Project Title: Efficient Extraction of the Respiratory Effort from the Surface Cardiovascular Signal: Photoplethysmogram.
Abstract: Noninvasive monitoring of respiratory activity is an emerging research area in biomedical health monitoring. This article describes a neural network-based model, intelligent photoplethysmography-derived Respiration signal Extraction, and Tracking (i-PRExT). Here, an ensemble empirical mode decomposition (EEMD) is used to select the appropriate intrinsic mode functions (IMFs) through filtering in the respiration band and reconstruct by a linear weighted sum to obtain the photoplethysmography derived respiration (PDR) signal. The weight factors are derived by a multilayer perceptron neural network (MLPNN) fed with respiratory induced amplitude variation (RIAV) features extracted by a deep autoencoder (DAE). The tracking of respiration rate (RR) is done by an adaptive filter-based predictor. i-PRExT was tested and validated with BIDMC data set under PhysioNet and 30 volunteers' data collected under resting condition. The PDRs achieved over 90% correlation and low error (NRMSE~0.2) with reference respiration signal, while RRs have almost 100% correlation even under motion artifact (MA) corrupted photoplethysmography (PPG). The PDR shows improved performance, while RR tracking outperforms the published research on respiration signal extraction based on PPG.
Principal Investigator: Dr. Jayanta Kumar Chandra, Co-Principal Investigator: Dr. Rajarshi Gupta.
Funding Agency: Department of Science and Technology and Biotechnology, Govt. of West Bengal.
Publication: B. Roy, A. Roy, J. K. Chandra, and R. Gupta, "i-PRExT: Photoplethysmography Derived Respiration Signal Extraction and Respiratory Rate Tracking Using Neural Networks," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021, Art no. 2504309, doi:10.1109/TIM.2020.3043506 .