{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T20:44:54Z","timestamp":1781901894508,"version":"3.54.5"},"reference-count":36,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Temporal lobe epilepsy, a neurological disease that causes seizures as a result of excessive neural activities in the brain, is the most common type of focal seizure, accounting for 30\u201335% of all epilepsies. Detection of epilepsy and localization of epileptic focus are essential for treatment planning and epilepsy surgery. Currently, epileptic focus is decided by expert physician by examining the EEG records and determining EEG channel where epileptic patterns begins and continues intensely during seizure. Examination of long EEG recordings is very time-consuming process, requires attention and decision can vary depending on physician. In this study, to assist physicians in detecting epileptic focus side from EEG recordings, a novel deep learning-based computer-aided diagnosis system is presented. In the proposed framework, ictal epochs are detected using long short-term memory network fed with EEG subband features obtained by discrete wavelet transform, and then, epileptic focus identification is realized by using asymmetry score. This algorithm was tested on EEG database obtained from the Ankara University hospital. Experimental results showed ictal and interictal epochs were classified with accuracy of 86.84%, sensitivity of 86.96% and specificity of 89.68% on Ankara University hospital dataset, and 96.67% success rate was obtained on Bonn EEG dataset. In addition, epileptic focus was identified with accuracy of 96.10%, sensitivity of 100% and specificity of 93.80% by using the proposed deep learning-based algorithm and university hospital dataset. These results showed that proposed method can be used properly in clinical applications, epilepsy treatment and surgical planning as a medical decision support system.<\/jats:p>","DOI":"10.3390\/diagnostics13132261","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T01:47:14Z","timestamp":1688435234000},"page":"2261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Identification of TLE Focus from EEG Signals by Using Deep Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"given":"Cansel","family":"Ficici","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Ankara University, 06830 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziya","family":"Telatar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ba\u015fkent University, 06790 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8240-4046","authenticated-orcid":false,"given":"Onur","family":"Kocak","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ba\u015fkent University, 06790 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4640-6570","authenticated-orcid":false,"given":"Osman","family":"Erogul","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, TOBB University of Economics and Technology, 06560 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, A., Pachori, R.B., Upadhyay, A., and Acharya, U.R. (2017). Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci., 7.","DOI":"10.3390\/app7040385"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ficici, C., Erogul, O., and Telatar, Z. (2019, January 28\u201330). Epileptic Activity Detection in EEG Signals using Linear and Non-linear Feature Extraction Methods. Proceedings of the 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey.","DOI":"10.23919\/ELECO47770.2019.8990401"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104250","DOI":"10.1016\/j.compbiomed.2021.104250","article-title":"Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals","volume":"131","author":"Zarei","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112895","DOI":"10.1016\/j.eswa.2019.112895","article-title":"A hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy","volume":"140","author":"Khan","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.compbiomed.2019.05.016","article-title":"Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier","volume":"110","author":"Raghu","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e0173138","DOI":"10.1371\/journal.pone.0173138","article-title":"A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG","volume":"12","author":"Chen","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.eswa.2018.03.053","article-title":"EEG signal classification using universum support vector machine","volume":"106","author":"Richhariya","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.patrec.2020.03.009","article-title":"Automated detection of abnormal EEG signals using localized wavelet filter banks","volume":"133","author":"Sharma","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17849","DOI":"10.1007\/s11042-021-10597-6","article-title":"Machine learning-based EEG signals classification model for epileptic seizure detection","volume":"80","author":"Qureshi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neucom.2017.02.053","article-title":"Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier","volume":"241","author":"Mursalin","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10395","DOI":"10.1007\/s12652-020-02837-8","article-title":"EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN\/SVM classifiers","volume":"12","author":"Omidvar","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9072","DOI":"10.1016\/j.eswa.2012.02.040","article-title":"Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework","volume":"39","author":"Acharya","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103755","DOI":"10.1016\/j.bspc.2022.103755","article-title":"Automated temporal lobe epilepsy and psychogenic nonepileptic seizure patient discrimination from multichannel EEG recordings using DWT based analysis","volume":"77","author":"Telatar","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.bspc.2016.09.008","article-title":"Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble","volume":"31","author":"Li","year":"2017","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.7555\/JBR.34.20190026","article-title":"EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms","volume":"34","author":"Slimen","year":"2020","journal-title":"J. Biomed. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104652","DOI":"10.1016\/j.bspc.2023.104652","article-title":"A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal","volume":"83","author":"Qiu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"77255","DOI":"10.1109\/ACCESS.2020.2989442","article-title":"Classification of focal and non-focal epileptic patients using single channel EEG and long short-term memory learning system","volume":"8","author":"Fraiwan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Poorani, S., and Balasubramanie, P. (2023). Deep learning based epileptic seizure detection with EEG data. Int. J. Syst. Assur. Eng. Manag., 1\u201310.","DOI":"10.1007\/s13198-022-01845-5"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101943","DOI":"10.1016\/j.jocs.2023.101943","article-title":"Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning","volume":"67","year":"2023","journal-title":"J. Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TBCAS.2019.2957087","article-title":"Deep learning approach for epileptic focus localization","volume":"14","author":"Daoud","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mir, W.A., Anjum, M., and Shahab, S. (2023). Deep-EEG: An optimized and robust framework and method for EEG-based diagnosis of epileptic seizure. Diagnostics, 13.","DOI":"10.3390\/diagnostics13040773"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1007\/s40747-021-00627-z","article-title":"Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features","volume":"8","author":"Singh","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102720","DOI":"10.1016\/j.bspc.2021.102720","article-title":"Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping","volume":"68","author":"Yildiz","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"T\u00fcrk, \u00d6., and \u00d6zerdem, M.S. (2019). Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sci., 9.","DOI":"10.3390\/brainsci9050115"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"17391","DOI":"10.1007\/s11042-022-13947-0","article-title":"Epilepsy-Net: Attention-based 1D-inception network model for epilepsy detection using one-channel and multi-channel EEG signals","volume":"82","author":"Lebal","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119010","DOI":"10.1016\/j.eswa.2022.119010","article-title":"Multimodal detection of epilepsy with deep neural networks","volume":"213","author":"Ilias","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"375","DOI":"10.3389\/fneur.2020.00375","article-title":"Deep convolutional neural network-based epileptic electroencephalogram (EEG) signal classification","volume":"11","author":"Gao","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Farooq, M.S., Zulfiqar, A., and Riaz, S. (2023). Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges. Diagnostics, 13.","DOI":"10.3390\/diagnostics13061058"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11571-022-09816-z","article-title":"Epileptic seizure focus detection from interictal electroencephalogram: A survey","volume":"17","author":"Islam","year":"2023","journal-title":"Cogn. Neurodynamics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"20043","DOI":"10.1109\/ACCESS.2021.3128611","article-title":"Imaginary control of a mobile vehicle using deep learning algorithm: A brain computer interface study","volume":"10","author":"Hekmatmanesh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_31","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_32","unstructured":"(2023, February 01). GitHub. Available online: https:\/\/github.com\/otoolej\/eeg_viewer."},{"key":"ref_33","unstructured":"The MathWorks Inc. (2023, April 12). Deep Learning Toolbox (R2021a). Available online: https:\/\/www.mathworks.com."},{"key":"ref_34","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_35","unstructured":"(2023, May 05). The MathWorks. Available online: https:\/\/www.mathworks.com\/help\/deeplearning\/gs\/create-simple-sequence-classification-network.html."},{"key":"ref_36","unstructured":"(2023, May 08). The MathWorks. Available online: https:\/\/www.mathworks.com\/help\/deeplearning\/gs\/create-simple-sequencehttps:\/\/www.mathworks.com\/help\/signal\/ug\/classify-ecg-signals-using-long-short-term-memory-networks.html."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/13\/2261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:05:40Z","timestamp":1760126740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/13\/2261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,4]]},"references-count":36,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["diagnostics13132261"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13132261","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,4]]}}}