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, Yurong Guan

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Article number11136
JournalInternational Journal of Environmental Research and Public Health
Issue number17
Publication statusPublished - 5 Sept 2022


  • elderly care
  • electrocardiogram
  • feature construction
  • machine learning methods
  • predictive models
  • wellness

ASJC Scopus subject areas

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis


Dive into the research topics of 'Prediction of Wellness Condition for Community-Dwelling Elderly via ECG Signals Data-Based Feature Construction and Modeling'. Together they form a unique fingerprint.

Cite this