Optimizing MLP Classifier and ECG Features for Sleep Apnea Detection.

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.

Keywords

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Optimizing MLP Classifier and ECG Features for Sleep Apnea Detection.

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.

Keywords

• Elektrokardiyogram, kalp atım hızı değişikliği, uyku apnesi, çok katmanlı algılayıcı, sınıflandırma, ilinti öznitelik seçimi