pectral analysis of photoplethysmographic signals : he importance of preprocessing

Heart rate variability (HRV) is an important and useful index to assess the responses of the autonomic nervous system (ANS). HRV analysis is performed using electrocardiography (ECG) or photoplethysmography (PPG) signals which are typically subject to noise and trends. Therefore, the elimination of these undesired conditions is very important to achieve reliable ANS activation results. The purpose of this study was to analyze and compare the effects of preprocessing on the spectral analysis of HRV signals obtained from PPG waveform. Preprocessing consists of two stages: filtering and detrending. The performance of linear Butterworth filter is compared with nonlinear weighted Myriad filter. After filtering, two different approaches, one based on least squares fitting and another on smoothness priors, were used to remove trends from the HRV signal. The results of two filtering and detrending methods were comyriad filtering etrending pectral analysis pared for spectral analysis accomplished using periodogram, Welch’s periodogram and Burg’s method. The performance of these methods is presented graphically and the importance of preprocessing clarified by comparing the results. Although both filters have almost the same performance in the results, the smoothness prior detrending approach was found more successful in removing trends that usually appear in the low frequency bands of PPG signals. In conclusion, the results showed that trends in PPG signals are altered during spectral analysis and must be removed prior to HRV analysis. Introduction Photoplethysmography (PPG) is a simple, noninvasive and useul technique that detects blood volume changes in the blood essels by optical methods. The PPG sensor consists of an infrared mitter, which passes light through the blood vessels, and a detecor, which detects light reflected from the vessels. Typically the mitter and detector are located in a transducer placed on the finer or earlobe [1]. The PPG waveform reflects fluctuations in blood olume and is synchronized with the beating of the heart [2,3]. Because the PPG signal contains information about heart rate nd heart rate variability (HRV), it can be used instead of the ECG ignal in analysis. HRV is defined as the variation of ECG R-wave ntervals (RR) with respect to time and is an important index used in utonomic nervous system (ANS) analysis [3–5]. For the PPG signal, eak-to-peak (PP) intervals could replace the RR intervals detected rom ECG signal [6,7] in HRV analysis. Many studies [2,8–10] verify ∗ Corresponding author at: Institute of Biomedical Engineering, Fatih University, 4500 Istanbul, Turkey. Tel. : +90 212 8663300x2643; fax: +90 212 8663412. E-mail address: saimeakar@fatih.edu. tr (S. Akdemir Akar). 746-8094/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. ttp://dx.doi. org/10.1016/j.bspc. 2012.04. 002 © 2012 Elsevier Ltd. All rights reserved. the high correlation between the RR intervals obtained from ECG signals and PP intervals obtained from PPG signals. Although PPG signals are more simple and useful than ECG signals, difficulties in analysis arise due to noise usually caused by motion artifacts and a quasi DC signal component that corresponds to the changes in the venous pressure [7]. Therefore, HRV waveforms obtained from PPG signals include both noise and slowly changing trends from this DC component [4,7]. These undesired components must be removed due to their detrimental effect on the spectral analysis results of HRV. Spectral analysis of HRV gives information about ANS activity. While the low frequency band reflects sympathetic and parasympathetic activity, the high frequency band is related to parasympathetic activity [11]. The power spectral density (PSD) is a spectral analysis method to find the power distribution over a frequency band contained in a signal. In several studies [6,7], nonparametric and parametric PSD estimation methods are applied to HRV data obtained from PPG signals to interpret variations of ANS. Because the PPG signal includes various sources of noise, such as the patient’s motion or respiration, both linear and adaptive filters have been used by researchers for removing these artifacts from PPG signals [12–14]. To remove slow non-stationary trends in the signal, various methods have been proposed [15–18]. While