https://scholars.lib.ntu.edu.tw/handle/123456789/631807
標題: | Noise-Level Aware Compressed Analysis Framework for Robust Electrocardiography Signal Monitoring | 作者: | Lo, Yi Cheng Beh, Win Ken Huang, Chiao Chun AN-YEU(ANDY) WU |
關鍵字: | compressed analysis | compressed sensing | Electrocardiography signal telemonito- ring | noise-level awareness | robustness | 公開日期: | 1-五月-2023 | 出版社: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | 卷: | 27 | 期: | 5 | 起(迄)頁: | 2243 | 來源出版物: | IEEE Journal of Biomedical and Health Informatics | 摘要: | Compressed sensing (CS) has drawn much attention in electrocardiography (ECG) signal monitoring for its effectiveness in reducing the transmission power of wireless sensor systems. Compressed analysis (CA) is an improved methodology to further elevate the system's efficiency by directly performing classification on the compressed data at the back-end of the monitoring system. However, conventional CA lacks of considering the effect of noise, which is an essential issue in practical applications. In this work, we observe that noise causes an accuracy drop in the previous CA framework, thus discovering that different signal-to-noise ratios (SNRs) require different sizes of CA models. We propose a two-stage noise-level aware compressed analysis framework. First, we apply the singular value decomposition to estimate the noise level in the compressed domain by projecting the received signal into the null space of the compressed ECG signal. A transfer-learning-aided algorithm is proposed to reduce the long-training-time drawback. Second, we select the optimal CA model dynamically based on the estimated SNR. The CA model will use a predictive dictionary to extract features from the ECG signal, and then imposes a linear classifier for classification. A weight-sharing training mechanism is proposed to enable parameter sharing among the pre-trained models, thus significantly reducing storage overhead. Lastly, we validate our framework on the atrial fibrillation ECG signal detection on the NTUH and MIT-BIH datasets. We show improvement in the accuracy of 6.4% and 7.7% in the low SNR condition over the state-of-the-art CA framework. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/631807 | ISSN: | 21682194 | DOI: | 10.1109/JBHI.2022.3199910 |
顯示於: | 電機工程學系 |
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