypothesis-generating for clinical decision support without prospective outcome validation.
OBJECTIVE: In Long-Term Monitoring (LTM), noise significantly impacts the quality of the electrocardiogram (ECG), posing challenges for accurate diagnosis and time-consuming analysis. The clinical severity of noise refers to the difficulty in interpreting the clinical content of the ECG, in contrast to the traditional approach based on quantitative severity. In a previous study, we trained Machine Learning (ML) algorithms using a data repository labeled according to the clinical severity. In this work, we explore Deep Learning (DL) models in the same database to design architectures that provide explainability of the decision making process. METHODS: We have developed two sets of Convolutional Neural Networks (CNNs): a 1-D CNN model designed from scratch, and pre-trained 2-D CNNs fine-tuned through transfer learning. Additionally, we have designed two Autoencoder (AE) architectures to provide model interpretability by exploiting the data regionalization in the latent spaces. RESULTS: The DL systems yield superior classification performance than the previous ML approaches, achieving an F1-score up to 0.84in the test set considering patient separation to avoid intra-patient overfitting. The interpretable architectures have shown similar performance with the advantage of qualitative explanations. CONCLUSIONS: The integration of DL and interpretable systems has proven to be highly effective in classifying clinical noise in LTM ECG recordings. This approach can enhance clinicians' confidence in clinical decision support systems based on learning methods, a key point for this technology transfer. SIGNIFICANCE: The proposed systems can help healthcare professionals to discriminate the parts of the ECG that contain valuable information to provide a diagnosis.
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Holgado–Cuadrado et al. (2024) studied Clinical noise in Long-Term Monitoring (LTM) ECG. Deep Learning (DL) models (CNNs and Autoencoders) vs. Previous Machine Learning (ML) approaches was evaluated on Classification performance (F1-score). Deep Learning models, including interpretable architectures, achieved superior clinical noise classification performance compared to previous machine learning approaches, with an F1-score up to 0.84.
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