DocumentCode :
604144
Title :
A Robust Algorithm for Derivation of Heart Rate Variability Spectra from ECG and PPG Signals
Author :
Verma, A. ; Cabrera, S. ; Mayorga, A. ; Nazeran, H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Texas at El Paso, El Paso, TX, USA
fYear :
2013
fDate :
3-5 May 2013
Firstpage :
35
Lastpage :
36
Abstract :
Digital spectral analysis of Heart Rate Variability (HRV) signals provides quantitative markers of the Autonomic Nervous System (ANS) activity, underpinning a variety of physiologic processes. In this investigation, a robust algorithm was developed to derive HRV signals, their FFT- or AR-based spectra and their standard spectral features from either electrocardiogram (ECG) or photoplethysmographic (PPG) signals. ECG/PPG signals were first interpolated to a common sampling rate and detrended using a first order DC-notch IIR high-pass filter to remove very low frequencies. Next, the undecimated Discrete Wavelet Transform (DWT) Daubechies-6 family of filters was used to selectively remove some of the high-frequency subbands from the signals. The filtered ECG/PPG signals were then squared to increase the dynamic range of the target dominant peaks from which accurate peak-to-peak intervals could be extracted. A smart peak-tracking and monitoring algorithm was used to detect the peaks while enforcing a valid trend of the instantaneous heart rate. Once the peak-to-peak intervals were obtained, the HRV signal was determined using cubic spline interpolation to create a signal at a very low sampling rate of 1-4 Hz. Standard Power Spectrum Estimation (PSD) of the HRV signal was then performed to generate normalized LF, HF and LF/HF spectral features to quantify parasympathetic influences and sympathovagal balance.
Keywords :
IIR filters; discrete wavelet transforms; electrocardiography; high-pass filters; interpolation; medical signal processing; neurophysiology; photoplethysmography; signal sampling; spectral analysis; ANS activity; AR-based spectra; DWT; ECG signals; FFT-based spectra; HRV signals; PPG signals; PSD; autonomic nervous system activity; cubic spline interpolation; digital spectral analysis; discrete wavelet transform Daubechies-6 family; electrocardiogram signals; first order DC-notch IIR high-pass filter; heart rate variability signals; heart rate variability spectra; instantaneous heart rate; photoplethysmographic signals; physiologic processes; sampling rate; standard power spectrum estimation; Autonomic nervous system; Discrete wavelet transforms; Electrocardiography; Filtering algorithms; Heart rate variability; IIR filters; Resonant frequency; ECG; PPG; RR Intervals; Heart Rate Variability; Power Spectrum Density; Autonomic Nervous System;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering Conference (SBEC), 2013 29th Southern
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4799-0624-6
Type :
conf
DOI :
10.1109/SBEC.2013.26
Filename :
6525663
Link To Document :
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