Current Proceedings on Technology

Current Proceedings on Technology

Diagnostic Estimation of SAS From Snore Sounds Using Logistic Regression

Yazarlar: Necmettin SEZGİN, M. Emin TAĞLUK, Ramazan TEKİN

Cilt 1 , Sayı - , 2012 , Sayfalar -

Konular:-

Anahtar Kelimeler:Sleep apnea syndrome,Snoring signal,Wavelet transform,Logistic regression.

Özet: Sleep Apnea Syndrome (SAS) is a serious health problem. Early recognition and decision on the treatment method is important. In this study a method concerning patient’s snoring signals for estimation of SAS is proposed. 1335 normal and 1463 abnormal snore episodes were employed and processed for their time-frequency (TF) energy profile through wavelet transform. The TF patterns of the signals were segregated into five sub bunds and seven parameters were derived from each sub band. The [episodes x parameters] data matrices were constructed and the Logistic Regression, a statistical model that explain the relationship between decision and condition features while the decision property is binary, is applied to these data matrices. Through this method SAS was 97.50% correctly estimated which is significantly higher than the performance of other strategies we have studied so far.

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@article{2012, title={Diagnostic Estimation of SAS From Snore Sounds Using Logistic Regression}, volume={1}, number={0}, publisher={Current Proceedings on Technology }, author={Necmettin SEZGİN, M. Emin TAĞLUK, Ramazan TEKİN}, year={2012} }
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Necmettin SEZGİN, M. Emin TAĞLUK, Ramazan TEKİN. (2012). Diagnostic Estimation of SAS From Snore Sounds Using Logistic Regression (Vol. 1). Vol. 1. Current Proceedings on Technology .
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Necmettin SEZGİN, M. Emin TAĞLUK, Ramazan TEKİN. Diagnostic Estimation of SAS From Snore Sounds Using Logistic Regression. no. 0, Current Proceedings on Technology , 2012.
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