Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi

Ozan Firat Civaner; Mustafa Kamasak

doi:10.1109/SIU.2018.8404352

Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi

Ozan Firat Civaner, Mustafa Kamasak

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Classification of pediatric snoring episodes using deep convolutional neural networks
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-4
Sayfa sayısı	4
ISBN (Elektronik)	9781538615010
DOI'lar	https://doi.org/10.1109/SIU.2018.8404352
Yayın durumu	Yayınlandı - 5 Tem 2018
Etkinlik	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Süre: 2 May 2018 → 5 May 2018

Yayın serisi

Adı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Ülke/Bölge	Turkey
Şehir	Izmir
Periyot	2/05/18 → 5/05/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Keywords

Deep convolutional neural network
Mel spectrogram
Obstructive sleep apnea
Snoring

Belgeye Erişim

10.1109/SIU.2018.8404352

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Civaner, O. F., & Kamasak, M. (2018). Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404352

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title = "Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi",

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.",

keywords = "Deep convolutional neural network, Mel spectrogram, Obstructive sleep apnea, Snoring",

author = "Civaner, {Ozan Firat} and Mustafa Kamasak",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

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Civaner, OF & Kamasak, M 2018, Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404352

Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi. / Civaner, Ozan Firat; Kamasak, Mustafa.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi

AU - Civaner, Ozan Firat

AU - Kamasak, Mustafa

PY - 2018/7/5

Y1 - 2018/7/5

N2 - 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.

AB - 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.

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KW - Obstructive sleep apnea

KW - Snoring

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Civaner OF, Kamasak M. Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4. (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). doi: 10.1109/SIU.2018.8404352

Pediyatrik Horlama Episodlarinin Derin Evrisimsel Sinir Aǧlari ile Siniflandirilmasi

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

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Alıntı Yap