Optimizing MLP Classifier and ECG Features for Sleep Apnea Detection.

Authors : Oğuz TİMUŞ, Erkan KIYAK

Pages : 1-18

View : 14 | Download : 8

Publication Date : 2016-01-20

Article Type : Research Paper

Abstract :The purpose of this study is to optimize multilayer perceptron insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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.
Keywords : Elektrokardiyogram, kalp atım hızı değişikliği, uyku apnesi, çok katmanlı algılayıcı, sınıflandırma, ilinti öznitelik seçimi