Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement

Abstract

Photoplethysmography (PPG) has been extensively employed to acquire some physiological parameters such as heart rate, oxygen saturation, and blood pressure. However, PPG signals are frequently corrupted by motion artifacts and baseline wandering, especially for the reflective PPG sensor. Several different algorithms have been studied for determining the signal quality of PPG by the characteristic parameters of its waveform and the rule-based methods. The levels of signal quality usually were defined by the manual operations. Thus, whether the good PPG waveforms are enough to increase the accuracy of the measurement is still a subjective issue. The aim of this study is to use a fuzzy neural network to determine the signal quality indexes (SQI) of PPG pulses measured by the impedance cardiography. To test the algorithm performance, the beat-to-beat stroke volumes (SV) were measured with our device and the medis® CS 2000, synchronously. A total of 1466 pulses from 10 subjects were used to validate our algorithm in which the SQIs of 1007 pulses were high, those of 71 pulses were in the middle, and those of 388 pulses were low. The total error of SV measurement was −18 ± 22.0 mL. The performances of the classification were that the sensitivity and specificity for the 1007 pulses with the high SQIs were 0.81 and 0.90, and the error of SV measurement was 6.4 ± 12.8 mL. The sensitivity and specificity for the 388 pulses with the low SQIs were 0.84 and 0.93, while the error of SV measurement was 30.4 ± 3.6 mL. The results show that the proposed algorithm could be helpful in choosing good-quality PPG pulses to increase the accuracy of SV measurement in the impedance plethysmography.

Evaluating Quality of Photoplethymographic Signal on Wearable Forehead Pulse Oximeter With Supervised Classification Approaches

“…In this study, the performance of VGG-19 model was not better than the SVM. This result had the opposite view to the previous studies of Liu et al [17,18]. The reason was considered that the SQI classification of PPG segment pertains to a generalized decision.…”

“…Thus, the SQI decision has to be peak by peak. Liu et al compared the performances of the SVM and CNN approaches [17,18]. They found that the CNN approach could detect slighter distortion for the PPG wave than the SVM approach.…”

“…Pereira et al, [15] and Chong et al [16] used the support vector machine (SVM) to determine the SQI of PPG segment. Liu et al used the fuzzy neural network to evaluate the SQI of PPG pulse [17]. Liu et al also used the deep learning approaches to classify the SQI of PPG pulse with the same data measured by the previous study [18].…”

Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach

“…PPG is a low-cost optical technique able to detect blood volume oscillations in peripheral arteries induced by pulse wave propagation from the heart [14][15][16]. PPG is widely used in medical devices for monitoring oxygen saturation through the evaluation of pulse-related intensity modulation at different light wavelengths (e.g., pulse oximeters).…”

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

“…For the ubiquitous healthcare, the detection of heartbeat based on filter banks and fuzzy inference was done by Lee and Kang [ 33 ]. For improving the stroke volume measurement, the PPG signal quality was classified using fuzzy neural networks by Liu et al [ 34 ]. Not much literature is available with exception to the very few works done with the application of fuzzy concept to PPG signal modeling, analysis, or classification.…”