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Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation
Photoplethysmography (PPG) provides a low-cost, non-invasive method to
continuously monitor various cardiovascular parameters. PPG signals are
generated by wearable devices and frequently contain large artifacts caused by
external factors, such as motion of the human subject. In order to ensure
robust and accurate extraction of physiological parameters, corrupted areas of
the signal need to be identified and handled appropriately. Previous
methodology relied either on handcrafted feature detectors or signal metrics
which yield sub-optimal performance, or relied on machine learning techniques
such as deep neural networks (DNN) which lack interpretability and are
computationally and memory intensive. In this work, we present a novel method
to learn a small set of interpretable convolutional kernels that has
performance similar to -- and often better than -- the state-of-the-art DNN
approach with several orders of magnitude fewer parameters. This work allows
for efficient, robust, and interpretable signal quality assessment and artifact
segmentation on low-power devices.Comment: 16 pages, 6 figure
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