EEG senkronizasyon ölçütleri kullanarak uyku apnesi çeşitlerinin siniflandirilmasi

Mehmet Feyzi Akşahin; Serap Aydin; Hikmet Firat; Osman Eroǧul; Sadik Ardiç

doi:10.1109/BIYOMUT.2010.5479810

EEG senkronizasyon ölçütleri kullanarak uyku apnesi çeşitlerinin siniflandirilmasi

Translated title of the contribution: Classification of sleep apnea types using EEG synchronization criteria

Mehmet Feyzi Akşahin
, Serap Aydin
, Hikmet Firat
, Osman Eroǧul
, Sadik Ardiç

Baskent University
19 Mayis Üniversitesi
Göǧüs Hastaliklari Kliniǧi
Gülhane Military Medical Academy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

In this study, to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed. For this purpose, sleep EEG series data collected from patients and healthy volunteers are classified by using a well known and widely used Feed-Forward Neural Network (FFNN) with respect to synchronic activities between C3 and C4 recordings. The results show that the degree of central EEG synchronization during night sleep is closely linked to sleep disorders like CSA and OSA. The MI and CF provide information in meaningful collaboration to support the clinical findings. These three groups were defined with a medical expert and can be very successfully classified by using the FFNN having two hidden layers with the average area of CF curves ranged form 0 Hz to 10 Hz and the average MI values are assigned as two features. This study is a preliminary study for classifying types of sleep apnea.

Translated title of the contribution	Classification of sleep apnea types using EEG synchronization criteria
Original language	Turkish
Title of host publication	2010 15th National Biomedical Engineering Meeting, BIYOMUT2010
DOIs	https://doi.org/10.1109/BIYOMUT.2010.5479810
Publication status	Published - 2010
Externally published	Yes
Event	2010 15th National Biomedical Engineering Meeting, BIYOMUT2010 - Antalya, Turkey Duration: 21 Apr 2010 → 24 Apr 2010

Publication series

Name	2010 15th National Biomedical Engineering Meeting, BIYOMUT2010

Conference

Conference	2010 15th National Biomedical Engineering Meeting, BIYOMUT2010
Country/Territory	Turkey
City	Antalya
Period	21/04/10 → 24/04/10

Access to Document

10.1109/BIYOMUT.2010.5479810

Cite this

@inproceedings{130f10c4d51a45aba5043ebd7e88cfab,

title = "EEG senkronizasyon {\"o}l{\c c}{\"u}tleri kullanarak uyku apnesi {\c c}e{\c s}itlerinin siniflandirilmasi",

abstract = "In this study, to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed. For this purpose, sleep EEG series data collected from patients and healthy volunteers are classified by using a well known and widely used Feed-Forward Neural Network (FFNN) with respect to synchronic activities between C3 and C4 recordings. The results show that the degree of central EEG synchronization during night sleep is closely linked to sleep disorders like CSA and OSA. The MI and CF provide information in meaningful collaboration to support the clinical findings. These three groups were defined with a medical expert and can be very successfully classified by using the FFNN having two hidden layers with the average area of CF curves ranged form 0 Hz to 10 Hz and the average MI values are assigned as two features. This study is a preliminary study for classifying types of sleep apnea.",

author = "Ak{\c s}ahin, \{Mehmet Feyzi\} and Serap Aydin and Hikmet Firat and Osman Eroǧul and Sadik Ardi{\c c}",

year = "2010",

doi = "10.1109/BIYOMUT.2010.5479810",

language = "T{\"u}rk{\c c}e",

isbn = "9781424463824",

series = "2010 15th National Biomedical Engineering Meeting, BIYOMUT2010",

booktitle = "2010 15th National Biomedical Engineering Meeting, BIYOMUT2010",

note = "2010 15th National Biomedical Engineering Meeting, BIYOMUT2010 ; Conference date: 21-04-2010 Through 24-04-2010",

}

Akşahin, MF, Aydin, S, Firat, H, Eroǧul, O & Ardiç, S 2010, EEG senkronizasyon ölçütleri kullanarak uyku apnesi çeşitlerinin siniflandirilmasi. in 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010., 5479810, 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010, 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010, Antalya, Turkey, 21/04/10. https://doi.org/10.1109/BIYOMUT.2010.5479810

EEG senkronizasyon ölçütleri kullanarak uyku apnesi çeşitlerinin siniflandirilmasi. / Akşahin, Mehmet Feyzi; Aydin, Serap; Firat, Hikmet et al.
2010 15th National Biomedical Engineering Meeting, BIYOMUT2010. 2010. 5479810 (2010 15th National Biomedical Engineering Meeting, BIYOMUT2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - EEG senkronizasyon ölçütleri kullanarak uyku apnesi çeşitlerinin siniflandirilmasi

AU - Akşahin, Mehmet Feyzi

AU - Aydin, Serap

AU - Firat, Hikmet

AU - Eroǧul, Osman

AU - Ardiç, Sadik

PY - 2010

Y1 - 2010

N2 - In this study, to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed. For this purpose, sleep EEG series data collected from patients and healthy volunteers are classified by using a well known and widely used Feed-Forward Neural Network (FFNN) with respect to synchronic activities between C3 and C4 recordings. The results show that the degree of central EEG synchronization during night sleep is closely linked to sleep disorders like CSA and OSA. The MI and CF provide information in meaningful collaboration to support the clinical findings. These three groups were defined with a medical expert and can be very successfully classified by using the FFNN having two hidden layers with the average area of CF curves ranged form 0 Hz to 10 Hz and the average MI values are assigned as two features. This study is a preliminary study for classifying types of sleep apnea.

AB - In this study, to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed. For this purpose, sleep EEG series data collected from patients and healthy volunteers are classified by using a well known and widely used Feed-Forward Neural Network (FFNN) with respect to synchronic activities between C3 and C4 recordings. The results show that the degree of central EEG synchronization during night sleep is closely linked to sleep disorders like CSA and OSA. The MI and CF provide information in meaningful collaboration to support the clinical findings. These three groups were defined with a medical expert and can be very successfully classified by using the FFNN having two hidden layers with the average area of CF curves ranged form 0 Hz to 10 Hz and the average MI values are assigned as two features. This study is a preliminary study for classifying types of sleep apnea.

UR - https://www.scopus.com/pages/publications/77954446201

U2 - 10.1109/BIYOMUT.2010.5479810

DO - 10.1109/BIYOMUT.2010.5479810

M3 - Konferans katkısı

AN - SCOPUS:77954446201

SN - 9781424463824

T3 - 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010

BT - 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010

T2 - 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010

Y2 - 21 April 2010 through 24 April 2010

ER -

EEG senkronizasyon ölçütleri kullanarak uyku apnesi çeşitlerinin siniflandirilmasi

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