{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T23:52:27Z","timestamp":1770162747537,"version":"3.49.0"},"reference-count":54,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,2,6]]},"abstract":"<jats:p>\n                    Research to\n                    <jats:italic>predict epileptic seizures<\/jats:italic>\n                    has been mainly focused on the analysis of\n                    <jats:italic>electroencephalography<\/jats:italic>\n                    (EEG) signals; however, recent research efforts have encouraged the use of a relatively new optical signal modality, called\n                    <jats:italic>functional Near-Infrared Spectroscopy<\/jats:italic>\n                    (fNIRS). In fNIRS, near-infrared light is injected into the scalp and the intensity of the reflected light is registered in optodes. Light absorption in hemoglobin depends on the level of blood oxygenation, which is related to brain activity. In this technique, two parameters are measured at each optode, the relative level of\n                    <jats:italic>oxygenated hemoglobin<\/jats:italic>\n                    (HbO) and the relative level of\n                    <jats:italic>deoxygenated hemoglobin<\/jats:italic>\n                    (HbR).\n                  <\/jats:p>\n                  <jats:p>\n                    In this work we investigated the feasibility of predicting epileptic seizures, using either fNIRS, EEG, or a combination of both signals. In one set of experiments, different implementations for epileptic seizure prediction are tested by using\n                    <jats:xref ref-type=\"disp-formula\">(1)<\/jats:xref>\n                    different combinations of electrical and optical signals (EEG, HbO, HbR, EEG+HbO, EEG+HbR, HbO+HbR, EEG+HbO+HbR) and\n                    <jats:xref ref-type=\"disp-formula\">(2)<\/jats:xref>\n                    two different classifiers, (\n                    <jats:italic>Support Vector Machine<\/jats:italic>\n                    - SVM and\n                    <jats:italic>Multi-Layer Perceptron<\/jats:italic>\n                    - MLP). In the second set of experiments, seizures are predicted within a five-minute window that is moved up to 15 minutes before the start of the epileptic seizure.\n                  <\/jats:p>\n                  <jats:p>\n                    By computing the\n                    <jats:italic>Positive Predictive Value<\/jats:italic>\n                    (PPV) and the\n                    <jats:italic>accuracy<\/jats:italic>\n                    , it is demonstrated that fNIRS-based epileptic prediction outperforms EEG-based epileptic prediction. By using optical signals and the SVM classifier, a\n                    <jats:italic>PPV<\/jats:italic>\n                    greater than 99% and an\n                    <jats:italic>accuracy<\/jats:italic>\n                    of 100% were obtained. PPV values of 100% are also obtained when seizures are predicted up to 15 minutes in advance. Furthermore,\n                    <jats:italic>Kernel Discriminant Analysis<\/jats:italic>\n                    (KDA) is used to demonstrate that the highest separability among the classes, corresponding to different epileptic signal phases (\n                    <jats:italic>pre-ictal<\/jats:italic>\n                    ,\n                    <jats:italic>ictal<\/jats:italic>\n                    , and\n                    <jats:italic>inter-ictal<\/jats:italic>\n                    ), is achieved when fNIRS recordings are used as features for prediction. Finally, fNIRS-based epileptic seizure prediction is tested with\n                    <jats:italic>Random Chance<\/jats:italic>\n                    classifiers.\n                  <\/jats:p>\n                  <jats:p>In this study, we showed that fNIRS signals are an effective tool to predict epileptic seizures, even without the use of EEG signals, which are the current standard for seizure prediction.<\/jats:p>","DOI":"10.3233\/jifs-190738","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T04:31:23Z","timestamp":1575520283000},"page":"2055-2068","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Prediction of epileptic seizures using fNIRS and machine learning"],"prefix":"10.1177","volume":"38","author":[{"given":"Edgar","family":"Guevara","sequence":"first","affiliation":[{"name":"CONACYT - Universidad Aut\u00f3noma de San Luis Potos\u00ed, Sierra Leona, Lomas 2a. secc., San Luis Potos\u00ed, Mexico"},{"name":"Terahertz Science and Technology Center (C2T2) and Science and Technology National Lab (LANCyTT), Universidad Aut\u00f3noma de San Luis Potos\u00ed, Mexico"}]},{"given":"Jorge-Arturo","family":"Flores-Castro","sequence":"additional","affiliation":[{"name":"Universidad de las Am\u00e9ricas - Puebla, Sta. Catarina M\u00e1rtir. Cholula, Puebla. C.P. 72820, Mexico"}]},{"given":"Ke","family":"Peng","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique de Montr\u00e9al, Department of Electrical Engineering, C.P. 6079 succ. Centre-ville, Montr\u00e9al, Qu\u00e9bec H3C 3A7, Canada"}]},{"given":"Dang Khoa","family":"Nguyen","sequence":"additional","affiliation":[{"name":"H\u00f4pital Notre-Dame du CHUM, Neurology Division, 1560 rue Sherbrooke est, Montr\u00e9al, Qu\u00e9bec H2L 4M1, Canada"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Lesage","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique de Montr\u00e9al, Department of Electrical Engineering, C.P. 6079 succ. 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