Noise-Level Aware Compressed Analysis Framework for Robust Electrocardiography Signal Monitoring

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 5; pp. 2243 - 2254
• Main Authors: Lo, Yi-Cheng, Beh, Win-Ken, Huang, Chiao-Chun, Wu, An-Yeu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Atrial Fibrillation - diagnosis; Classification; compressed analysis; compressed sensing; Costs; EKG; Electrocardiography; Electrocardiography - methods; Electrocardiography signal telemonito- ring; Feature extraction; Humans; Machine learning; Monitoring; Monitoring systems; Monitoring, Physiologic; Noise level; Noise levels; noise-level awareness; Robustness; Signal detection; Signal monitoring; Signal Processing, Computer-Assisted; Signal to noise ratio; Singular value decomposition; System effectiveness; Training; Transfer learning
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2022.3199910

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.