A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables

The objective
of this study is to develop an effective supervised algorithm to locate the regions of artifacts
within PPG signals. Approach . We treat artifact detection as a 1D segmentation problem. We solve it
via a novel combination of an active-contour-based loss and an adapted U-Net architecture. The
proposed algorithm was trained on the PPG DaLiA training set, and further evaluated on the PPG DaLiA
testing set, WESAD dataset and TROIKA dataset. Main results . We evaluated with the DICE score, a
well-established metric for segmentation accuracy evaluation in the field of computer vision. The
proposed method outperforms baseline methods on all three datasets by a large margin ( ?7 perce...

Source: Physiological Measurement - December 29, 2021 Category: Physiology Authors: Zhicheng Guo, Cheng Ding, Xiao Hu and Cynthia Rudin Source Type: research