Journal ArticleDOI
Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals
TLDR
It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation, but in extreme situations the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators.Abstract:
This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. The authors have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals the authors have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.read more
Citations
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Journal ArticleDOI
Detecting stress during real-world driving tasks using physiological sensors
TL;DR: The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level, indicating that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring.
Journal ArticleDOI
A dynamical model for generating synthetic electrocardiogram signals
TL;DR: A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals and may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.
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Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure
TL;DR: The relationship between rPTT, preejection period (PEP; the R-wave/mechanical cardiac delay), and BP would vary with different vasoactive drugs, but the relationship is not reliable enough to be used as a surrogate marker of SBP, although it may be useful in assessing BP variability.
Journal ArticleDOI
Two distinct neuronal networks mediate the awareness of environment and of self
Audrey Vanhaudenhuyse,Athena Demertzi,Manuel Schabus,Quentin Noirhomme,Serge Brédart,Mélanie Boly,Christophe Phillips,Andrea Soddu,André Luxen,Gustave Moonen,Steven Laureys +10 more
TL;DR: A significant anticorrelation between external and internal awareness is found with a mean switching frequency of 0.05 Hz (range: 0.01–0.1 Hz), which is similar to BOLD fMRI slow oscillations.
References
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Journal ArticleDOI
Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control
TL;DR: It is shown that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz.