Extraction Of Respiratory Activity Research Articles

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

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.

Extraction of Respiratory Activity from Photoplethysmographic Signals

Extraction of Respiratory Activity from Photoplethysmographic Signals

Respiration and Heart Rate Monitoring from Photoplethysmograph Signal

Photoplethysmograph is a simple and cost effective method to assess cardiovascular related parameters such as heart rate, arterial blood oxygen saturation, BP the fact that the Photoplethysmograph (PPG) signal caries respiratory information. Earlier physicians used to insert separate sensors to assess respiration and heart rate. In this present work, an efficient algorithm is presented, based on the wavelet decomposition technique to extract the respiratory activity from the PPG signals. With this aim displaying of heart rate and the extraction of respiratory activity is done. Here MATLAB and MULTISIM tools were used for simulation. Wavelet decomposition performed exceptionally well for extraction of respiratory activity from PPG. Extracted signal is compared with a signal which was acquired from the existing respiratory sensor and the correlation between them is up to 90%. Then the obtained parameters are sent to the physician's mobile phone for faster and better assessment of the patient from remote places. Hence using this method it can reduce the usage of sensor for respiration, and also reduces the discomfort caused to the patient, also cost effective.

Extraction of Respiratory Activity from Photoplethysmographic Signals Based on an Independent Component Analysis Technique: Preliminary Report

A new method is reported for monitoring respiratory activity using photoplethysmography (PPG) without the need of prior knowledge of the respiratory rate range. Two channels of transmission mode PPG signals were collected from the subject's index finger and analyzed using an independent component analysis (ICA) algorithm. The respiratory activity was separated from the heart‐related pulsation in PPG after the ICA analysis. The results demonstrated that the ICA analysis could successfully extract normal and simulated apnoea respiratory activities. The algorithm was applied for both simulated signals and those collected from 10 young normal adults. The reported technique may be potentially used for the simultaneous monitoring of the pulse rate and respiratory activity based on the hardware of current pulse oximetry devices. The structure and implementation of this preliminary respiratory activity monitoring system were presented. Possible further improvements for the system performance were also discussed.