Leveraging Multimodal and Feature Selection Approaches to Improve Sleep Apnea Classification Performance

Date

2017-05-15

Author

Memiş, Gökhan
Sert, Mustafa
Yazıcı, Adnan

Metadata

Show full item record

Item Usage Stats

218
views

0
downloads

Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust features from both biological signals and design three classifiers, namely Naive Bayes (NB), k-nearest neighbors (kNN), and Support Vector Machine (DVM) to test these features. Our experimental results on the real clinical samples from the PhysioNet dataset show that the proposed multimodal and Relieff feature selection based method improves the average classification accuracy by 4.67% on all test scenarios.

Subject Keywords

Sleep apnea syndromes, Electrocardiograph, Breathing rate

URI

https://hdl.handle.net/11511/74621
https://ieeexplore.ieee.org/document/7960547

DOI

https://doi.org/10.1109/SIU.2017.7960547

Conference Name

25th Signal Processing and Communications Applications Conference, SIU 2017 (15-18 May 2017)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Mobile sleep apnea detection and monitoring based on thermocouple and pulse oximeter sensors Demirkol Çakmak, Duygu; Eyüboğlu, Behçet Murat; Department of Electrical and Electronics Engineering (2018) Sleep apnea syndrome is becoming a prevalent disease for both adults and children. It is described as the cessation of breath for at least 10 seconds during sleep. Detecting sleep apnea is considered as a troublesome and time-consuming method, which requires the patients to stay one or more nights in dedicated sleep disorder rooms with sensors physically attached to their body. Undiagnosed, thereby untreated, sleep apnea patients are under high risk of hypertension, heart attack, accidental injuries through...
Portable obstructive sleep apnea detection and mobile monitoring Cakmak, Duygu Demirkol; Eyüboğlu, Behçet Murat (2017-04-10) Obstructive sleep apnea syndrome is becoming a prevalent disease for both adults and children. It is described as the cessation of breath for at least 10 seconds during sleep. Detecting sleep apnea is considered as a troublesome and time-consuming method, which requires the patients to stay one or more nights in dedicated sleep disorder rooms with sensors physically attached to their body. Undiagnosed thereby untreated sleep apnea patients are under high risk of hypertension, heart attack, traffic accident ...
Serum copper and zinc status in obstructive sleep apnea patients Akyüz, Ahmet Oğuz; Pelin, Z.; Ozogur, S.; Ercan, O. (2006-09-01) Obstructive Sleep Apnea Syndrome (OSAS) is a chronic disease characterized by oxygen desaturation and awakens follow from repetitive apnea and hipopnea during the night. It is thought that diversity of oxygen levels during night in OSAS cause oxidative stress with the increment of the free radical production. In this study, serum levels of copper (Cu) and zinc (Zn), two of the most important elements of antioxidant defense mechanism, were investigated. Patients to be participated into the study were divided...
An efficient fast method of snore detection for sleep disorder investigation Çavuşoğlu, Mustafa; Serinağaoğlu Doğrusöz, Yeşim; Department of Electrical and Electronics Engineering (2007) Snores are breath sounds that most people produce during sleep and they are reported to be a risk factor for various sleep disorders, such as obstructive sleep apnea syndrome (OSAS). Diagnosis of sleep disorders relies on the expertise of the clinician that inspects whole night polysomnography recordings. This inspection is time consuming and uncomfortable for the patient. There are surgical and therapeutic treatments. However, evaluation of the success of these methods also relies on subjective criteria an...
Detection of post apnea sounds and apnea periods from sleep sounds Karcı, Ersin; Serinağaoğlu Doğrusöz, Yeşim; Çiloğlu, Tolga; Department of Electrical and Electronics Engineering (2011) Obstructive Sleep Apnea Syndrome (OSAS) is defined as a sleep related breathing disorder that causes the body to stop breathing for about 10 seconds and mostly ends with a loud sound due to the opening of the airway. OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, with multiple electrodes attached to the patient's body. Snoring is a symptom which may indicate presence of OSAS; thus investigation of snoring sounds, which can be r...

Citation Formats

G. Memiş, M. Sert, and A. Yazıcı, “Leveraging Multimodal and Feature Selection Approaches to Improve Sleep Apnea Classification Performance,” presented at the 25th Signal Processing and Communications Applications Conference, SIU 2017 (15-18 May 2017), Antalya, Turkey, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/74621.