Extraction of Respiratory Activity from Pulse Oximeter Signals using Tunable Q-factor Wavelet Transform

Abstract

Online monitoring of respiratory activity is essential in situations such as cardiopulmonary disorders, ambulatory monitoring, stress tests, sleep disorder investigations and post-operative hypoxemia. Extraction of respiratory activity from physiological signals having respiratory influence such as pulse oximeter’s photoplethysmographic (PPG) signals would be an alternative under clinical settings compared to that of all direct methods of recording respiratory signals such as spirometry, pneumography or capnography. The respiratory information can be extracted from PPG signal using a simple band pass filter, but the design of narrow band pass filter (NBPF) with classical filter design cannot be possible. In this paper, we present a simple method, based on tunable Q-factor Wavelet transform (TQWT), for extraction of respiratory activity from PPG signals. Advantage of TQWT stems from the fact that, the realization of practical narrow band pass filter with a specific Q-factor value can be designed, which motivated the authors to use for this application. The method is applied on, PPG data recorded from 15 healthy subjects; each consisting of simultaneously recorded PPG and respiratory signals. The extracted respiratory signals are compared with the original respiratory signals. Statistical parameters such as relative correlation co-efficient (RCC) in time domain as well as magnitude squared coherence (MSC) in frequency domain are used for performance evaluation along with error analysis using the accuracy rate (AcR) and normalized mean square error (NRMSE). Experimental results have shown a good acceptance for the extracted signal when compared with the originally recorded respiratory signal. The proposed technique could become an efficient approach for extraction of surrogate respiratory activity from PPG signals, avoiding usage of additional specialized sensor for respiratory monitoring.