Scopus
YÖKSİS Eşleşti
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
Biomedical Signal Processing and Control · Mart 2022
YÖKSİS Kayıtları
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
BIOMEDICAL SIGNAL PROCESSING AND CONTROL · 2022 SCI
PROFESÖR ŞAKİR TAŞDEMİR →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
Biomedical Signal Processing and Control · 2022 SCI-Expanded
PROFESÖR ŞAKİR TAŞDEMİR →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
Biomedical Signal Processing and Control · 2022 SCI-Expanded
DOÇENT ADEM GÖLCÜK →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
Biomedical Signal Processing and Control · 2022 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ GÜZİN ÖZMEN →
Makale Bilgileri
DergiBiomedical Signal Processing and Control
Yayın TarihiMart 2022
Cilt / Sayfa73
Scopus ID2-s2.0-85120652259
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 obtained a total of 35 features using different feature extraction methods for five different classes (Normal, Hypopnea, Obstructive Apnea, Mixed Apnea, Central Apnea). Finally, we applied machine learning algorithms (Artificial Neural Network, Support Vector Machine, Random Forest, Naive Bayes, K Nearest Neighborhood, Decision Tree and Logistic Regression) on 5-class and 35-feature data sets. We used10 fold cross validation to verify the classification success. Our main contribution to the literature is that we developed a classification system to score all four different types of sleep-disordered breathing simultaneously by using 6 types of PSG data. As a five-class scoring result, the Random Forest (RF) algorithm showed the highest success with 76.3 % classification accuracy. When Hypopnea was excluded from the evaluation, classification accuracy increased to 86.6% for three Apnea-type disorders. Our proposed method provided 89.7% accuracy for the diagnosis of Obstructive Apnea by the RF classifier. The results show that time and time–frequency domain features are distinctive in Sleep-disordered breathing scoring, which is a very difficult process for physicians and a diagnostic support system can be design by evaluating many PSG data simultaneously.
Yazarlar (4)
1
Mehmet Balci
2
Şakir Taşdemir
3
Güzin Özmen
ORCID: 0000-0003-3007-5807
4
Adem Golcuk
ORCID: 0000-0002-6734-5906
Anahtar Kelimeler
Apnea
Hypopnea
Machine learning
Sleep disordered breathing
Time–frequency domain features
Kurumlar
Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
4
Atıf
4
Yazar
5
Anahtar Kelime