Time-variant power spectrum analysis for the detection of transient episodes in HRV signal

Abstract

A time-variant algorithm of autoregressive (AR) identification is introduced and applied to the heart rate variability (HRV) signal. The power spectrum is calculated from the AR coefficients derived from each single RR interval considered. Time-variant AR coefficients are determined through adaptive parametric identification with a forgetting factor which obtains weighed values on a running temporal window of 50 preceding measurements. Power spectrum density (PSD) is hence obtained at each cardiac cycle, making it possible to follow the dynamics of the spectral parameters on a beat-by-beat basis. These parameters are mainly the LF (low frequency) and the HF (high frequency) powers, and their ratio LF/HF. These together account for the balanced sympatho-vagal control mechanism affecting the heart rate. This method is applied to subjects suffering from transient ischemic attacks. The time variant spectral parameters suggest an early activation of LF component in the HRV power spectrum. It precedes by approximately 1.5-2 min the tachycardia and the ST displacement, generally indicative of the onset of an ischemic episode. The results suggest an arousal of sympathetic system before the acute attack.

Cardiac Autonomic Patterns Preceding Occasional Vasovagal Reactions in Healthy Humans

“…It is based on a recursive least-squares-method algorithm, which makes the autoregressive identification procedure suitable to update the coefficients of the model every new beat. Therefore, power spectral analysis can be performed on a beat-by-beat basis, permitting the evaluation of the changes in the spectral components during unstable conditions like transient ischemia 15 and syncopal events. 17 The forgetting factor weighs the prediction error terms exponentially, thus permitting focus on the most recent data out of the window of interest.…”

“…The principles of the software for data acquisition and time-variant spectral analysis have been escribed elsewhere. [15][16][17] Time-variant spectral analysis of heart period variability represents a development of usual autoregressive power spectral analysis. It is based on a recursive least-squares-method algorithm, which makes the autoregressive identification procedure suitable to update the coefficients of the model every new beat.…”

“…Time-variant autoregressive spectral analysis was used to obtain markers of sympathetic and vagal cardiac modulation on a beat-by-beat basis 15 to evaluate continuously the possible complex and rapid changes in the autonomic control of heart rate before syncope.…”

Abnormal autonomic cardiac response to transient hypoxia in sickle cell anemia

“…These data then were resampled to 2 Hz to match other datasets. Time-varying spectra of RRI were then computed from an autoregressive model (Bianchi et al 1993, Eckberg 1997) using the same method as previously described by our group (Blasi et al 2003b). This method allows a new estimate of the RRI spectrum to be calculated with each successive time step.…”

Cyclic Variation in Heart Rate Score by Holter Electrocardiogram as Screening for Sleep-Disordered Breathing in Subjects With Heart Failure

“…25 A Holter automatic arrhythmia analysis system (SCM-8000, Fukuda Denshi) identified all R-wave positions and excluded abnormal beats, such as ventricular ectopic and supraventricular ectopic beats and artifacts. Moreover, the R-R interval of normal beats that exceeded Ϯ 30% of the average R-R interval was removed from the trend signal.…”