Snore-related sound classification based on time-domain features by using ANFIS model

Ankişhan H., ARI F.

2011 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2011, Istanbul-Kadikoy, Türkiye, 15 - 18 Haziran 2011, ss.441-444 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/inista.2011.5946113
  • Basıldığı Şehir: Istanbul-Kadikoy
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.441-444
  • Anahtar Kelimeler: adaptive neuro fuzzy inference system, obstructive sleep apnea/hypopnea, snore-related sound classification
  • Ankara Üniversitesi Adresli: Evet

Özet

Obstructive sleep apnea/hypopnea (OSAH) is a highly prevalent disease which causes collapse in upper airway while sleeping. The purpose of this study is to classify snore related sounds into snore/non-snore episodes using adaptive neuro fuzzy inference system (ANFIS). Time-domain features which are entropy, energy and zero crossing rates were used and applied to data for ANFIS classifier model. At first, apnea and normal snore related sounds obtained from different patients are segmented. After segmentation, energy, entropy and zero crossing rates are calculated. Unlike the previous studies, entropy information was firstly used for snoring classification. Then, ANFIS was used to classify episodes as snore/non-snore. Experimental results have shown that ANFIS is able to classify snore segments with accuracy rate 97.08%. In conclusion, the results prove that ANFIS has good performance for classifying snore related sounds. © 2011 IEEE.