Scopus
YÖKSİS DOI Eşleşti
SJR Q1
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-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
BIOMEDICAL SIGNAL PROCESSING AND CONTROL · 2022 SCI
Prof. Dr. 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
Doç. Dr. 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
Dr. Öğr. Üyesi GÜZİN ÖZMEN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
CNN-based Bi-directional and Directional Long-short Term Memory Network for Determination of Face Mask
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI-Expanded Q2
Dr. Öğr. Üyesi GÜZİN ÖZMEN →
Improving efficiency in convolutional neural networks with 3D image filters
2022 ISSN: 1746-8094 SCI-Expanded Q2
Dr. Öğr. Üyesi NEJAT ÜNLÜKAL →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. ADEM GÖLCÜK →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
Design and implementation of a hybrid FLC + PID controller for pressure control of sleep devices
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. ADEM GÖLCÜK →
Future activity prediction of multiple sclerosis with 3D MRI using 3D discrete wavelet transform
2022 ISSN: 1746-8094 SCI-Expanded Q2
Dr. Öğr. Üyesi ZÜLEYHA YILMAZ ACAR →
Improving efficiency in convolutional neural networks with 3D image filters
2022 ISSN: 1746-8094 SCI Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features
2022 ISSN: 1746-8094 SCI
Prof. Dr. ŞAKİR TAŞDEMİR →
CNN-based Bi-directional and Directional Long-short Term Memory Network for Determination of Face Mask
2022 ISSN: 1746-8094 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Involution-based HarmonyNet: An efficient hyperspectral imaging model for automatic detection of neonatal health status
2024 ISSN: 1746-8094 SCI-Expanded Q1
Doç. Dr. MURAT KONAK →
Abc-based weighted voting deep ensemble learning model for multiple eye disease detection
2024 ISSN: 1746-8094 SCI-Expanded Q1
Prof. Dr. ŞAKİR TAŞDEMİR →
Makale Bilgileri
ISSN17468094
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
Scimago Dergi (ISSN Eşleşmesi)
Biomedical Signal Processing and Control
Q1
SJR Skoru1,229
H-Index125
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Biomedical Engineering (Q1)
Health Informatics (Q1)
Signal Processing (Q1)
Metrikler
11
Atıf
4
Yazar
5
Anahtar Kelime