School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China.
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.
Int J Environ Res Public Health. 2022 Sep 5;19(17):11136. doi: 10.3390/ijerph191711136.
The accelerated growth of elderly populations in many countries and regions worldwide is creating a major burden to the healthcare system. Intelligent approaches for continuous health monitoring have the potential to promote the transition to more proactive and affordable healthcare. Electrocardiograms (ECGs), collected from portable devices, with noninvasive and cost-effective merits, have been widely used to monitor various health conditions. However, the dynamic and heterogeneous pattern of ECG signals makes relevant feature construction and predictive model development a challenging task. In this study, we aim to develop an integrated approach for one-day-forward wellness prediction in the community-dwelling elderly using single-lead short ECG signal data via multiple-features construction and predictive model implementation. Vital signs data from the elderly were collected via station-based equipment on a daily basis. After data preprocessing, a set of features were constructed from ECG signals based on the integration of various models, including time and frequency domain analysis, a wavelet transform-based model, ensemble empirical mode decomposition (EEMD), and the refined composite multiscale sample entropy (RCMSE) model. Then, a machine learning based predictive model was established to map the l-day lagged features to wellness condition. The results showed that the approach developed in this study achieved the best performance for wellness prediction in the community-dwelling elderly. In practice, the proposed approach could be useful in the timely identification of elderly people who might have health risks, and could facilitating decision-making to take appropriate interventions.
全球许多国家和地区的老年人口增长迅速，这给医疗保健系统带来了重大负担。智能的连续健康监测方法有可能促进向更积极主动和更经济实惠的医疗保健转变。从便携式设备中采集的心电图（ECG）具有非侵入性和成本效益高的优点，已被广泛用于监测各种健康状况。然而，心电图信号的动态和异质模式使得相关特征构建和预测模型开发成为一项具有挑战性的任务。在这项研究中，我们旨在开发一种综合方法，通过多特征构建和预测模型实现，使用单导联短 ECG 信号数据对社区居住的老年人进行一天的前瞻性健康预测。老年人的生命体征数据通过基于站点的设备每天收集。在数据预处理之后，根据各种模型的集成，从心电图信号中构建了一组特征，包括时域和频域分析、基于小波变换的模型、集合经验模态分解（EEMD）和改进的综合多尺度样本熵（RCMSE）模型。然后，建立了基于机器学习的预测模型，将一天滞后的特征映射到健康状况。结果表明，本研究中开发的方法在社区居住的老年人健康预测方面表现最佳。在实践中，所提出的方法可用于及时识别可能存在健康风险的老年人，并有助于做出适当干预的决策。
