https://scholars.lib.ntu.edu.tw/handle/123456789/637716
標題: | Intermittent blood pressure prediction via multiscale entropy and ensemble artificial neural networks | 作者: | Sadrawi, Muammar Shieh, Jiann Shing SHOU-ZEN FAN Lin, Chien Hung Haraikawa, Koichi Chien, Jen Chien Abbod, Maysam F. |
公開日期: | 1-一月-2016 | 來源出版物: | IECBES 2016 - IEEE-EMBS Conference on Biomedical Engineering and Sciences | 摘要: | This study evaluates the correlation between the intermittent blood pressure (BP) and the photoplethysmography (PPG). This study of a total of twenty-five cases is started by the partitioning of the PPG signal into a 5-minute segment. The segmented PPG is filtered by ensemble empirical mode decomposition (EEMD). The feature extraction method, multiscale entropy (MSE) is utilized for the purified signal to achieve some information. The seventy-five scale of MSE is taken into the input of the artificial neural network (ANN) modeling. The outputs of this system are the intermittent diastolic and systolic blood pressure. Originally, thousand models are created. The best model is chosen for the best single ANN model. In advanced, the ensemble artificial neural network (EANN) model is initiated to observe the testing data. The result, compared to the best single ANN model, shows that the EANN model recognizes better the testing data by producing lower mean absolute error (MAE). |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637716 | ISBN: | 9781467377911 | DOI: | 10.1109/IECBES.2016.7843473 |
顯示於: | 醫學系 |
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