Energy based feature extraction for classification of sleep apnea syndrome

Authors:
Necmettin Sezgin;M. Emin Tagluk
Affiliations:
Department of Electrical and Electronics Eng., University of Batman, Batman, Turkey;Department of Electrical and Electronics Eng., University of Inonu, Malatya, Turkey
Venue:
Computers in Biology and Medicine
Year:
2009

Citing 6
Cited 1

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Fundamentals of neural networks: architectures, algorithms, and applications
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Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets + Neural Networks

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A mixture of experts for classifying sleep apneas

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Abstract

In this paper it is aimed to classify sleep apnea syndrome (SAS) by using discrete wavelet transforms (DWT) and an artificial neural network (ANN). The abdominal and thoracic respiration signals are separated into spectral components by using multi-resolution DWT. Then the energy of these spectral components are applied to the inputs of the ANN. The neural network was configured to give three outputs to classify the SAS situation of the subject. The apnea can be mainly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however, there are no respiratory efforts. In this paper we aim to classify sleep apnea in one of three basic types: obstructive, central and mixed. A significant result was obtained.