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 recognize. This paper presents a metamodel predicting falls using short term Heart Rate Variability (HRV) analysis acquired at the baseline. About 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 Electrocardiogram (ECG) can be easily recorded without significant increase of healthcare costs. For each subject, 11 consecutive excerpts of 5 min 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 metamodel was based on Multinomial Naïve Bayes, which enabled to predict first-time fallers with sensitivity, specificity, and accuracy rates of 72%, 61%, and 68%, respectively.
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