智能健康住宅监护数据的分析方法

Analytical Method of Monitoring Data for Health Smart Home

智能健康住宅为缓解人口老龄化的压力和优化医疗资源配置提供了重要手段,其特点是在家庭环境下提供各项生命体征参数监测,并对监护数据自动分析处理.文中提出了以时序建模为基础的智能健康住宅监护数据分析方法.此方法由3个部分构成,包括模型辨识、模型更新以及基于模型的预报区间确定.模型阶数基于最终预报准则确定,使得模型能更好地符合观测数据.基于自适应滤波器算法的模型参数在线更新,能确保模型更好地描述监护数据的动态特性.根据建模结果,作前向30步预报,可确定预报区间.据此,可实现对监护数据中的平稳值、异常值以及状态变化3种特征模式的识别.通过应用PhysioNet中的3组数据集进行实验研究,发现文中方法的预报结果准确,能实现对智能健康住宅中连续监测数据的在线分析.

The health smart home（HSH） is an important measure to ease up the social pressure caused by the aging population and to optimize the allocation of medicine recourses.In an HSH,the vital parameters of a patient can be monitored and automatically analyzed at home.This paper presents an analytical method of monitoring data based on the time series modeling.Three modules of the proposed method,namely the model identification,the model adjustment and the prediction interval（PI） determination are discussed.In the proposed method,the model order is determined according to the final prediction error（FPE） criterion,thus ensuring the model to accord well with the monitoring data.The model parameters are adjusted on line,based on adaptive filter algorithms,thus facilitating the model to describe the dynamic features of monitoring data in a better way.The interval of 30-step-forward prediction is computed according to the modeling results,thus implementing the recognition of the characteristic patterns with stable data,outlier data and state change,respectively.Moreover,three datasets in PhysioNet biomedicine database are used to perform an experimental investigation.The results indicate that the proposed method can analyze the continuous monitoring data on line with high accuracy.