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

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Listed:
  • 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)

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.

Suggested Citation

  • Yang Zhao & Fan Xu & Xiaomao Fan & Hailiang Wang & Kwok-Leung Tsui & Yurong Guan, 2022. "Prediction of Wellness Condition for Community-Dwelling Elderly via ECG Signals Data-Based Feature Construction and Modeling," IJERPH, MDPI, vol. 19(17), pages 1-16, September.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:11136-:d:907300
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    References listed on IDEAS

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    2. Kashnitsky, Ilya & de Beer, Joop & van Wissen, Leo, 2017. "Decomposition of regional convergence in population aging across Europe," OSF Preprints ykqbv, Center for Open Science.
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