Automated characterization and detection of fibromyalgia using slow wave sleep EEG signals with glucose pattern and D’hondt pooling technique

• Published in: Cognitive neurodynamics 2024-04, Vol.18 (2), p.383-404
• Main Authors: Karabey Aksalli, Isil, Baygin, Nursena, Hagiwara, Yuki, Paul, Jose Kunnel, Iype, Thomas, Barua, Prabal Datta, Koh, Joel E. W., Baygin, Mehmet, Dogan, Sengul, Tuncer, Turker, Acharya, U. Rajendra
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Automation; Biochemistry; Biomedical and Life Sciences; Biomedicine; Classification; Cognitive Psychology; Computer Science; Deep learning; Disease; EEG; Electroencephalography; Feature extraction; Feature selection; Fibromyalgia; Glucose; Greedy algorithms; Machine learning; Magnetic resonance imaging; Methods; Neurosciences; NREM sleep; Patients; Principal components analysis; Quality of life; Regression analysis; Research Article; Signal classification; Sleep; Soft tissues; Statistical analysis; Support vector machines
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-023-10005-9

Fibromyalgia is a soft tissue rheumatism with significant qualitative and quantitative impact on sleep macro and micro architecture. The primary objective of this study is to analyze and identify automatically healthy individuals and those with fibromyalgia using sleep electroencephalography (EEG) signals. The study focused on the automatic detection and interpretation of EEG signals obtained from fibromyalgia patients. In this work, the sleep EEG signals are divided into 15-s and a total of 5358 (3411 healthy control and 1947 fibromyalgia) EEG segments are obtained from 16 fibromyalgia and 16 normal subjects. Our developed model has advanced multilevel feature extraction architecture and hence, we used a new feature extractor called GluPat, inspired by the glucose chemical, with a new pooling approach inspired by the D’hondt selection system. Furthermore, our proposed method incorporated feature selection techniques using iterative neighborhood component analysis and iterative Chi2 methods. These selection mechanisms enabled the identification of discriminative features for accurate classification. In the classification phase, we employed a support vector machine and k-nearest neighbor algorithms to classify the EEG signals with leave-one-record-out (LORO) and tenfold cross-validation (CV) techniques. All results are calculated channel-wise and iterative majority voting is used to obtain generalized results. The best results were determined using the greedy algorithm. The developed model achieved a detection accuracy of 100% and 91.83% with a tenfold and LORO CV strategies, respectively using sleep stage (2 + 3) EEG signals. Our generated model is simple and has linear time complexity.