QaPRExt: A unified deep learning framework for quality-aware PPG-derived respiration signal extraction for personalized healthcare

Abstract The presence of motion artifact in the photoplethysmogram (PPG) signal makes it challenging for PPG-derived respiration (PDR) measurements in continuous health monitoring. In this work, a deep-learning framework for quality-aware PPG derived respiration signal extraction, named QaPRExt, is described. This includes three key stages: pre-processing of the raw PPG signal, PPG snippet quality evaluation using a reservoir computing model, namely RCSQNet, and fnally, PDR extraction using the proposed QaRExt module. QaPRExt was evaluated with a synthetic noisy signal using two noise insertion strategies (NISs), viz., additive and convolution, and the real-time noisy PPG signals with their corresponding reference respiration signals collected in the laboratory. The proposed framework achieves an average correlation of 0.91, 0.88 for PDR extraction and an average mean absolute error (MAE) of 0.70, 0.10 breaths per minute (BPM), respectively in estimating respiration rate (RR) from the
noisy PPG segments for the 53 subjects of BIDMC and 30 subjects from the volunteers’ database, respectively.