Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
Yazarlar (3)
Doç. Dr. Adem Gölcük Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Güzin Özmen Selçuk Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Biomedical Signal Processing and Control (Q1) | ||
| Dergi ISSN | 1746-8094 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2022 |
| Cilt / Sayı / Sayfa | 73 / 1 / 103402–0 | DOI | 10.1016/j.bspc.2021.103402 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S174680942100999X | ||
| UAK Araştırma Alanları |
Yapay Zeka
Görüntü İşleme
Bulanık Mantık
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| Özet |
| Sleep-disordered breathing is a disease that many people experience unconsciously and can have very serious consequences that can result in death. Therefore, it is extremely important to analyze the data obtained from the patient during sleep. It has become inevitable to use computer technologies in the diagnosis or treatment of many diseases in the medical field. Especially, advanced software using artificial intelligence methods in the diagnosis and decision-making processes of physicians is becoming increasingly widespread. In this study, we aimed to classify the sleep-disordered breathing type by using machine learning techniques utilizing time and time- frequency domain features. We used Pressure Flow, ECG, Pressure Snore, SpO2, Pulse and Thorax data from among the polysomnography records of 19 patients. We employed digital signal processing methods for six types of physiological data and … |
| Anahtar Kelimeler |
| Apnea | Hypopnea | Machine learning | Sleep disordered breathing | Time–frequency domain features |
Science Direct
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 9 |
| Scopus | 11 |
| Google Scholar | 17 |