Prediction of Wellness Condition for Community-Dwelling Elderly via ECG Signals Data-Based Feature Construction and Modeling

Yang Zhao, Fan Xu (), Xiaomao Fan, Hailiang Wang, Kwok-Leung Tsui and Yurong Guan
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Yang Zhao: School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China
Fan Xu: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Xiaomao Fan: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518000, China
Hailiang Wang: School of Design, The Hong Kong Polytechnic University, Hong Kong, China
Kwok-Leung Tsui: Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Yurong Guan: Department of Computer Science, Huanggang Normal University, Huanggang 438000, China

IJERPH, 2022, vol. 19, issue 17, 1-16

Abstract: 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.

Keywords: electrocardiogram; elderly care; wellness; feature construction; predictive models; machine learning methods (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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