Castaldo Rossana, Melillo Paolo, Izzo R, De Luca N, Pecchia Leandro
IEEE J Biomed Health Inform. 2017 Mar;21(2):399-406. doi: 10.1109/JBHI.2016.2543960. Epub 2016 Mar 18.
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.
跌倒在晚年是一个重大问题，对生活质量有严重影响，在西方国家是一个沉重负担。人们已经提出了许多技术解决方案来评估跌倒风险或预测跌倒，其中大多数基于加速度计和陀螺仪。然而，在识别首次跌倒者方面所做的工作非常少，而首次跌倒者很难被识别出来。本文提出了一种元模型，通过在基线时获取的短期心率变异性（HRV）分析来预测跌倒。研究了约170名高血压患者（年龄：72±8岁，56名女性），其中34人在基线评估后的3个月内跌倒过一次。本研究聚焦于高血压患者，将其视为方便实用的样本，因为他们定期门诊就诊，在此期间可以轻松记录短期心电图（ECG），而不会显著增加医疗成本。对于每个受试者，从10:30至12:30记录的心电图中提取11个连续的5分钟片段（共55分钟）并进行分析。提取线性和非线性HRV特征，并在这11个片段中求平均值，然后将其用于统计和数据挖掘分析。最佳预测元模型基于多项式朴素贝叶斯，其预测首次跌倒者的灵敏度、特异度和准确率分别为72%、61%和68%。
