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Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Method<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and <jats:italic>F<\/jats:italic> 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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12911-019-1012-8","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T15:02:37Z","timestamp":1577718157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals"],"prefix":"10.1186","volume":"19","author":[{"given":"Xiaomao","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1571-5394","authenticated-orcid":false,"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwok Leung","family":"Tsui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"issue":"1","key":"1012_CR1","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s41118-017-0018-2","volume":"73","author":"I Kashnitsky","year":"2017","unstructured":"Kashnitsky I, de Beer J, van Wissen L. 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