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However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader\u2019s predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.<\/jats:p>","DOI":"10.3390\/s23062971","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T02:05:54Z","timestamp":1678413954000},"page":"2971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MindReader: Unsupervised Classification of Electroencephalographic Data"],"prefix":"10.3390","volume":"23","author":[{"given":"Salvador Daniel","family":"Rivas-Carrillo","sequence":"first","affiliation":[{"name":"Department of Medical Biochemistry and Microbiology, Uppsala University, 75237 Uppsala, Sweden"},{"name":"Department of Cell and Molecular Biology, Uppsala University, 75237 Uppsala, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2552-9512","authenticated-orcid":false,"given":"Evgeny E.","family":"Akkuratov","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory, Department of Applied Physics, Royal Institute of Technology, 11428 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7586-6894","authenticated-orcid":false,"given":"Hector","family":"Valdez Ruvalcaba","sequence":"additional","affiliation":[{"name":"Epilepsy Clinic, Instituto Nacional de Neurologia y Neurocirug\u00eda, Mexico City 14269, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angel","family":"Vargas-Sanchez","sequence":"additional","affiliation":[{"name":"Independent Researcher, Guadalajara 44670, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0766-8789","authenticated-orcid":false,"given":"Jan","family":"Komorowski","sequence":"additional","affiliation":[{"name":"Department of Cell and Molecular Biology, Uppsala University, 75237 Uppsala, Sweden"},{"name":"Washington National Primate Research Center, Seattle, WA 98121, USA"},{"name":"The Institute of Computer Science, Polish Academy of Sciences, 01-248 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6685-5851","authenticated-orcid":false,"given":"Daniel","family":"San-Juan","sequence":"additional","affiliation":[{"name":"Epilepsy Clinic, Instituto Nacional de Neurologia y Neurocirug\u00eda, Mexico City 14269, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8792-6508","authenticated-orcid":false,"given":"Manfred G.","family":"Grabherr","sequence":"additional","affiliation":[{"name":"Department of Medical Biochemistry and Microbiology, Uppsala University, 75237 Uppsala, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1097\/01.wnp.0000220079.61973.6c","article-title":"Neurophysiologic basis of EEG","volume":"23","author":"Olejniczak","year":"2006","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/S1474-4422(18)30454-X","article-title":"Global, regional, and national burden of epilepsy, 1990\u20132016: A systematic analysis for the Global Burden of Disease Study 2016","volume":"18","author":"Beghi","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1111\/epi.12720","article-title":"Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: A population-based study","volume":"55","author":"Baldin","year":"2014","journal-title":"Epilepsia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1177\/155005940303400307","article-title":"Value of the Early Electroencephalogram after a First Unprovoked Seizure","volume":"34","author":"Schreiner","year":"2003","journal-title":"Clin. Electroencephalogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1016\/j.clinph.2018.01.019","article-title":"Clinical utility of EEG in diagnosing and monitoring epilepsy in adults","volume":"129","author":"Tatum","year":"2018","journal-title":"Clin. Neurophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1186\/s13643-020-01439-x","article-title":"Educational initiatives and implementation of electroencephalography into the acute care environment: A protocol of a systematic review","volume":"9","author":"Taran","year":"2020","journal-title":"Syst. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jelectrocard.2021.04.016","article-title":"12-Lead ECG arrhythmia classification using cascaded convolutional neural network and expert feature","volume":"67","author":"Yang","year":"2021","journal-title":"J. Electrocardiol."},{"key":"ref_9","unstructured":"Bajaj, V., and Sinha, G.R. (2022). Artificial Intelligence-Based Brain-Computer Interface, Academic Press."},{"key":"ref_10","unstructured":"Bajaj, V., and Sinha, G.R. (2022). Artificial Intelligence-Based Brain-Computer Interface, Academic Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1111\/epi.12135","article-title":"Standardized computer-based organized reporting of EEG: SCORE","volume":"54","author":"Beniczky","year":"2013","journal-title":"Epilepsia"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1097\/WNP.0b013e31821215e3","article-title":"Web-based collection of expert opinion on routine scalp EEG: Software development and interrater reliability","volume":"28","author":"Halford","year":"2011","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.cnp.2022.06.001","article-title":"EEG normal variants: A prospective study using the SCORE system","volume":"7","author":"Terney","year":"2022","journal-title":"Clin. Neurophysiol. Pract."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1097\/00004691-200302000-00005","article-title":"Overintepretation of EEGs and misdiagnosis of epilepsy","volume":"20","author":"Benbadis","year":"2003","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1097\/WNP.0000000000000613","article-title":"Normal Variants Are Commonly Overread as Interictal Epileptiform Abnormalities","volume":"36","author":"Kang","year":"2019","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1212\/01.WNL.0000163991.97456.03","article-title":"Clinical and EEG features of patients with EEG wicket rhythms misdiagnosed with epilepsy","volume":"64","author":"Krauss","year":"2005","journal-title":"Neurology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.clinph.2009.03.005","article-title":"Prevalence of benign epileptiform variants","volume":"120","author":"Santoshkumar","year":"2009","journal-title":"Clin. Neurophysiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1111\/j.1528-1157.2000.tb01529.x","article-title":"Uses and abuses of the EEG in epilepsy","volume":"41","author":"Fowle","year":"2000","journal-title":"Epilepsia"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6486570","DOI":"10.1155\/2022\/6486570","article-title":"EEG-Based Epileptic Seizure Detection via Machine\/Deep Learning Approaches: A Systematic Review","volume":"2022","author":"Ahmad","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rathod, P., and Naik, S. (2022, January 29\u201330). Review on Epilepsy Detection with Explainable Artificial Intelligence. Proceedings of the 2022 10th International Conference on Emerging Trends in Engineering and Technology\u2014Signal and Information Processing (ICETET-SIP-22), Nagpur, India.","DOI":"10.1109\/ICETET-SIP-2254415.2022.9791595"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"602","DOI":"10.21105\/joss.00602","article-title":"Flux: Elegant machine learning with Julia","volume":"3","author":"Innes","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1016\/S1388-2457(03)00123-8","article-title":"European data format \u2018plus\u2019 (EDF+), an EDF alike standard format for the exchange of physiological data","volume":"114","author":"Kemp","year":"2003","journal-title":"Clin. Neurophysiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/aic.690370209","article-title":"Nonlinear principal component analysis using autoassociative neural networks","volume":"37","author":"Kramer","year":"1991","journal-title":"AIChE J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_26","first-page":"1","article-title":"Variational Autoencoder based Anomaly Detection using Reconstruction Probability","volume":"2","author":"An","year":"2015","journal-title":"Spec. Lect. IE"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zamani, N., Russell, P., Lantz, H., Hoeppner, M.P., Meadows, J.R., Vijay, N., Mauceli, E., Di Palma, F., Lindblad-Toh, K., and Jern, P. (2013). Unsupervised genome-wide recognition of local relationship patterns. BMC Genom., 14.","DOI":"10.1186\/1471-2164-14-347"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.seizure.2016.05.018","article-title":"Correlation of invasive EEG and scalp EEG","volume":"41","author":"Ramantani","year":"2016","journal-title":"Seizure"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gupta, S.K., Kumar, K., Seelamantula, C.S., and Singh Thakur, C. (2019, January 23\u201327). A Portable Ultrasound Imaging System Utilizing Deep Generative Learning-Based Compressive Sensing on Pre-Beamformed RF Signals. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857437"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106542","DOI":"10.1016\/j.cmpb.2021.106542","article-title":"Semi-supervised automatic seizure detection using personalized anomaly detecting variational autoencoder with behind-the-ear EEG","volume":"213","author":"You","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pisa, I., Morell, A., Vicario, J.L., and Vilanova, R. (2020). Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments-The Wastewater Treatment Plant Control Case. Sensors, 20.","DOI":"10.3390\/s20133743"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, R., Yan, R., Wang, J., and Mao, K. (2017). Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors, 17.","DOI":"10.3390\/s17020273"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Luong, M.-T., Pham, H., and Manning, C.D. (2015). Effective Approaches to Attention-based Neural Machine Translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_35","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2971\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:45Z","timestamp":1760122305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23062971"],"URL":"https:\/\/doi.org\/10.3390\/s23062971","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,9]]}}}