Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals.

Journal: BMC medical informatics and decision making

Published Date: Dec 30, 2019

Abstract

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.

Authors

Xiaomao Fan
Yang Zhao

The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
Hailiang Wang

Center for System Informatics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China.
Kwok Leung Tsui

School of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China.

Keywords

Aged Deep Learning Electrocardiography Forecasting Health Status Humans Independent Living Machine Learning Models, Theoretical

External Resources

View on PubMed Access via DOI PubMed (31888608)

Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals.

Abstract

Authors

Keywords

External Resources

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