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MLP Sınıflandırıcısı ve EKG Özniteliklerinin Uyku Apnesi Tanısı için Optimizasyonu

Optimizing MLP Classifier and ECG Features for Sleep Apnea Detection

Journal Name:

  • Journal of Naval Sciences And Engineering

Publication Year:

  • 2015

Key Words:

Keywords (Original Language):

Author Name
Abstract (2. Language): 
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.
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Abstract (Original Language): 
Bu çalışmanın amacı otomatik uyku apnesi tanımlamasında daha iyi sınıflandırma sağlamak amacıyla çok katmanlı algılayıcı sınıflandırıcısı ile kullanılacak EKG özniteliklerinin optimizasyonunu gerçekleştirmektir. Uzman hekim tarafından değerlendirilmiş 12 hastanın bulunduğu DREAMS projesi veri tabanından elde edilen PSG kayıtlarındaki apne olgularının sınıflandırılmasında k-kat çapraz doğrulama algoritması kullanılmıştır. Çok katmanlı algılayıcı ile sınıflandırmada en iyi başarımı elde etmek için ilinti öznitelik seçim metodu kullanılmıştır. Apne olgularının tespitinde, sınıflandırıcı ve öznitelik optimizasyonu sonrasında doğrulukta yaklaşık %10, duyarlılıkta %7 ve kesinlikte %13 artış elde edilmiştir.
FULL TEXT (PDF): 
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  • Turkish

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