title: Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects.
abstract: Accidental falls are a major problem of later life. Different technologies to predict falls have been investigated, but with limited success, mainly because of low specificity due to a high false positive rate. This Letter presents an automatic classifier based on heart rate variability (HRV) analysis with the goal to identify fallers automatically. HRV was used in this study as it is considered a good estimator of autonomic nervous system (ANS) states, which are responsible, among other things, for human balance control. Nominal 24 h electrocardiogram recordings from 168 cardiac patients (age 72 ± 8 years, 60 female), of which 47 were fallers, were investigated. Linear and nonlinear HRV properties were analysed in 30 min excerpts. Different data mining approaches were adopted and their performances were compared with a subject-based receiver operating characteristic analysis. The best performance was achieved by a hybrid algorithm, RUSBoost, integrated with feature selection method based on principal component analysis, which achieved satisfactory specificity and accuracy (80 and 72%, respectively), but low sensitivity (51%). These results suggested that ANS states causing falls could be reliably detected, but also that not all the falls were due to ANS states.
authors.affiliation: Multidisciplinary Department of Medical, Surgical and Dental Sciences , Second University of Naples , Via S. Pansini, 5 , Naples 80138 , Italy ; SHARE Project , Italian Ministry of Education , Scientific Research and University , Rome , Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Melillo
authors.affiliation: Faculty of Electrical Engineering and Computing , University of Zagreb , Unska 3 , HR-10000 Zagreb , Croatia.
authors.forename: Alan
authors.initials: A
authors.lastname: Jovic
authors.affiliation: Department of Translational Medical Sciences , University of Naples Federico II , Via S. Pansini, 5 , Naples 80138 , Italy.
authors.forename: Nicola
authors.initials: N
authors.lastname: De Luca
authors.affiliation: School of Engineering , University of Warwick , Coventry CV4 7AL , UK.
authors.forename: Leandro
authors.initials: L
authors.lastname: Pecchia
keywords: RUSBoost; accidental falls; automatic classifier; autonomic nervous system states; cardiac patients; data mining; electrocardiogram recordings; electrocardiography; feature selection; feature selection method; heart rate variability; high false positive rate; human balance control; hypertensive subjects; linear HRV properties; medical signal processing; neurophysiology; nonlinear HRV properties; principal component analysis; sensitivity analysis; signal classification; subject-based receiver operating characteristic analysis; time 30 min