Abstract 17189: Explaining the Deep Learning Black Box by Identifying Segments of the Single-Lead ECG Signal Used for Detection of Atrial Fibrillation

Abstract

Introduction: Deep learning (DL) has proved effective for automatic identification of atrial fibrillation (AF) using single-lead ECG. Adoption and trust of DL by clinicians is limited by its black box nature. Hypothesis: Post hoc explanations can elucidate what part of ECG signal is used by the black box DL algorithm, quantifying the importance of clinically relevant features in the classification decision. Making DL decision process transparent will help its integration into clinical practice. Methods: 8,528 single-lead ECG recordings collected using AliveCor devices (PhysioNet) were used. Each signal was labeled as normal sinus rhythm, AF, other arrhythmia or noise. DL automatic classification involves a lightweight convolutional neural network architecture - MobileNet - whose performance is analyzed with an explanation method for DL. Results: Each RR interval is divided into 8 equal segments, where segment 1 follows each R peak, 4 and 5 correspond to the isoelectric baseline, and 7 to the P wave. The explanation method substitutes one of these segments with a straight line, and the corresponding change in sensitivity highlights its importance for the DL algorithm decision. MobileNet achieved a sensitivity of 92.5% to identify AF (9.4% of ECGs were in AF). Sensitivity increases by 2.5% when Segment 7 is removed, indicating that the absence of P wave leads the network to classify more frequently samples as AF.(Figure) When Segments 4 and 5 are removed, the sensitivity decreases by 2.5% and 5.0%, and by 26.7% when removed together. When all RR intervals are normalized to the same value (RR in the Figure), sensitivity for AF drops by 78.3%, showing that RR intervals are key for AF detection by DL algorithm. Conclusions: Post hoc explanations for AF detection by DL from single-lead ECG show the importance of common morphological features used for classifying AF. These methods can be used to understand the decision-making process of DL and motivate its clinical adoption.