Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals

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 fashi...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. 64; no. 9; pp. 2196 - 2205
Main Authors: Gogna, Anupriya, Majumdar, Angshul, Ward, Rabab
Format: Journal Article
Language:English
Published: United States IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Arrhythmia
Body area network (BAN)
Classification
Compressed sensing
Correlation
Data processing
deep learning
EEG
EKG
Electrocardiography - methods
Electroencephalography
Image reconstruction
Information Storage and Retrieval - methods
Monitoring
Pattern Recognition, Automated - methods
Real time operation
Reconstruction
Signal analysis
Signal classification
Signal Processing, Computer-Assisted
Supervised Machine Learning
ISSN:0018-9294, 1558-2531
Online Access:Get full text
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Summary: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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ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2016.2631620