BACKGROUND The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions. METHOD In this study, we present a novel approach to forecasting one-day-forward wellness conditions for community-dwelling elderly by analyzing single lead short ECG signals acquired from a station-based monitoring device. More specifically, exponentially weighted moving-average (EWMA) method is employed to eliminate the high-frequency noise from original signals at first. Then, Fisher-Yates normalization approach is used to adjust the self-evaluated wellness score distribution since the scores among different individuals are skewed. Finally, both deep learning-based and traditional machine learning-based methods are utilized for building wellness forecasting models. RESULTS The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F value are 93.21% and 91.98% respectively. The deep learning-based methods, with the merit of non-hand-crafted engineering, have superior wellness forecasting performance towards the competitive traditional machine learning-based methods. CONCLUSION The developed approach in this paper is effective in wellness forecasting for community-dwelling elderly, which can provide insights in terms of implementing a cost-effective approach to informing healthcare provider about health conditions of elderly in advance and taking timely interventions to reduce the risk of malignant events.

中文翻译：

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使用单导联短心电图信号预测社区居住老年人的一日健康状况。

背景技术老年人口的加速增长在许多发达国家和地区给医疗保健系统造成沉重负担。心电图（ECG）分析已被认为是诊断心血管疾病的有效方法，并已广泛用于监测个性化健康状况。方法在这项研究中，我们提出了一种新颖的方法，通过分析从基于站的监测设备获取的单个铅短ECG信号，来预测社区居住的老年人的一日前健康状况。更具体地说，首先采用指数加权移动平均（EWMA）方法从原始信号中消除高频噪声。然后，Fisher-Yates归一化方法用于调整自我评估的健康评分分布，因为不同个体之间的评分存在偏差。最后，基于深度学习的方法和基于传统机器学习的方法均用于构建健康预测模型。结果实验结果表明，基于深度学习的方法具有最佳的拟合预测性能，其预测准确度和F值分别为93.21％和91.98％。基于深度学习的方法具有非手工工程的优点，与基于竞争的传统基于机器学习的方法相比，具有出色的健康预测性能。结论本文中开发的方法可以有效地预测社区居民的健康状况，