Time-frequency analysis of heart-rate variability

Vila, José L.; Palácios, Francisco; Presedo, J.; Fernández-Delgado, Manuel; Félix, Paulo; Barro, Senén

doi:10.1109/51.620503

Cited by 82 publications

(41 citation statements)

References 17 publications

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“…We developed a custom algorithm to detect the BVP peaks in the interpolated signal and applied it to obtain the interbeat intervals (IBIs). To avoid inclusion of artifacts, such as ectopic beats or motion, the IBIs were filtered using the noncausal of variable threshold (NC-VT) algorithm [18] with a tolerance of 30%. HR was calculated from the mean of the IBI time series as 60/IBI.…”

Section: Quantification Of Physiological Parametersmentioning

confidence: 99%

Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

Poh

McDuff

Picard

2011

IEEE Trans. Biomed. Eng.

1,255

833

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Abstract-We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.Index Terms-Autonomic nervous system, blood volume pulse (BVP), heart rate variability (HRV), independent component analysis (ICA), noncontact, photoplethysmography (PPG), remote sensing, respiration.

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Section: Quantification Of Physiological Parametersmentioning

confidence: 99%

Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

Poh

McDuff

Picard

2011

IEEE Trans. Biomed. Eng.

1,255

833

View full text Add to dashboard Cite

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“…HRV dynamics have been addressed in many studies and several time-varying methods have been proposed for estimating changes in HRV spectra; for a good review on these methods and their properties see Chan et al 10 and Mainardi. 25 These methods include short time Fourier transform (STFT) and wavelet transform, 22,33,43 time-frequency distributions such as the Wigner distribution, 26,29,34,42 and time-varying autoregressive (AR) modeling based methods. 7,8,37 In addition, one fairly recent approach to model the dynamics of HRV is the point process model, 4 which has been applied e.g., for dynamic estimation of RSA component and baroreflex sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Dynamics During Low-Dose Propofol and Dexmedetomidine Anesthesia

et al. 2012

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Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

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“…The spectral analysis of heart rate variability (HRV) is an established non-invasive method for quantitative evaluation of autonomic activity [11][12][13]. From the spectral analysis, several parameters can be calculated including power in the low-frequency (LF) band (0.04-0.15 Hz), representing mainly sympathetic activity, power in the high frequency (HF) band (0.15-0.5 Hz), representing parasympathetic (vagal) activity, and the LF-to-HF ratio (LF/HF), which is a reliable indicator of sympathovagal balance [14,15].…”

Section: Introductionmentioning

confidence: 99%

The Effect of a Meal and Water Loading on Heart Rate Variability in Children with Functional Dyspepsia

et al. 2009

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Time-frequency analysis of heart-rate variability

Cited by 82 publications

References 17 publications

Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

Heart Rate Variability Dynamics During Low-Dose Propofol and Dexmedetomidine Anesthesia

The Effect of a Meal and Water Loading on Heart Rate Variability in Children with Functional Dyspepsia

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