Realtime PPG based respiration rate estimation for remote health monitoring applications. (August 2022) - Selvakumar, K; Vinodh Kumar, E; Sailesh, M; Varun, Mamtani; Allan, Anbu; Biswajit, Nanda; Namrata, Panga; Upasana, Sivaramakrishnan
Highlights: Design and validation of an unobtrusive and easy-to-use wearable de-vice to measure RR is presented. The RIAV from reflectance type PPG signals is extracted using IMS algorithm. Adaptive thresholding algorithm and FFT analysis is employed to ex-tract RR from RIAV signature. The proposed approach achieves a RR accuracy of 1 bpm deviation against the ground truth. Abstract: Among the many vital parameters of human body, breath rate monitoring is paramount to detect the symptoms of several respiratory diseases such as sleep apnea syndrome, chronic obstructive pulmonary disease (COPD), and asthma. Hence, this paper puts forward a novel framework to measure the respiration rate (RR) using the reflective type photoplethysmogram (PPG) signals acquired using an inexpensive and easy-to-use wearable device. Extracting the respiration induced amplitude variations (RIAV) from PPG signal using the incremental merge segmentation (IMS) algorithm, the proposed approach shows that robust RR estimation in realtime is feasible through low cost Cortex-M4 microcontroller. Using the sliding window approach augmented with adaptive thresholding technique that can deal with motion artifacts, we show that robust RR estimation is viable using reflectance type PPG, which is largely unexplored. Moreover, to handle the non-uniform nature of RIAV signal, this work employs uniform interval interpolation technique and removes the non-respiratory frequencies using finite impulse response (FIR) band-pass filter. Through the fast Fourier transform (FFT) analysis of regular interval RIAV sequence, the dominant frequency corresponding to the RR is extracted. The performance of the proposed scheme is validated not only on two publicly available PPG datasets but also on a custom designed experimental setup integrated with the smartphone. The experimental results highlight that our approach can achieve a RR estimation accuracy of 1 bpm deviation against the ground truth.