6533b860fe1ef96bd12c31d5
RESEARCH PRODUCT
Quantifying the complexity of short-term heart period variability through K nearest neighbor local linear prediction
Luca FaesSilvia ErlaGiandomenico Nollosubject
Series (mathematics)Degree (graph theory)Computer Science Applications1707 Computer Vision and Pattern Recognitionk-nearest neighbors algorithmTerm (time)Nonlinear systemPosition (vector)Control theorySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaComputer Science Applications1707 Computer Vision and Pattern Recognition; Cardiology and Cardiovascular MedicineTime seriesPredictabilityCardiology and Cardiovascular MedicineAlgorithmMathematicsdescription
The complexity of short-term heart period (HP) variability was quantified exploiting the paradigm that associates the degree of unpredictability of a time series to its dynamical complexity. Complexity was assessed through k-nearest neighbor local linear prediction. A proper selection of the parameter k allowed us to perform either linear or nonlinear prediction, and the comparison of the two approaches to infer the presence of nonlinear dynamics. The method was validated on simulations reproducing linear and nonlinear time series with varying levels of predictability. It was then applied to HP variability series measured from healthy subjects during head-up tilt test, showing that short-term HP complexity increases significantly from the supine to the upright position, and that nonlinearities are involved in the generation of HP dynamics in both positions.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2008-09-01 |