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199195

Classification of Sleep Apnea Events Using Nasal Air Flow (NAF) Signals.

Article

Last updated: 22 Jan 2023

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Subjects

-

Tags

Electronics and Communications Engineering

Abstract

Detecting and diagnosing different types of Sleep Apnea (Obstructive, Central and Hypopneaisone of the major tasks in sleep medicine. Clinically, analyzing Nasal Airflow (NAF) signal is the most sufficient and direct reliably effective method for the automatic detection of Sleep Apnea events by checking the airflow amplitude reduction. This paper investigates and compares the performance of three classifiers: a kohen self-organizing map (SOM), Adaptive Neuro Fuzzy Inference System ?(ANFIS) AND Hidden Markov Model (HMM) for the classification of Sleep apnea events. The results have shown that the highest correct classification rate is 95.7% when using HMM with order 13.

DOI

10.21608/bfemu.2021.199195

Authors

First Name

Fatma

Last Name

Abdel-Meguid

MiddleName

Z.

Affiliation

Electronics and Communications Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

Email

-

City

Mansoura

Orcid

-

First Name

Fatma

Last Name

Abou-Chadi

MiddleName

E. Z.

Affiliation

Electronics and Communications Engineering Department., Faculty o Engineering., El-Mansoura University., Mansoura., Egypt.

Email

-

City

Mansoura

Orcid

-

First Name

S.

Last Name

Loza

MiddleName

-

Affiliation

Cairo Center for Sleep Disorder., Cairo., Egypt.

Email

-

City

-

Orcid

-

Volume

37

Article Issue

4

Related Issue

17853

Issue Date

2012-12-01

Receive Date

2012-10-10

Publish Date

2021-10-12

Page Start

1

Page End

10

Print ISSN

1110-0923

Online ISSN

2735-4202

Link

https://bfemu.journals.ekb.eg/article_199195.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=199195

Order

12

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023