Destek Vektör Makineleri Kullanarak Uyku Seslerinin Çoklu Sınıflandırılması

Uyku sürekliliği ve uyku hijyeni, insanların günlük yaşantısını doğrudan etkilemektedir. Uyku sırasında ortaya çıkan horlama, öksürme, tıksırma gibi uyanmaya neden olan sesler genellikle uyku hastalıklarıyla ilintilidir. Horlama gibi gürültülü ses paternleri hasta ile aynı ortamda uyuyan diğer insanların da uyku kalitesini olumsuz yönde etkileyebilmektedir. Hastaların fizyolojik sinyalleri ve uyku sesleri polisomnografi ile kayıt edilir. Ardından tüm sonuçlar uzman doktor tarafından incelenir ve sonuçlarına göre uygun teşhis konulur. Görsel veya işitsel skorlama mesleki deneyim gerektiren, oldukça zor, zaman alan ve yorucu bir süreçtir. Bu nedenle, uykudaki seslerin otomatik sınıflandırılması üzerine yapılan çalışmalar önem kazanmaktadır. Sunulan çalışmada, uyku seslerini hızlı ve güvenilir bir şekilde analiz edebilen, otomatik olarak sınıflandırabilen bilgisayar destekli tanı algoritmasının geliştirilmesi amaçlanmıştır. Altı farklı uyku ses paterni (nefes alma/verme, öksürme, basit horlama, dubleks düşük frekans horlama, dubleks yüksek frekans horlama ve tripleks horlama) zaman bölgesinden elde edilen öznitelikler kullanılarak makine öğrenmesine dayanan bir algoritmayla otomatik olarak sınıflandırılmaktadır. Önerilen algoritma üç aşamadan oluşur: Birinci aşamada ham ses sinyallerine kontrol ve ön işleme yapılır. İkinci aşamada dalga formu analizleri yapılarak öznitelikler edilir. Son aşamada ise destek vektör makineleri kullanılarak sınıflandırma işlemi yapılır. Çalışma sonucunda, altı farklı uyku sesi paterni ortalama % 90.20 doğruluk oranıyla sınıflandırılmıştır.

Anahtar Kelimeler:

Uyku Sesleri, Basit Horlama, Dubleks Horlama, Tripleks Horlama, Sınıflandırma

Multi Classification of Sleep Sounds using Support Vector Machines

Sleep continuity and sleep hygiene directly affect people's daily lives. The sounds that cause awakening such as snoring, coughing, and obstruction during sleep are generally related to sleep diseases. Noisy sound patterns, such as snoring, can negatively affect the sleep quality of other people who sleep in the same environment as the patient. Physiological signals and sleep sounds of patients are recorded by polysomnography. Then all results are examined by the sleep physician and appropriate diagnosis is made according to the results. Visual or auditory scoring is a very difficult, time-consuming and tiring process that requires professional experience. Hence, studies on the automatic classification of sleep sounds become important. In the presented study, it is aimed to develop a computer-aided diagnostic algorithm that can analyze sleep sounds quickly and reliably and classify them automatically. Six different sleep sound patterns (breathing / exhaling, coughing, simple snoring, duplex low frequency snoring, duplex high frequency snoring and triplex snoring) are automatically classified with an algorithm based on machine learning using the time-domain features. The proposed algorithm consists of three stages: In the first stage, raw sound signals are checked and pre-processed. In the second stage, features are obtained with waveform analysis. At the last stage, classification is done by using support vector machines. As a result of the study, six different sleep patterns were classified with an average accuracy rate of 90.20 %.

Keywords:

Sleeping Sounds, Simple Snoring, Duplex Snoring, Triplex Snoring, Classification,

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