Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi

Translated title of the contribution: Classification of pediatric snoring episodes using deep convolutional neural networks

Ozan Firat Civaner, Mustafa Kamasak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper, a deep convolutional neural network is designed for automatic detection of snoring episodes of children from ambient audio data. In order to test the performance of the network, a dataset collected from 38 pediatric patients with simple snoring or obstructive sleep apnea. The ambient audio signal is converted to Mel spectrogram in order to make the signal two dimensional. The network is trained using Adadelta algorithm on 1.200 samples. In order to fine tune the system parameters, a validation set of 300 samples is used. Finally, a test set of 300 samples was used observe the performance of the developed network. The samples in the training, validation, and test samples are all exclusive. The mean accuracy of the proposed system over 20 experiments is observed as 91% for snore and non-snore classification.

Translated title of the contributionClassification of pediatric snoring episodes using deep convolutional neural networks
Original languageTurkish
Title of host publication26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538615010
DOIs
Publication statusPublished - 5 Jul 2018
Event26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey
Duration: 2 May 20185 May 2018

Publication series

Name26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/TerritoryTurkey
CityIzmir
Period2/05/185/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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