{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T21:35:35Z","timestamp":1761514535846,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T00:00:00Z","timestamp":1582502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1\u201332 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 \u00b1 0.013 (RS-SVM), 0.99 \u00b1 0.02 (RS-LDA), and 0.82 \u00b1 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 \u00b1 0.0233 (LOO-SVM), 0.98 \u00b1 0.124 (LOO-LDA), and 0.81 \u00b1 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.<\/jats:p>","DOI":"10.3390\/s20041235","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T04:21:26Z","timestamp":1582604486000},"page":"1235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects"],"prefix":"10.3390","volume":"20","author":[{"given":"Giuseppe","family":"Varone","sequence":"first","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}]},{"given":"Sara","family":"Gasparini","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"},{"name":"Regional Epilepsy Centre, Great Metropolitan Hospital, 89100 Reggio Calabria, Italy"}]},{"given":"Edoardo","family":"Ferlazzo","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"},{"name":"Regional Epilepsy Centre, Great Metropolitan Hospital, 89100 Reggio Calabria, Italy"}]},{"given":"Michele","family":"Ascoli","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"},{"name":"Regional Epilepsy Centre, Great Metropolitan Hospital, 89100 Reggio Calabria, Italy"}]},{"given":"Giovanbattista Gaspare","family":"Tripodi","sequence":"additional","affiliation":[{"name":"Regional Epilepsy Centre, Great Metropolitan Hospital, 89100 Reggio Calabria, Italy"}]},{"given":"Chiara","family":"Zucco","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}]},{"given":"Barbara","family":"Calabrese","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1502-2387","authenticated-orcid":false,"given":"Mario","family":"Cannataro","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4574-2951","authenticated-orcid":false,"given":"Umberto","family":"Aguglia","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"},{"name":"Regional Epilepsy Centre, Great Metropolitan Hospital, 89100 Reggio Calabria, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.seizure.2009.06.006","article-title":"Psychogenic non-epileptic seizures: Definition, etiology, treatment and prognostic issues: A critical review","volume":"8","author":"Bodde","year":"2009","journal-title":"Seizure"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.yebeh.2011.03.025","article-title":"Rating scale for psychogenic nonepileptic seizures: Scale development and clinimetric testing","volume":"21","author":"Cianci","year":"2011","journal-title":"Epilepsy Behav."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.yebeh.2009.12.008","article-title":"Heterogeneity in psychogenic nonepileptic seizures: Understanding the role of psychiatric and neurological factors","volume":"17","author":"Baslet","year":"2010","journal-title":"Epilepsy 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