A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals

The noninvasive peripheral oxygen saturation (SpO2) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects’ PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.

Wavelet-Based Filtering Method for Sleep EEG Signal

In this paper, in accordance with several common signal interference in sleep EEG detection, it is processed by wavelet transform. It mainly includes: ①.remove white noise from EEG using wavelet threshold method; ②.remove baseline drift from EEG using wavelet decomposition and reconstruction method; ③.remove sharp pulse interference using wavelet modulus maximum algorithm; ④.remove EMG from EEG using wavelet decomposition and reconstruction as well as modulus maximum method. The results of simulation study show that: it can filter a variety of common interference in EEG detection preferably by wavelet transform.