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Fall prediction in hypertensive patients via short-term HRV analysis

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Castaldo, Rossana, Melillo, Paolo, Izzo, Raffaele, De Luca, Nicola and Pecchia, Leandro (2017) Fall prediction in hypertensive patients via short-term HRV analysis. IEEE Journal of Biomedical and Health Informatics, 21 (2). pp. 399-406. doi:10.1109/JBHI.2016.2543960

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Official URL: https://doi.org/10.1109/JBHI.2016.2543960

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Abstract

Falls are a major problem of later life having severe consequences on quality of life and a significant burden in occidental countries. Many technological solutions have been proposed to assess the risk or to predict falls and the majority is based on accelerometers and gyroscopes. However, very little was done for identifying first time fallers, which are very difficult to recognise. This paper presents a meta-model predicting falls using short term Heart Rate Variability (HRV) analysis acquired at the baseline. 170 hypertensive patients (age: 72 ± 8 years, 56 female) were investigated, of which 34 fell once in the 3 months after the baseline assessment. This study is focused on hypertensive patients, which were considered as convenient pragmatic sample, as they undergo regular outpatient visits, during which short term ECG can be easily recorded without significant increase of healthcare costs. For each subject, 11 consecutive excerpts of 5 minutes each (55 min) were extracted from ECGs recorded between 10:30 and 12:30 and analysed. Linear and nonlinear HRV features were extracted and averaged among the 11 excerpts, which were, then, considered for the statistical and data mining analysis. The best predictive meta-model was based on Multinomial Naïve Bayes, which enabled to predict first-time fallers with sensitivity, specificity and accuracy rates of 72%, 61%, 68% respectively.

Item Type: Journal Article
Subjects: R Medicine > RC Internal medicine
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): Falls (Accidents) in old age
Journal or Publication Title: IEEE Journal of Biomedical and Health Informatics
Publisher: IEEE
ISSN: 2168-2194
Official Date: 1 March 2017
Dates:
DateEvent
1 March 2017Published
18 March 2016Available
11 March 2016Accepted
Volume: 21
Number: 2
Number of Pages: 8
Page Range: pp. 399-406
DOI: 10.1109/JBHI.2016.2543960
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDSeventh Framework Programme (European Commission) (FP7)UNSPECIFIED
UNSPECIFIEDItalian National Operational Programme for Research and CompetitivenessUNSPECIFIED

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