Multi-Observation Hidden Semi-Markov Model for Photoplethysmogram Signal Semantic Segmentation

Photoplethysmogram (PPG) is a major indicator of a patient's physiological status. PPG is generally studied using manually designed algorithms to detect its critical morphological points. However, existing algorithms for analyzing these signals do not serve the purpose effectively and accurately, particularly for abnormal signals. This paper proposes a multi-observation hidden semi-Markov model (HSMM) for PPG signal semantic segmentation, which leverages the information available in raw signal and its first and second derivatives simultaneously. The results indicate the feasibility of PPG signal segmentation with high accuracy using an HSMM and a small training dataset. Moreover, employing a multi-observation approach improves the accuracy significantly.