Robust Extraction of Respiratory Activity From PPG Signals Using Modified MSPCA

The pulse oximeter's photoplethysmographic (PPG) signals can be well utilized for extracting the vital respiratory activity, in addition to saturation and heart rate estimations, avoiding the usage of additional sensor for recording respiratory signal, in turn reducing the number of sensors connected to the patient's body for recording vital signals. In this paper, we present a robust algorithm called modified multi scale principal component analysis (MMSPCA), for extraction of respiratory activity embedded in the PPG signals. The PPG signals are more commonly corrupted by motion artifacts (MA) due to voluntary or involuntary movements of the patients, making it difficult for the algorithms to extract respiratory signals. The problem of extracting respiratory signals from PPGs in the presence of MA is addressed for the first time in this paper. The problem gets aggravated when PPGs are severely afflicted with MAs in situations such as the MA frequency band (usually below 0.2 Hz) overlapping on to the band of respiratory frequencies (0.2-0.4 Hz). In the presented algorithm, the kurtosis and energy contribution levels (ECL) of approximate and detail coefficients are calculated for each wavelet sub-band matrix, generating a modified wavelet sub-band matrix. This makes the presented algorithm based on MMSPCA more robust in the sense that it is made motion resistant by suitably modifying the MSPCA. Functioning of the proposed algorithm is tested on the data recorded from 15 healthy subjects. Each data set consists of intentionally created possible MA noises, viz., vertical, horizontal, waving, and pressing MAs with different breathing patterns. The method is also applied on the recordings available with MIMIC database of Physionet archive. The statistical and error analysis, performed to test the efficacy of the presented MMSPCA algorithm, revealed a very good acceptance for derived respiratory signal, when compared with the originally recorded respiratory signals using classical method. The MMSPCA method clearly outperformed the conventional MSPCA-based method in the presence of MA.

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