scholarly journals Comparison of nonparametric and parametric methods for time-frequency heart rate variability analysis in a rodent model of cardiovascular disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242147
Author(s):  
Emily M. Wong ◽  
Fern Tablin ◽  
Edward S. Schelegle
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fourier Transform ◽  
Ozone Exposure ◽  
Heart Rate Variability Analysis ◽  
Time Varying ◽  
Autoregressive Modeling ◽  
Short Time Fourier Transform ◽  
Modeling Methods ◽  
Short Time

The aim of time-varying heart rate variability spectral analysis is to detect and quantify changes in the heart rate variability spectrum components during nonstationary events. Of the methods available, the nonparametric short-time Fourier Transform and parametric time-varying autoregressive modeling are the most commonly employed. The current study (1) compares short-time Fourier Transform and autoregressive modeling methods influence on heart rate variability spectral characteristics over time and during an experimental ozone exposure in mature adult spontaneously hypertensive rats, (2) evaluates the agreement between short-time Fourier Transform and autoregressive modeling method results, and (3) describes the advantages and disadvantages of each method. Although similar trends were detected during ozone exposure, statistical comparisons identified significant differences between short-time Fourier Transform and autoregressive modeling analysis results. Significant differences were observed between methods for LF power (p ≤ 0.014); HF power (p ≤ 0.011); total power (p ≤ 0.027); and normalized HF power (p = 0.05). Furthermore, inconsistencies between exposure-related observations accentuated the lack of agreement between short-time Fourier Transform and autoregressive modeling overall. Thus, the short-time Fourier Transform and autoregressive modeling methods for time-varying heart rate variability analysis could not be considered interchangeable for evaluations with or without interventions that are known to affect cardio-autonomic activity.

Quantifiying the Influence of Moving Artifact on the Determination of Pulse Rate Variability (PRV) from the Pulse Oximetry (SpO2) Signal Measurements

2015 ◽  
Vol 771 ◽  
pp. 204-208
Author(s):  
Sumber ◽  
Aulia Nasution
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Cardiac Activity ◽  
Short Time Fourier Transform ◽  
Finger Movements ◽  
Pulse Rate Variability ◽  
Movement Artifacts ◽  
Short Time

Determination of Heart Rate Variability (HRV) derived from the Pulse Rate Variability (PRV) of the SpO2 signals measurement can be used to monitor cardiac activity. One disadvantage of the use of SpO2 probe is due to existence unavoidable movement artifacts. These artifacts tend to reduce the accuracy of PRV determination. In order to quantify the influence of moving artifacts on the measured SpO2 signals, the Short-time Fourier Transform (STFT) method is used and this has not been done in previous studies. This method is regarded to be suitable since the artifacts only occurs momentarily, i.e. as the finger moves. Three modes of finger movements were simulated, in addition to the still finger as a control, i.e. in direction of up-down, left-right, and rotating one. Contributing spectra from each of these movements will be recognized, and suitable filtering schemes are then being applied to suppress the influence of these moving artifacts. Parallelly measurements using three-leads ECG were also done to determine the HRV for each of the finger movements condition. Results show that by implementing filtering scheme to each mode of finger movements may reduce the error rate in HRV determination from SpO2 measurements, i.e. from 6 - 25 % (without filtering) to be only 0 - 1.56 %. Meanwhile measurements both HRV and PRV under still finger show only 0-3.33 % difference for each of data groups.

scholarly journals Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

1999 ◽  
Vol 42 (3) ◽  
Author(s):  
T. Bartosch ◽  
D. Seidl
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Array Data ◽  
Definition Of ◽  
Short Time

Among a variety of spectrogram methods Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were selected to analyse transients in non-stationary tremor signals. Depending on the properties of the tremor signal a more suitable representation of the signal is gained by CWT. Three selected broadband tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli.

scholarly journals DISCRIMINATION BETWEEN HEALTHY AND SICK CARDIAC AUTONOMIC NERVOUS SYSTEM BY DETRENDED HEART RATE VARIABILITY ANALYSIS

Fractals ◽  
1999 ◽  
Vol 07 (01) ◽  
pp. 85-91 ◽  
Author(s):  
Y. ASHKENAZY ◽  
M. LEWKOWICZ ◽  
J. LEVITAN ◽  
S. HAVLIN ◽  
K. SAERMARK ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Healthy Subjects ◽  
Heart Rate Variability Analysis ◽  
Fluctuation Analysis ◽  
Cardiac Autonomic Nervous System ◽  
Data Points ◽  
Short Time

Multiresolution Wavelet Transform and Detrended Fluctuation Analysis have recently been proven to be excellent methods in the analysis of Heart Rate Variability and in distinguishing between healthy subjects and patients with various dysfunctions of the cardiac nervous system. We argue that it is possible to obtain a distinction between healthy subjects/patients of at least similar quality by, first, detrending the time-series of RR-intervals by subtracting a running average based on a local window with a length of around 32 data points, then calculating the standard deviation of the detrended time-series. The results presented here indicate that the analysis can be based on very short time-series of RR-data (7–8 minutes), which is a considerable improvement relative to 24-hour Holter recordings.

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