Assessment of spontaneous cardiovascular oscillations in Parkinson's disease. (April 2016)

• Record Type: Journal Article
• Authors: Valenza, Gaetano; Orsolini, Stefano; Diciotti, Stefano; Citi, Luca; Scilingo, Enzo Pasquale; Guerrisi, Maria; Danti, Sabrina; Lucetti, Claudio; Tessa, Carlo; Barbieri, Riccardo; Toschi, Nicola
• Is Part Of: Biomedical signal processing and control. Volume 26(2016)
• Page Start: 80
• Page End: 89
• Publication Date: 2016-04
• Subjects: Parkinson's disease -- Heart rate variability -- Autonomic nervous system -- Point process -- Laguerre expansion -- Bispectrum -- Lyapunov exponents -- Support vector machine -- Autonomic dysfunction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.12.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We present a comprehensive assessment of spontaneous cardiovascular oscillations in PD. We combine standard ANS-related HRV metrics with instantaneous complexity and HOS. The time-varying structure of features is essential for classification of PD patients. Feature dynamics significantly correlates with motor and cognitive scores in PD. Abstract: Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, 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.