Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals
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 monitor...
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-019-1012-8 |
| _version_ | 1819051923797442560 |
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| author | Xiaomao Fan Yang Zhao Hailiang Wang Kwok Leung Tsui |
| author_facet | Xiaomao Fan Yang Zhao Hailiang Wang Kwok Leung Tsui |
| author_sort | Xiaomao Fan |
| collection | DOAJ |
| description | 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. Method 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. Results The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F 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. Conclusion 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. |
| first_indexed | 2024-12-21T12:11:39Z |
| format | Article |
| id | doaj.art-018bfb7452c54f0989cd67b6ccee337a |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-21T12:11:39Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-018bfb7452c54f0989cd67b6ccee337a2022-12-21T19:04:33ZengBMCBMC Medical Informatics and Decision Making1472-69472019-12-0119111410.1186/s12911-019-1012-8Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signalsXiaomao Fan0Yang Zhao1Hailiang Wang2Kwok Leung Tsui3School of Data Science, City University of Hong KongCenter for System Informatics, City University of Hong KongCenter for System Informatics, City University of Hong KongSchool of Data Science, City University of Hong KongAbstract 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. Method 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. Results The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F 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. Conclusion 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.https://doi.org/10.1186/s12911-019-1012-8Elderly careWellness forecastingData miningDeep learning |
| spellingShingle | Xiaomao Fan Yang Zhao Hailiang Wang Kwok Leung Tsui Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals BMC Medical Informatics and Decision Making Elderly care Wellness forecasting Data mining Deep learning |
| title | Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals |
| title_full | Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals |
| title_fullStr | Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals |
| title_full_unstemmed | Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals |
| title_short | Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals |
| title_sort | forecasting one day forward wellness conditions for community dwelling elderly with single lead short electrocardiogram signals |
| topic | Elderly care Wellness forecasting Data mining Deep learning |
| url | https://doi.org/10.1186/s12911-019-1012-8 |
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