Improved Bivariate-VAR Model for Extraction of Respiratory Information from Artifact Corrupted ECG and PPG Signals

Abstract

In general in ICUs, operation theatres, post-operative critical care units, and even ambulatory monitors, the patients are continuously examined with ECG and pulse oximeters PPG. In these situations, where the ECG and/or PPG are afflicted by severe artifacts, the idea of extracting respiratory signal from both ECG and PPG signals rather than from any one of them is tested in this work. As respiratory trend is present in both ECG and PPG signals, the common respiratory trend present in simultaneously recorded ECG and PPG signals is extracted, using a bivariate vector autoregressive modeling (BVAR) technique. This technique effectively reduced the inevitable artifacts and resulted in better estimation of the respiratory activity. For further improving the performance of the BVAR method, in extracting respiratory activity from ECG and PPG signals corrupted with sever artifacts, and also works for broad range of breathing rates, an improved BVAR (IB-VAR) technique is proposed. This technique is robust in the sense that it, firstly, works well even in the presence of various artifacts present in either of the signals, and extracts the signal common to both i.e. respiratory information, with a greater accuracy. Secondly, it also works even for a broad range of breathing rates covering as low as 6 breaths per minute (bpm) to as high as 90 bpm. The novel part of the proposed IB-VAR method is that the respiratory pole lying in that broad breathing range is automatically selected from among all other possible poles, which also include the ones corresponding to noises like motion artifact (MA) and baseline wander (BLW), making use of kurtosis values of extracted signals. An analog front end is developed to record ECG, PPG and respiratory signals with different breathing rates and respiration patterns simultaneously from the volunteers. The method, applied on the recorded data of fifteen healthy subjects, performed extremely well even in the presence of MA and BLW, compared to the well known wavelet based approach. Correlation analysis, done in both frequency and time domains, has shown a high degree of acceptance for the extracted respiratory signal with respect to the original reference respiratory signal. Higher values of accuracy rate (EDR: 98.10 ± 1.45, PDR: 98.45 ± 1.30) and lower values of NRMSE calculations (EDR: − 6.47 ± 4.29, PDR: − 6.50 ± 4.17) clearly confirmed the validity of the extracted respiratory signal. An important finding of this work is that the PPG derived respiratory signal very closely matched with the original than the ECG derived signal.