{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T01:47:28Z","timestamp":1725068848031},"reference-count":45,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2021,5]]},"abstract":" Epilepsy is a neurological disease that is very common worldwide. Patient\u2019s electroencephalography (EEG) signals are frequently used for the detection of epileptic seizure segments. In this paper, a high-resolution time-frequency (TF) representation called Synchrosqueezing Transform (SST) is used to detect epileptic seizures. Two different EEG data sets, the IKCU data set we collected, and the publicly available CHB-MIT data set are analyzed to test the performance of the proposed model in seizure detection. The SST representations of seizure and nonseizure (pre-seizure or inter-seizure) EEG segments of epilepsy patients are calculated. Various features like higher-order joint TF (HOJ-TF) moments and gray-level co-occurrence matrix (GLCM)-based features are calculated using the SST representation. By using single and ensemble machine learning methods such as k-Nearest Neighbor (kNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Boosted Trees (BT), and Subspace kNN (S-kNN), EEG features are classified. The proposed SST-based approach achieved 95.1% ACC, 96.87% PRE, 95.54% REC values for the IKCU data set, and 95.13% ACC, 93.37% PRE, 90.30% REC values for the CHB-MIT data set in seizure detection. Results show that the proposed SST-based method utilizing novel TF features outperforms the short-time Fourier transform (STFT)-based approach, providing over 95% accuracy for most cases, and compares well with the existing methods. <\/jats:p>","DOI":"10.1142\/s0129065721500052","type":"journal-article","created":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T14:42:06Z","timestamp":1605883326000},"page":"2150005","source":"Crossref","is-referenced-by-count":23,"title":["Classification of Epileptic EEG Signals Using Synchrosqueezing Transform and Machine Learning"],"prefix":"10.1142","volume":"31","author":[{"given":"Ozlem Karabiber","family":"Cura","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli 35620, Izmir, Turkey"}]},{"given":"Aydin","family":"Akan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Izmir University of Economics, Balcova 35330, Izmir, Turkey"}]}],"member":"219","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"S0129065721500052BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.03.023"},{"key":"S0129065721500052BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.seizure.2015.01.012"},{"issue":"3","key":"S0129065721500052BIB003","first-page":"40","volume":"4","author":"Ali S.","year":"2016","journal-title":"Int. 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