{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:32:35Z","timestamp":1774294355354,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T00:00:00Z","timestamp":1603929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.<\/jats:p>","DOI":"10.3390\/computers9040085","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T09:44:53Z","timestamp":1603964693000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0186-4049","authenticated-orcid":false,"given":"Antonio","family":"Quintero-Rinc\u00f3n","sequence":"first","affiliation":[{"name":"Departament of Electronic, Catholic University of Argentina (UCA), Av. Alicia Moreau de Justo 1300, Buenos Aires C1107AAZ, Argentina"},{"name":"Foundation for the Fight against Pediatric Neurological Disease (FLENI), Monta\u00f1eses 2325, Buenos Aires C1428AQK, Argentina"}]},{"given":"Valeria","family":"Muro","sequence":"additional","affiliation":[{"name":"Foundation for the Fight against Pediatric Neurological Disease (FLENI), Monta\u00f1eses 2325, Buenos Aires C1428AQK, Argentina"}]},{"given":"Carlos","family":"D\u2019Giano","sequence":"additional","affiliation":[{"name":"Foundation for the Fight against Pediatric Neurological Disease (FLENI), Monta\u00f1eses 2325, Buenos Aires C1428AQK, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1570-2115","authenticated-orcid":false,"given":"Jorge","family":"Prendes","sequence":"additional","affiliation":[{"name":"IRIT-INPT-ENSEEIHT, University of Toulouse, 31000 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0433-2152","authenticated-orcid":false,"given":"Hadj","family":"Batatia","sequence":"additional","affiliation":[{"name":"MACS School, Heriot-Watt University, Dubai Campus, Dubai Knowledge Park, Blocks 5 &amp; 14, P.O. Box 38103, Dubai, UAE"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"ref_1","unstructured":"Schomer, D.L., and da Silva, F.H.L. (2010). Niedermeyer\u2019s Electroencephalography Basic Principles, Clinical Applications, and Related Fields, LWW."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1038\/srep01483","article-title":"Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice","volume":"3","author":"Bergstrom","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3378","DOI":"10.18203\/2320-6012.ijrms20173526","article-title":"Interictal wave pattern study in EEG of epilepsy patients","volume":"5","author":"Bhuyan","year":"2013","journal-title":"Int. J. Res. Med. 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