Current Proceedings on Technology

Current Proceedings on Technology

Diagnosing apnea using extreme learning machine with bicoherence features of snore signals

Yazarlar: Necmettin SEZGİN, M. Emin Tagluk

Cilt 3 , Sayı - , 2013 , Sayfalar -

Konular:-

Anahtar Kelimeler:Obstructive sleep apnea syndrome,Extreme learning machine,Bispectrum analysis,Bicoherence

Özet: In this study, the performance of a hybrid decision system, Extreme Learning Machine (ELM) accompanied higher order spectra of snoring sounds, in identification of Obstructive Sleep Apnea Syndrome (OSAS), was performed. The method involves characterization of snore sounds through the Quadratic Phase Coupling (QPC) values emerged with the bispectrum analysis of signal. These phase dependent characteristics were basically classified through ELM. By this technique the OSAS and normal snore were identified in a high accuracy. This classification method was considered to help experts in making their final decisions.

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@article{2013, title={Diagnosing apnea using extreme learning machine with bicoherence features of snore signals}, volume={3}, number={0}, publisher={Current Proceedings on Technology }, author={Necmettin SEZGİN, M. Emin Tagluk}, year={2013} }
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Necmettin SEZGİN, M. Emin Tagluk. (2013). Diagnosing apnea using extreme learning machine with bicoherence features of snore signals (Vol. 3). Vol. 3. Current Proceedings on Technology .
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Necmettin SEZGİN, M. Emin Tagluk. Diagnosing Apnea Using Extreme Learning Machine with Bicoherence Features of Snore Signals. no. 0, Current Proceedings on Technology , 2013.
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