@inproceedings{b49de068c0164ecb810f31177f07271c,
title = "{\c C}ocuklarda horlama episodlarinin otomatik tespiti",
abstract = "In this paper, a novel algorithm is proposed for automatic detection of snoring sounds from ambient acoustic data in a pediatric population. With the approval of institutional ethic committee and parents, the respiratory sounds of 50 subjects were recorded by using a pair of microphones and multichannel data acquisition system simultaneously with full-night polysomnography during sleep. Brief sound chunks of 0.5 s were classified as either belonging to a snoring event or not with a multi-layer perceptron which was trained in a supervised fashion using stochastic gradient descent on a large hand-labeled dataset using frequency domain features. The overall accuracy of the proposed algorithm was found to be 88.93% for primary snorers and 80.6% for obstructive sleep apnea (OSA) patients.",
keywords = "multi-layer perceptron, obstructive sleep apnea, Snoring",
author = "Mustafa Cavusoglu and Kamasak, {Mustafa E.} and Burger, {Harold Christopher} and Osman Erogul and Brockmann, {Pablo E.} and Poets, {Christian F.} and Urschitz, {Michael S.}",
year = "2014",
doi = "10.1109/SIU.2014.6830435",
language = "T{\"u}rk{\c c}e",
isbn = "9781479948741",
series = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
publisher = "IEEE Computer Society",
pages = "1138--1141",
booktitle = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
address = "United States",
note = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 ; Conference date: 23-04-2014 Through 25-04-2014",
}