Classification of Sleep Apnea Events Using Nasal Air Flow (NAF) Signals.
Abdel-Meguid, F., Abou-Chadi, F., Loza, S. (2021). Classification of Sleep Apnea Events Using Nasal Air Flow (NAF) Signals.. EKB Journal Management System, 37(4), 1-10. doi: 10.21608/bfemu.2021.199195
Fatma Z. Abdel-Meguid; Fatma E. Z. Abou-Chadi; S. Loza. "Classification of Sleep Apnea Events Using Nasal Air Flow (NAF) Signals.". EKB Journal Management System, 37, 4, 2021, 1-10. doi: 10.21608/bfemu.2021.199195
Abdel-Meguid, F., Abou-Chadi, F., Loza, S. (2021). 'Classification of Sleep Apnea Events Using Nasal Air Flow (NAF) Signals.', EKB Journal Management System, 37(4), pp. 1-10. doi: 10.21608/bfemu.2021.199195
Abdel-Meguid, F., Abou-Chadi, F., Loza, S. Classification of Sleep Apnea Events Using Nasal Air Flow (NAF) Signals.. EKB Journal Management System, 2021; 37(4): 1-10. doi: 10.21608/bfemu.2021.199195

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

MEJ- Mansoura Engineering Journal
Article 12, Volume 37, Issue 4, December 2012, Page 1-10  XML
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.199195
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Authors
Fatma Z. Abdel-Meguid* 1; Fatma E. Z. Abou-Chadi2; S. Loza3
1Electronics and Communications Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
2Electronics and Communications Engineering Department., Faculty o Engineering., El-Mansoura University., Mansoura., Egypt.
3Cairo Center for Sleep Disorder., Cairo., Egypt.
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.
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