- Turkish Journal of Electrical Engineering and Computer Science
- Volume:24 Issue:3
- An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation me...
An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method
Authors : Ahmet Reşit KAVSAOĞLU, Kemal POLAT, Mehmet Recep BOZKURT
Pages : 1782-1796
View : 15 | Download : 11
Publication Date : 0000-00-00
Article Type : Research Paper
Abstract :The purpose of this paper is twofold. The first purpose is to detect M-peaks from raw photoplethysmography insert ignore into journalissuearticles values(PPG); signals with no preprocessing method applied to the signals. The second purpose is to estimate heart rate variability insert ignore into journalissuearticles values(HRV); by finding the peaks in the PPG signal. HRV is a measure of the fluctuation of the time interval between heartbeats and is calculated based on time series between strokes derived from electrocardiogram insert ignore into journalissuearticles values(ECG);, arterial pressure insert ignore into journalissuearticles values(AP);, or PPG signals, separately. PPG is a method widely used to measure blood volume of tissue on the basis of blood volume change in every heartbeat. In the estimation of the HRV signal from the PPG signal, HRV is calculated by measuring the time intervals between the peak values in the PPG signal. In the present paper, a novel peak detection algorithm was developed for PPG signals. Finding peak values correctly from PPG signals, the HRV signal can be estimated. This peak detection algorithm has been called an adaptive segmentation method insert ignore into journalissuearticles values(ASM);. In this method, the PPG signals are first separated into segments with sample sizes and then the peak points in these signals are detected by comparing with maximum points in these segments. To evaluate the estimated pulse rate and HRV signals from PPG, Poincar\`{e} plots and time domain features including minimum, maximum, mean, mode, standard deviation, variance, skewness, and kurtosis values were used. Our experimental results demonstrated that ASM could be even used both in the estimation of HRV signals and to detect the peaks from raw and noisy PPG signals without a pre-processing method.Keywords : Peak detection, heart rate variability, photoplethysmography signal, adaptive segmentation method