{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:56:00Z","timestamp":1773802560446,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T00:00:00Z","timestamp":1628726400000},"content-version":"vor","delay-in-days":223,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time\u2010frequency analysis of electroencephalography (EEG) signals and GLCM features extracted from them. To this end, at first, the time\u2010frequency map (TFM) of each channel of each EEG is computed by smoothed pseudo\u2010Wigner\u2013Ville distribution (SPWVD), where the EEG signal used in this paper is recorded in 15 channels. After that, we consider the gray\u2010level co\u2010occurrence matrix (GLCM) to obtain the content of TFM, and after that, four features such as homogeneity, correlation, energy, and contrast are obtained for each GLCM. Finally, after the selection of efficient features using the minimum redundancy maximum relevance (MRMR) method, the K\u2010nearest neighbor (KNN) classifier is utilized to determine the DoA. Here, we consider the three states, namely, deep hypnotic, surgical anesthesia, and sedation and awake states according to bispectral index (BIS), and each EEG epoch is classified to these states. We also employ data augmentation to enhance the training phase and increase accuracy. We obtain the accuracy and confusion matrix of the proposed method. We also analyze the effects of a number of gray levels of GLCM, distance measure in KNN classifier, and parameters of data augmentation on the performance of the proposed method. Results indicate the efficiency of the proposed method to determine the DoA during surgery.<\/jats:p>","DOI":"10.1155\/2021\/8430565","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T22:50:17Z","timestamp":1628808617000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Time\u2010Frequency Analysis of EEG Signals and GLCM Features for Depth of Anesthesia Monitoring"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2492-4772","authenticated-orcid":false,"given":"Seyed Mortaza","family":"Mousavi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6928-090X","authenticated-orcid":false,"given":"Akbar","family":"Asgharzadeh-Bonab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7065-9060","authenticated-orcid":false,"given":"Ramin","family":"Ranjbarzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/s101210896"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/bja\/ael120"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/bja\/aev145"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-9891-3_2"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.3238\/arztebl.2011.0001"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/354739"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0889-8537(01)80018-3"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.4103\/0019-5049.103956"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"BarthelmeA. 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