Key result
A deep learning-based BiLSTM model using single-lead short ECG signals forecasted one-day-forward wellness conditions in community-dwelling elderly with 93.21% accuracy and an F-score of 91.98%, outperforming traditional machine learning methods.
Why the study?
The accelerated growth of the elderly population burdens healthcare systems, prompting the need for effective approaches using ECG analysis to monitor personalized health conditions.
Do deep learning-based methods improve the forecasting accuracy of one-day-forward wellness conditions using single lead short ECG signals in community-dwelling elderly compared to traditional machine learning methods?
Do deep learning-based methods improve the forecasting accuracy of one-day-forward wellness conditions using single lead short ECG signals in community-dwelling elderly compared to traditional machine learning methods?
Deep learning-based analysis of single-lead short ECG signals can accurately forecast one-day-forward wellness conditions in community-dwelling elderly, outperforming traditional machine learning methods.
May support remote wellness monitoring via DL ECG analysis in elderly; hypothesis-generating pending prospective validation.
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.
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Fan et al. (2019) studied Wellness conditions in community-dwelling elderly (n=11). Deep learning-based forecasting models (BiLSTM) vs. Traditional machine learning-based methods (ANN, SVM) was evaluated on Forecasting accuracy for one-day-forward wellness conditions. A deep learning-based BiLSTM model using single-lead short ECG signals forecasted one-day-forward wellness conditions in community-dwelling elderly with 93.21% accuracy and an F-score of 91.98%, outperforming traditional machine learning methods.
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