Point-Process Nonlinear Models With Laguerre and Volterra Expansions: Instantaneous Assessment of Heartbeat Dynamics

Valenza, Gaetano; Citi, Luca; Scilingo, Enzo Pasquale; Barbieri, Riccardo

doi:10.1109/tsp.2013.2253775

Cited by 78 publications

(197 citation statements)

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“…Within this framework, Laguerre expansions of the Wiener-Volterra linear and nonlinear autoregressive terms account for long-term nonlinear information [36][37][38]. As major advantages, instantaneous measures can be estimated without applying any interpolation techniques to the RR interval series, and are associated to effective goodness-of-fit measures.…”

Section: Point-process Model Of the Heartbeatmentioning

confidence: 99%

“…The coefficients , with , and with correspond to the time-varying zero-, first-, second-order NARL coefficients, respectively, and performing the Laguerre expansion on the derivative R-R series improves stationarity within the sliding time window (in this work we have chosen s) [15,37]. The corresponding nonlinear autoregressive Volterra-Wiener long-term memory model with second-order nonlinearity becomes [27]: (5) As is defined in continuous time, it is possible to obtain an instantaneous R-R mean estimate at arbitraty timescales without interpolating between the arrival times of two consecutive heartbeats.…”

Section: Point-process Model Of the Heartbeatmentioning

confidence: 99%

“…In order to provide quantitative tools related to standard measures defined in the time and frequency domains, higher order spectral representations, and complexity, it is necessary to link the NARL model to the traditional input-output Wiener-Volterra model [37]. Just like for any linear autoregressive model one can define equivalent infinite-memory moving average model, a quadratic NARL model can be linked to an input-output Volterra model, driven by the same NARL noise term.…”

Section: Instantaneous Time Frequency and Higher-order Spectral Anamentioning

confidence: 99%

“…In particular, given the input-output Volterra kernels of the NARL model for the instantaneous R-R interval mean , we can compute the time-varying parametric (linear) autospectrum [37]. with their ratio (LF/HF).…”

Section: Instantaneous Time Frequency and Higher-order Spectral Anamentioning

confidence: 99%

“…To this end, we employ our instantaneous inhomogeneous point-process framework [36][37][38], which augments standard ANS-related HRV metrics defined in the time and frequency domains [5] with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) [36,38] and higher-order statistics (bispectra) [37].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of spontaneous cardiovascular oscillations in Parkinson's disease

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Biomedical Signal Processing and Control

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Parki so 's disease PD has ee reported to i ol e postga glio i s patheti failure and a wide spectrum of autonomic dysfunctions including cadiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population.Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains.3

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Section: Point-process Model Of the Heartbeatmentioning

confidence: 99%