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- W2016306540 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." @default.
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- W2016306540 date "2013-05-01" @default.
- W2016306540 modified "2023-09-24" @default.
- W2016306540 title "A Robust Algorithm for Derivation of Heart Rate Variability Spectra from ECG and PPG Signals" @default.
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- W2016306540 doi "https://doi.org/10.1109/sbec.2013.26" @default.
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