{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T13:59:43Z","timestamp":1770904783990,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["G512F11007"],"award-info":[{"award-number":["G512F11007"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["390895286"],"award-info":[{"award-number":["390895286"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u2019s Excellence Strategy\u2014EXC 2177\/1","award":["G512F11007"],"award-info":[{"award-number":["G512F11007"]}]},{"name":"Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u2019s Excellence Strategy\u2014EXC 2177\/1","award":["390895286"],"award-info":[{"award-number":["390895286"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Epileptic seizures affect around 1% of people worldwide and have an enormous impact on the quality of life as well as the health of each patient. Electroencephalography (EEG) is widely used to diagnose epilepsy and detect seizures. Automatic detection and documentation of epileptic seizures using EEG signals would help neurologists evaluate the course of disease of each patient individually. As scalp EEG systems are not suited to be worn in everyday life situations, there is a need for mobile EEG systems to permanently record EEG signals. An approach for such mobile devices consists of using behind-the-ear (BTE) electrodes, leading to a reduction in electrode channels. To address this reduction, we investigated the influence of different scalp EEG channel arrangements on the detection of epileptic seizures. Raw EEG signals have been used as input for a long short-term memory (LSTM) recurrent neural network (RNN), as well as a combination of a convolutional neural network (CNN) and LSTM to classify ictal and inter-ictal phases. When using all channels of the 10\u201320 EEG cap system, the CNN-LSTM model achieved a sensitivity of 73%, with fewer than two seizures being falsely detected per hour. The usage of BTE channels as input to the proposed epileptic seizure detection produced a promising sensitivity of 68% with around 10 false alarms per hour.<\/jats:p>","DOI":"10.3390\/info16010020","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T10:17:23Z","timestamp":1735899443000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Patient-Independent Epileptic Seizure Detection with Reduced EEG Channels and Deep Recurrent Neural Networks"],"prefix":"10.3390","volume":"16","author":[{"given":"Nadine","family":"El-Dajani","sequence":"first","affiliation":[{"name":"Communication Acoustics, Department for Medical Physics and Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky Universit\u00e4t Oldenburg, 26129 Oldenburg, Germany"}]},{"given":"Tim Friedrich Lutz","family":"Wilhelm","sequence":"additional","affiliation":[{"name":"Communication Acoustics, Department for Medical Physics and Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky Universit\u00e4t Oldenburg, 26129 Oldenburg, Germany"}]},{"given":"Jan","family":"Baumann","sequence":"additional","affiliation":[{"name":"Klinik und Polyklinik f\u00fcr Epileptologie, University Clinic, 53127 Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-8582","authenticated-orcid":false,"given":"Rainer","family":"Surges","sequence":"additional","affiliation":[{"name":"Klinik und Polyklinik f\u00fcr Epileptologie, University Clinic, 53127 Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9190-2111","authenticated-orcid":false,"given":"Bernd T.","family":"Meyer","sequence":"additional","affiliation":[{"name":"Communication Acoustics, Department for Medical Physics and Acoustics and Cluster of Excellence Hearing4all, Carl von Ossietzky Universit\u00e4t Oldenburg, 26129 Oldenburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"ref_1","first-page":"414","article-title":"Mortality and sudden unexpected death in epilepsy (SUDEP)","volume":"82","author":"Surges","year":"2014","journal-title":"Fortschritte Der Neurol. Psychiatr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1001\/archneur.64.11.1595","article-title":"Epilepsy: Accuracy of patient seizure counts","volume":"64","author":"Hoppe","year":"2007","journal-title":"Arch. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/0013-4694(87)90206-9","article-title":"Cerebral location of international 10\u201320 system electrode placement","volume":"66","author":"Homan","year":"1987","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1097\/WNP.0000000000000774","article-title":"Intracranial EEG validation of single-channel subgaleal EEG for seizure identification","volume":"39","author":"Pacia","year":"2022","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1111\/epi.16360","article-title":"Ultra-long-term subcutaneous home monitoring of epilepsy\u2014490 days of EEG from nine patients","volume":"60","author":"Weisdorf","year":"2019","journal-title":"Epilepsia"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bleichner, M.G., and Debener, S. (2017). Concealed, unobtrusive ear-centered EEG acquisition: cEEGrids for transparent EEG. Front. Hum. Neurosci., 11.","DOI":"10.3389\/fnhum.2017.00163"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1111\/epi.13699","article-title":"MRI-negative temporal lobe epilepsy\u2014What do we know?","volume":"58","author":"Muhlhofer","year":"2017","journal-title":"Epilepsia"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gu, Y., Cleeren, E., Dan, J., Claes, K., Paesschen, W.V., Huffel, S.V., and Hunyadi, B. (2018). Comparison between scalp EEG and behind-the-ear EEG for development of a wearable seizure detection system for patients with focal epilepsy. Sensors, 18.","DOI":"10.3390\/s18010029"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1111\/epi.16470","article-title":"Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels","volume":"61","author":"Vandecasteele","year":"2020","journal-title":"Epilepsia"},{"key":"ref_10","unstructured":"Shoeb, A.H., and Guttag, J.V. (2010, January 21\u201324). Application of machine learning to epileptic seizure detection. Proceedings of the ICML, Haifa, Israel."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s12553-018-0265-z","article-title":"A robust methodology for classification of epileptic seizures in EEG signals","volume":"9","author":"Tzimourta","year":"2019","journal-title":"Health Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TIM.2018.2855518","article-title":"Accurate classification of seizure and seizure-free intervals of intracranial EEG signals from epileptic patients","volume":"68","author":"Lahmiri","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jana, G.C., Sharma, R., and Agrawal, A. (2020). A 1D-CNN-Spectrogram Based Approach for Seizure Detection from EEG Signal, Elsevier B.V.","DOI":"10.1016\/j.procs.2020.03.248"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","article-title":"Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals","volume":"100","author":"Acharya","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_15","first-page":"1","article-title":"Deep learning for epileptic spike detection","volume":"33","author":"Le","year":"2018","journal-title":"VNU J. Sci. Comput. Sci. Commun. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Manaswi, N.K. (2018). Rnn and lstm. Deep Learning with Applications Using Python, Springer.","DOI":"10.1007\/978-1-4842-3516-4"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.clinph.2018.10.010","article-title":"Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals","volume":"130","author":"Hussein","year":"2019","journal-title":"Clin. Neurophysiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","article-title":"Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state","volume":"64","author":"Andrzejak","year":"2001","journal-title":"Phys. Rev. E"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bouaziz, B., Chaari, L., Batatia, H., and Quintero-Rinc\u00f3n, A. (2019). Epileptic seizure detection using a convolutional neural network. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine, Springer.","DOI":"10.1007\/978-3-030-11800-6_9"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Choi, G., Park, C., Kim, J., Cho, K., Kim, T.J., Bae, H., Min, K., Jung, K.Y., and Chong, J. (2019, January 11\u201313). A Novel Multi-scale 3D CNN with Deep Neural Network for Epileptic Seizure Detection. Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2019.8661969"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Golmohammadi, M., Ziyabari, S., Shah, V., Von Weltin, E., Campbell, C., Obeid, I., and Picone, J. (2017, January 2). Gated recurrent networks for seizure detection. Proceedings of the 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA.","DOI":"10.1109\/SPMB.2017.8257020"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Obeid, I., and Picone, J. (2016). The temple university hospital EEG data corpus. Front. Neurosci., 10.","DOI":"10.3389\/fnins.2016.00196"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Graves, A., and Schmidhuber, J. (August, January 31). Framewise phoneme classification with bidirectional LSTM networks. Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, QC, Canada.","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Golmohammadi, M., Shah, V., Obeid, I., and Picone, J. (2020). Deep Learning Approaches for Automated Seizure Detection from Scalp Electroencephalograms. Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-36844-9_8"},{"key":"ref_26","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_27","unstructured":"Houta, S., Bisgin, P., and Dulich, P. (2019, January 24\u201328). Machine learning methods for detection of Epileptic seizures with long-term wearable devices. Proceedings of the Eleventh International Conference on eHealth, Telemedicine, and Social Medicine, Athens, Greece."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Devi, D., Biswas, S.K., and Purkayastha, B. (2020, January 2\u20134). A Review on Solution to Class Imbalance Problem: Undersampling Approaches. Proceedings of the 2020 International Conference on Computational Performance Evaluation (ComPE), Shillong, India.","DOI":"10.1109\/ComPE49325.2020.9200087"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-validatory choice and assessment of statistical predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_31","first-page":"1","article-title":"An introduction to the bootstrap","volume":"57","author":"Tibshirani","year":"1993","journal-title":"Monogr. Stat. Appl. Probab."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1212\/WNL.51.5.1256","article-title":"Is the underlying cause of epilepsy a major prognostic factor for recurrence?","volume":"51","author":"Semah","year":"1998","journal-title":"Neurology"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Maia, P., Lopes, E., Hartl, E., Vollmar, C., Noachtar, S., and Cunha, J.P.S. (2020). Multimodal Approach for Epileptic Seizure Detection in Epilepsy Monitoring Units, Springer.","DOI":"10.1007\/978-3-030-31635-8_133"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vandecasteele, K., De Cooman, T., Gu, Y., Cleeren, E., Claes, K., Paesschen, W.V., Huffel, S.V., and Hunyadi, B. (2017). Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment. Sensors, 17.","DOI":"10.3390\/s17102338"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O\u2019Brien, T., Perucca, P., and Ge, Z. (2020, January 4\u20136). Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study. Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, VIC, Australia.","DOI":"10.1145\/3373017.3373055"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/1\/20\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:22:35Z","timestamp":1759918955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/1\/20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["info16010020"],"URL":"https:\/\/doi.org\/10.3390\/info16010020","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]}}}