Deep Neural Networks Learn by Using Human-Selected ECG Features and Novel Features


We sought to investigate whether artificial intelligence (AI) and specifically deep neural networks for electrocardiogram (ECG) signal analysis can be explained using human selected features. We also sought to quantify such explainability and test if the AI model learns features that are similar to a human expert. We used a set of 100,000 ECGs that were annotated by human explainable features. We applied both linear and nonlinear models to predict published ECG AI models output for the detection of patients' age and sex. We further used canonical correlation analysis to quantify the amount of shared information between the features found in an unsupervised way.

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