Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment.

ABSTRACT: Photoplethysmography (PPG) is a noninvasive way to monitor various aspects of the circulatory system, and is becoming more and more widespread in biomedical processing. Recently, deep learning methods for analyzing PPG have also become prevalent, achieving state of the art results on heart rate estimation, atrial fibrillation detection, and motion artifact identification. Consequently, a need for interpretable deep learning has arisen within the field of biomedical signal processing. In this paper, we pioneer novel explanatory metrics which leverage domain-expert knowledge to validate a deep learning model. We visualize model attention over a whole testset using saliency methods and compare it to human expert annotations. Congruence, our first metric, measures the proportion of model atten

SUBMITTER: Zhang O 

PROVIDER: S-EPMC7978398 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature