Interpretability of Neural Networks applied to Electrocardiograms : Translational Applications in Cardiovascular Diseases

Résumé

Electrocardiograms (ECGs) are non-invasive tools for assessing the electrical activity of the heart, they are widely used to detect cardiac abnormalities. Deep learning algorithms enable automatic detection of complex patterns in ECG data, offering significant potential for improved cardiac diagnosis. However, their adoption is hindered by a low level of trust among medical professionals and a substantial need for data to train the models. Artificial intelligence, particularly deep learning, allows for exploration of hierarchical representations of complex data, leading to a better understanding of internal interactions. Nevertheless, interpretability of the models are crucial to gain specialists’ trust and facilitate widespread implementation. This thesis aims to develop a novel interpretability algorithm for neural networks applied to ECG analysis, working in close collaboration with cardiology specialists. Our study focuses on a specific cardiac pathology, Torsades-de-Pointes (TdP). TdP is a life threatening arrhythmia associated with various factors, including medications and congenital mutations. Accurate prediction of this risk can enhance patient care and potentially save lives. We started by designing a neural network algorithm for predicting the risk of TdP using ECG data. Second, we developed a new interpretability algorithm named Evocclusion, that enables a better understanding of the neural network’s decision process. This algorithm aims to provide human readable insights into the model’s predictions, leading to increased trust among clinicians and specialists. Third, we present two main frameworks developed to improve ECG analysis and the interpretability method. A crucial aspect of ECG analysis is signal quality. Therefore, we propose a new method using a denoising autoencoder to significantly remove noise from the ECG data and partially recover the waveform from alterations. This technique improves the reliability of the input data for subsequent analysis and ensures that the neural networks have access to high quality information. We also developed neural networks to segment the ECG and extract beats, P and T waves, and QRS complexes. These segmentation results enable a deeper understanding of the ECG components and facilitate further analysis. Additionally, we provide a method to assess a quality score vector of the ECG, enabling us to focus on parts of the signal that have a good quality score. This approach ensures that the most reliable information is used for analysis and clinicians which reduces the risk of false positives and negatives. This research seeks to enhance trust in artificial intelligence, leading to better automation of complex tasks in medicine and beyond, ultimately improving patient outcomes.

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