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Optimizing MLP Classifier and ECG Features for Sleep Apnea Detection.

Year 2015, Volume 11, Issue 1, 1 - 18, 20.01.2016

Abstract

The purpose of this study is to optimize multilayer perceptron (MLP) classifier and find optimal ECG features to achieve better classification for automated sleep apnea detection. k-fold crossvalidation technique was employed for classification of apneaic events on the apnea database of the DREAMS project containing 12 whole-night Polysomnography (PSG) recordings previously examined by an expert. To achieve the best possible performance with MLP, the correlation feature selection method was utilized. The performance for apnea event diagnosis after optimization of the features and the classifier resulted almost 10% in accuracy, %7 in sensitivity and %13 in specificity.

Optimizing MLP Classifier and ECG Features for Sleep Apnea Detection.

Year 2015, Volume 11, Issue 1, 1 - 18, 20.01.2016

Abstract

The purpose of this study is to optimize multilayer perceptron (MLP) classifier and find optimal ECG features to achieve better classification for automated sleep apnea detection. K-fold crossvalidation technique was employed for classification of apneaic events on the apnea database of the DREAMS project containing 12 whole-night Polysomnography (PSG) recordings previously examined by an expert. To achieve the best possible performance with MLP, the correlation feature selection method was utilized. The performance for apnea event diagnosis after optimization of the features and the classifier resulted almost 10% in accuracy, %7 in sensitivity and %13 in specificity.

Details

Primary Language English
Journal Section Articles
Authors

Oğuz TİMUŞ This is me


Erkan KIYAK This is me

Publication Date January 20, 2016
Published in Issue Year 2015, Volume 11, Issue 1

Cite

APA Timuş, O. & Kıyak, E. (2016). Optimizing MLP Classifier and ECG Features for Sleep Apnea Detection. . Journal of Naval Sciences and Engineering , 11 (1) , 1-18 . Retrieved from https://dergipark.org.tr/en/pub/jnse/issue/10002/123533