期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 64, 期 9, 页码 2196-2205出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2016.2631620
关键词
Body area network (BAN); classification; deep learning; reconstruction
资金
- Qatar National Research Fund (a member of Qatar Foundation) under NPRP [7-684-1-127]
Objective: An autoencoder-based framework that simultaneously reconstruct and classify biomedical signals is proposed. Previous work has treated reconstruction and classification as separate problems. This is the first study that proposes a combined framework to address the issue in a holistic fashion. Methods: For telemonitoring purposes, reconstruction techniques of biomedical signals are largely based on compressed sensing (CS); these are designed techniques where the reconstruction formulation is based on some assumption regarding the signal. In this study, we propose a new paradigm for reconstruction-the reconstruction is learned, using an autoencoder; it does not require any assumption regarding the signal as long as there is sufficiently large training data. But since the final goal is to analyze/classify the signal, the system can also learn a linear classification map that is added inside the autoencoder. The ensuing optimization problem is solved using the Split Bregman technique. Results: Experiments were carried out on reconstructing and classifying electrocardiogram (ECG) (arrhythmia classification) and EEG (seizure classification) signals. Conclusion: Our proposed tool is capable of operating in a semi-supervised fashion. We show that our proposed method is better in reconstruction and more than an order magnitude faster than CS based methods; it is capable of real-time operation. Our method also yields better results than recently proposed classification methods. Significance: This is the first study offering an alternative to CS-based reconstruction. It also shows that the representation learning approach can yield better results than traditional methods that use hand-crafted features for signal analysis.
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