Abstract
A wearable health-monitoring device such as photoplethysmography (PPG) is widespread in clinical application and in-home care. However, the effect of motion artifacts is a big problem. In this thesis, a method that is used to extract physiological information from PPG signal with motion artifacts detect and removal is proposed. The procedures of our method contain three parts. First, the PPG data which are recorded from the wearable transmission type PPG sensor are preprocessed by discrete wavelet trans- form (DWT) in order to remove some unwanted noise and extract AC and DC component of PPG signal. Then, the characteristic points such as peaks and troughs are identified for the fea- ture extraction and physiological information extraction. The second part is feature extraction and motion artifact detection. The features include four time domain parameters, which are standard deviation of peak-to-peak amplitudes, standard deviation of peak-to-peak intervals, mean absolute deviation of peak-to-peak amplitudes, and the kurtosis of the signal segments. To detect the motion artifact periods, we employ support vector machine (SVM) to classify. The detection performance was verified on PPG signals recording by the 11 different healthy subjects with waving hands. The detection method gives the best performance in 7 second period with accuracy of 94.4%, the sensitivity of 90.35%, and the specificity of 99.36%. The third part is arterial oxygen saturation (SpO 2 ), heart rate (HR) extraction and motion artifact removal. The motion artifact removal part is accomplished by using Kalman filter to track the SpO 2 and HR extracted from motion artifact-corrupted periods. The parameters of Kalman filter are determined by the detection results. In the case of waving hand left-right, the average mean absolute bias of artifact-corrupted SpO 2 and HR are 1.34% and 7.29 bpm, respectively. After applying the algorithm, the bias become 0.8% and 4.29 bpm. In the case of waving hand up and down, the errors of SpO 2 and HR reduce from 1.31% to 0.82% and 13.97 bpm to 6.87 bpm.