{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:21:27Z","timestamp":1761402087065},"reference-count":84,"publisher":"World Scientific Pub Co Pte Ltd","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:p>Stroke, if not lethal, is a primary cause of disability. Early assessment of markers of recovery can allow personalized interventions; however, it is difficult to deliver indexes in the acute phase able to predict recovery. In this perspective, evaluation of electrical brain activity may provide useful information. A machine learning approach was explored here to predict post-stroke recovery relying on multi-channel electroencephalographic (EEG) recordings of few minutes performed at rest. A data-driven model, based on partial least square (PLS) regression, was trained on 19-channel EEG recordings performed within 10 days after mono-hemispheric stroke in 101 patients. The band-wise (delta: 1\u20134[Formula: see text]Hz, theta: 4\u20137[Formula: see text]Hz, alpha: 8\u201314[Formula: see text]Hz and beta: 15\u201330[Formula: see text]Hz) EEG effective powers were used as features to predict the recovery at 6\u00a0months (based on clinical status evaluated through the NIH Stroke Scale, NIHSS) in an optimized and cross-validated framework. In order to exploit the multimodal contribution to prognosis, the EEG-based prediction of recovery was combined with NIHSS scores in the acute phase and both were fed to a nonlinear support vector regressor (SVR). The prediction performance of EEG was at least as good as that of the acute clinical status scores. A posteriori evaluation of the features exploited by the analysis highlighted a lower delta and higher alpha activity in patients showing a positive outcome, independently of the affected hemisphere. The multimodal approach showed better prediction capabilities compared to the acute NIHSS scores alone ([Formula: see text] versus [Formula: see text], AUC = 0.80 versus AUC = 0.70, [Formula: see text]). The multimodal and multivariate model can be used in acute phase to infer recovery relying on standard EEG recordings of few minutes performed at rest together with clinical assessment, to be exploited for early and personalized therapies. The easiness of performing EEG may allow such an approach to become a standard-of-care and, thanks to the increasing number of labeled samples, further improving the model predictive power.<\/jats:p>","DOI":"10.1142\/s0129065720500677","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T15:13:58Z","timestamp":1599578038000},"page":"2050067","source":"Crossref","is-referenced-by-count":30,"title":["Electroencephalography-Derived Prognosis of Functional Recovery in Acute Stroke Through Machine Learning Approaches"],"prefix":"10.1142","volume":"30","author":[{"given":"Antonio Maria","family":"Chiarelli","sequence":"first","affiliation":[{"name":"Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Universit\u00e0 G. d\u2019Annunzio, Chieti, 66100, Italy"}]},{"given":"Pierpaolo","family":"Croce","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Universit\u00e0 G. d\u2019Annunzio, Chieti, 66100, Italy"}]},{"given":"Giovanni","family":"Assenza","sequence":"additional","affiliation":[{"name":"Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Campus Bio-Medico University of Rome, Rome, Italy"}]},{"given":"Arcangelo","family":"Merla","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Universit\u00e0 G. d\u2019Annunzio, Chieti, 66100, Italy"}]},{"given":"Giuseppe","family":"Granata","sequence":"additional","affiliation":[{"name":"Fondazione Policlinico A. Gemelli IRCCS, Catholic University of Sacred Heart, Rome, Italy"}]},{"given":"Nadia Mariagrazia","family":"Giannantoni","sequence":"additional","affiliation":[{"name":"Neurocenter of Southern Switzerland, Ospedale Regionale di Lugano (EOC), Lugano, Switzerland"}]},{"given":"Vittorio","family":"Pizzella","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Universit\u00e0 G. d\u2019Annunzio, Chieti, 66100, Italy"}]},{"given":"Franca","family":"Tecchio","sequence":"additional","affiliation":[{"name":"Laboratory of Electrophysiology for Translational NeuroScience (LET\u2019S), Istituto di Scienze e Teconologie della Cognizione (ISTC) - Consiglio Nazionale delle Ricerche (CNR), Rome, Italy"}]},{"given":"Filippo","family":"Zappasodi","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Universit\u00e0 G. d\u2019Annunzio, Chieti, 66100, Italy"}]}],"member":"219","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"S0129065720500677BIB001","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1136\/jnnp-2016-313361","volume":"87","author":"Crichton S. 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