Name
Abstract | ||
|---|---|---|
Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and, in 25% of patients, autonomic failure. In this work we investigate autonomic dynamics in PD using a novel methodology able to provide instantaneous estimates of the Lyapunov spectrum within a point process framework. Physiological signals were recorded from 10 healthy subjects and 9 cognitively preserved PD patients. We computed conventional heart rate variability (HRV) features as well as the full Lyapunov spectrum over 600s recordings at rest, and tested for significant group effects using a general linear model taking into account age and gender as nuisance covariates. Additionally, the discriminatory power of all features was tested by training a Support Vector Machine (SVM) classifier combined with recursive feature elimination (RFE) with a variable number of target features. The first and second Lyapunov exponents were significantly higher (p<;0.05) in PD patients vs. controls. No other HR V measure difered significantly between groups. The best classification performance (75% sensitivity, 8O% specificity, area under ROC curve O.8) was attained when instructing RFE to retain 2 features, where the algorithm selected the first and second Lyapunov exponents. Our results suggest that autonomic control in PD entails a preponderance of nonlinear, more unstable heartbeat dynamics with respect to controls. This could point to possible autonomic dysfunction which cannot be detected by conventional HRV measures. |
Year | Venue | Keywords |
|---|---|---|
2013 | CinC | lyapunov methods,diseases,electrocardiography,feature extraction,medical signal processing,neurophysiology,plethysmography,sensitivity analysis,signal classification,support vector machines,parkinson's disease,rfe,support vector machine classifier,area under roc curve,autonomic control,autonomic dynamics,autonomic dysfunction,autonomic failure,classification performance,conventional hrv measures,conventional heart rate variability features,discriminatory power,first lyapunov exponents,full lyapunov spectrum,general linear model,healthy subjects,instantaneous estimates,nonlinear preponderance,nuisance covariates,physiological signal,point process framework,postganglionic sympathetic failure,recursive feature elimination,second lyapunov exponents,target features,time 600 s,unstable heartbeat dynamics |
Field | DocType | Volume |
Lyapunov function,Covariate,Heartbeat,General linear model,Heart rate variability,Control theory,Internal medicine,Cardiology,Support vector machine,Electrocardiography,Lyapunov exponent,Mathematics | Conference | 40 |
ISSN | ISBN | Citations |
2325-8861 | 978-1-4799-0884-4 | 2 |
PageRank | References | Authors |
0.58 | 4 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riccardo Barbieri | 1 | 460 | 70.50 |
| luca citi | 2 | 168 | 27.88 |
| Gaetano Valenza | 3 | 314 | 48.21 |
| maria guerrisi | 4 | 11 | 6.95 |
| stefano orsolini | 5 | 3 | 0.98 |
| carlo tessa | 6 | 12 | 2.98 |
| Stefano Diciotti | 7 | 22 | 3.80 |
| nicola toschi | 8 | 36 | 15.57 |