Format

Send to

Choose Destination
IEEE Trans Biomed Eng. 2017 Sep;64(9):2196-2205. doi: 10.1109/TBME.2016.2631620. Epub 2016 Nov 22.

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

Abstract

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.

PMID:
27893378
DOI:
10.1109/TBME.2016.2631620
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for IEEE Engineering in Medicine and Biology Society
Loading ...
Support Center