January 1, 1996IEEE Transactions on Biomedical Engineering
Estimation of the power spectral density in nonstationary cardiovascular time series: assessing the role of the time-frequency representations (TFR)
Key result
Time-frequency representation (TFR) based power spectral estimators showed superiority over classical and autoregressive methods when a sharp time resolution is required for nonstationary series.
Population
Artificial data with typical patterns of nonstationary series and real RR interval time series obtained…
Comparison
Time-frequency representation based power… vs Classical and autoregressive power spectral…
Design
Other
Authors
Discussion
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Implication
TFR estimators may better track autonomic transients than FFT/AR methods; leaves open optimal clinical use pending validation studies.
Structured PICO
P
Population
Artificial data with typical patterns of nonstationary series and real RR interval time series obtained during autonomic tests
I
Intervention
Time-frequency representation (TFR) based power spectral estimators
C
Comparator
Classical (FFT based) and autoregressive (both block and sequential mode) power spectral estimators
O
Outcome
Ability to track transients and provide sharp time resolution in nonstationary cardiovascular time series
Time-frequency representation (TFR) estimators provide superior time resolution for analyzing nonstationary cardiovascular time series, such as RR intervals during autonomic tests, compared to classical and autoregressive methods.
Cite This Study
Pola et al. (1996) studied Cardiovascular time series analysis. Time-frequency representation (TFR) based power spectral estimators vs. Classical (FFT based) and autoregressive estimators was evaluated on Tracking transients in nonstationary cardiovascular series. Time-frequency representation (TFR) based power spectral estimators showed superiority over classical and autoregressive methods when a sharp time resolution is required for nonstationary series.