{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T16:39:33Z","timestamp":1769013573605,"version":"3.49.0"},"reference-count":39,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T00:00:00Z","timestamp":1735430400000},"content-version":"vor","delay-in-days":363,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.055910","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T09:00:47Z","timestamp":1731920447000},"page":"3903-3926","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification"],"prefix":"10.32604","volume":"81","author":[{"given":"Umapathi","family":"Krishnamoorthy","sequence":"first","affiliation":[]},{"given":"Shanmugam","family":"Jagan","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Zakariah","sequence":"additional","affiliation":[]},{"given":"Abdulaziz S.","family":"Almazyad","sequence":"additional","affiliation":[]},{"given":"K.","family":"Gurunathan","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","article-title":"A ResNet-LSTM hybrid model for predicting epileptic seizures using a pretrained model with supervised contrastive learning","volume":"14","author":"Lee","year":"Jan. 2024","journal-title":"Sci. Rep."},{"key":"ref2","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1684\/epd.2020.1234","article-title":"How to distinguish seizures from non-epileptic manifestations","volume":"22","author":"Leibetseder","year":"Dec. 2020","journal-title":"Epileptic Disord."},{"key":"ref3","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s13042-020-01096-5","article-title":"Recent advances in deep learning","volume":"11","author":"Wang","year":"Apr. 2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/RBME.2020.3008792","article-title":"Machine learning for predicting epileptic seizures using EEG signals: A review","volume":"14","author":"Rasheed","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"42021","DOI":"10.1007\/s11042-023-15052-2","article-title":"EEG seizure detection: Concepts, techniques, challenges, and future trends","volume":"82","author":"Ein Shoka","year":"Nov. 2023","journal-title":"Multimed. Tools Appl."},{"key":"ref6","unstructured":"S. Ramakrishnan, R. Asuncion, and A. Rayi, \u201cLocalization-related epilepsies on EEG,\u201d 2024. Accessed: Aug. 15, 2024. [Online]. Available: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK557645\/"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1212\/WNL.57.11.2022","article-title":"The localizing value of ictal EEG in focal epilepsy","volume":"57","author":"Foldvary","year":"Dec. 2001","journal-title":"Neurology"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.knosys.2013.02.014","article-title":"Automated EEG analysis of epilepsy: A review","volume":"45","author":"Acharya","year":"Jun. 2013","journal-title":"Knowl. Based Syst."},{"key":"ref9","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.jneumeth.2012.07.003","article-title":"Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine","volume":"210","author":"Song","year":"Sep. 2012","journal-title":"J. Neurosci Methods"},{"key":"ref10","article-title":"A novel epilepsy seizure prediction model using deep learning and classification","volume":"4","author":"Jaishankar","year":"Dec. 2023","journal-title":"Healthc. Anal."},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.4103\/jnsbm.JNSBM_285_16","article-title":"Electroencephalogram signal classification for automated epileptic seizure detection using genetic algorithm","volume":"8","author":"Nanthini","year":"2017","journal-title":"J. Nat. Sci. Biol. Med."},{"key":"ref12","article-title":"EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network","volume":"13","author":"Yogarajan","year":"Oct. 2023","journal-title":"Sci. Rep."},{"key":"ref13","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s40846-016-0214-0","article-title":"Automatic epileptic seizure detection in EEG using nonsubsampled wavelet-fourier features","volume":"37","author":"Chen","year":"Feb. 2017","journal-title":"J. Med. Biol. Eng."},{"key":"ref14","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/TBME.2014.2360101","article-title":"Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform","volume":"62","author":"Samiee","year":"Feb. 2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref15","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neunet.2018.04.018","article-title":"Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram","volume":"105","author":"Truong","year":"Sep. 2018","journal-title":"Neural Netw."},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics13040773","article-title":"Deep-EEG: An optimized and robust framework and method for EEG-based diagnosis of epileptic seizure","volume":"13","author":"Mir","year":"Feb. 2023","journal-title":"Diagnostics"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.3390\/biomedicines11092370","article-title":"Automatic detection and classification of epileptic seizures from EEG data: Finding optimal acquisition settings and testing interpretable machine learning approach","volume":"11","author":"Statsenko","year":"Aug. 2023","journal-title":"Biomedicines"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.bspc.2017.01.005","article-title":"Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals","volume":"34","author":"Jaiswal","year":"Apr. 2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1109\/TBME.2018.2809798","article-title":"Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function","volume":"65","author":"Wang","year":"Nov. 2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-57744-1","article-title":"Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction","volume":"14","author":"Pontes","year":"Apr. 2024","journal-title":"Sci. Rep."},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-56019-z","article-title":"Comparison between epileptic seizure prediction and forecasting based on machine learning","volume":"14","author":"Costa","year":"Mar. 2024","journal-title":"Sci. Rep."},{"key":"ref22","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.eswa.2019.03.021","article-title":"A novel approach for classification of epileptic seizures using matrix determinant","volume":"127","author":"Raghu","year":"Aug. 2019","journal-title":"Expert. Syst. Appl."},{"key":"ref23","doi-asserted-by":"crossref","first-page":"13475","DOI":"10.1016\/j.eswa.2011.04.149","article-title":"EEG signals classification using the K-means clustering and a multilayer perceptron neural network model","volume":"38","author":"Orhan","year":"Sep. 2011","journal-title":"Expert. Syst. Appl."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1175305","DOI":"10.3389\/frsip.2023.1175305","article-title":"Epileptic seizure prediction based on multiresolution convolutional neural networks","volume":"3","author":"Ibrahim","year":"May 2023","journal-title":"Front. Signal Process."},{"key":"ref25","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":"Nov. 2001","journal-title":"Phys. Rev. E"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David and C. E. Elger, The Bonn EEG Time Series, 2001. Accessed: Mar. 28, 2024. [Online]. Available: https:\/\/www.upf.edu\/web\/ntsa\/downloads\/-\/asset_publisher\/xvT6E4pczrBw\/content\/2001-indications-of-nonlinear-deterministic-and-finite-dimensional-structures-in-time-series-of-brain-electrical-activity-dependence-on-recording-regi","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"ref27","unstructured":"P. Swami, B. Panigrahi, S. Nara, M. Bhatia, and T. Gandhi, \u201cEEG epilepsy datasets,\u201d 2016. doi: 10.13140\/RG.2.2.14280.32006."},{"key":"ref28","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":"Jun. 2017","journal-title":"Neurocomputing"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s00521-017-3003-y","article-title":"Epileptic seizure detection using hybrid machine learning methods","volume":"31","author":"Subasi","year":"Jan. 2019","journal-title":"Neural Comput. Appl."},{"key":"ref30","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3389\/fnhum.2019.00052","article-title":"Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization","volume":"13","author":"Wang","year":"Feb. 2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref31","doi-asserted-by":"crossref","first-page":"127357","DOI":"10.1109\/ACCESS.2024.3450449","article-title":"Novel EEG classification based on hellinger distance for seizure epilepsy detection","volume":"12","author":"Sadiq","year":"2024","journal-title":"IEEE Access"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s11517-023-02914-y","article-title":"Dynamical graph neural network with attention mechanism for epilepsy detection using single channel EEG","volume":"62","author":"Li","year":"Jan. 2024","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref33","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1016\/j.clinph.2017.04.026","article-title":"Seizure prediction in patients with focal hippocampal epilepsy","volume":"128","author":"Aarabi","year":"Jul. 2017","journal-title":"Clin. Neurophysiol."},{"key":"ref34","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":"Sep. 2018","journal-title":"Comput. Biol. Med."},{"key":"ref35","doi-asserted-by":"crossref","first-page":"14722","DOI":"10.1109\/ACCESS.2018.2810882","article-title":"Towards brain big data classification: Epileptic EEG identification with a lightweight VGGNet on global MIC","volume":"6","author":"Ke","year":"2018","journal-title":"IEEE Access"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"15485","DOI":"10.1007\/s12652-019-01220-6","article-title":"Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks","volume":"14","author":"Hu","year":"Nov. 2023","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"ref37","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":"Jan. 2019","journal-title":"Clin. Neurophysiol."},{"key":"ref38","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.physd.2004.02.013","article-title":"Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic","volume":"194","author":"Maiwald","year":"Jul. 2004","journal-title":"Physica D"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"61046","DOI":"10.1109\/ACCESS.2019.2915610","article-title":"Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features","volume":"7","author":"Chen","year":"May 2019","journal-title":"IEEE Access"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/cmc\/v81n3\/59026\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T06:04:09Z","timestamp":1741327449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v81n3\/59026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.055910","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-07-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-19","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}