{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:30:21Z","timestamp":1760956221270,"version":"3.40.5"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Istanbul University \u2013 Cerrahpasa","award":["36160","36160"],"award-info":[{"award-number":["36160","36160"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s11760-024-03077-5","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T19:01:46Z","timestamp":1709838106000},"page":"4349-4361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ConceFT-based epileptic seizure detection via transfer learning"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5696-7267","authenticated-orcid":false,"given":"Mosab A. A.","family":"Yousif","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2600-7051","authenticated-orcid":false,"given":"Mahmut","family":"Ozturk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"3077_CR1","unstructured":"WHO: \u201cEpilepsy\u201d, https:\/\/www.who.int\/news-room\/factsheets\/detail\/epilepsy. [Accessed Oct. 12, 2021]."},{"issue":"4","key":"3077_CR2","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1111\/j.0013-9580.2005.66104.x","volume":"46","author":"RS Fisher","year":"2005","unstructured":"Fisher, R.S., Boas, W.E., Blume, W., Elger, C., Genton, P., Lee, P., Engel, J.: Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia 46(4), 470\u2013472 (2005)","journal-title":"Epilepsia"},{"issue":"4","key":"3077_CR3","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1111\/epi.12550","volume":"55","author":"RS Fisher","year":"2014","unstructured":"Fisher, R.S., Acevedo, C., et al.: ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55(4), 475\u2013482 (2014)","journal-title":"Epilepsia"},{"key":"3077_CR4","doi-asserted-by":"crossref","unstructured":"Alarc\u00f3n, G., Valent\u00edn, A.: Introduction to Epilepsy. Cambridge University Press (2012)","DOI":"10.1017\/CBO9781139103992"},{"key":"3077_CR5","doi-asserted-by":"crossref","unstructured":"Freeman,W.J., Quiroga, R.Q.: Imaging Brain Function with EEG: Advanced Tem\u2014poral and Spatial Analysis of Electroencephalographic Signals. Springer (2013)","DOI":"10.1007\/978-1-4614-4984-3"},{"key":"3077_CR6","volume-title":"Principles of Neurology","author":"A Ropper","year":"2005","unstructured":"Ropper, A., Brown, R.H.: Principles of Neurology, 8th edn. USA, McGraw-Hill, Boston (2005)","edition":"8"},{"issue":"8","key":"3077_CR7","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1109\/TNSRE.2016.2611601","volume":"25","author":"T Zhang","year":"2017","unstructured":"Zhang, T., Chen, W.: LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans. Neural Syst. Rehabil. Eng. 25(8), 1100\u20131108 (2017)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"3077_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.eswa.2018.04.021","volume":"107","author":"I Ullah","year":"2018","unstructured":"Ullah, I., Hussain, M., Qazi, E.-U.-H., Aboalsamh, H.: An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst. Appl. 107, 61\u201371 (2018)","journal-title":"Expert Syst. Appl."},{"key":"3077_CR9","doi-asserted-by":"crossref","unstructured":"Mohseni, H. R., Maghsoudi, A., Shamsollahi, M. B.: Seizure detection in EEG signals: a comparison of different approaches. IEEEEMBC (2006)","DOI":"10.1109\/IEMBS.2006.260931"},{"key":"3077_CR10","unstructured":"Kevric, J., Subasi, A.: Classification of EEG signals for epileptic seizure prediction using ANN. In: Proceedings of the International Symposium Sustainable Development, IEEE (2012)"},{"key":"3077_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2013.08.006","volume":"9","author":"V Joshi","year":"2014","unstructured":"Joshi, V., Pachori, R.B., Vijesh, A.: Classification of ictal and seizure free EEG signals using fractional linear prediction. Biomed. Signal Process. Control 9, 1\u20135 (2014)","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"3077_CR12","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","volume":"13","author":"AT Tzallas","year":"2009","unstructured":"Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time\u2013frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703\u2013710 (2009)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"issue":"10","key":"3077_CR13","doi-asserted-by":"publisher","first-page":"13475","DOI":"10.1016\/j.eswa.2011.04.149","volume":"38","author":"U Orhan","year":"2011","unstructured":"Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475\u201313481 (2011)","journal-title":"Expert Syst. Appl."},{"key":"3077_CR14","first-page":"209","volume":"243","author":"Y Kaya","year":"2014","unstructured":"Kaya, Y., Uyar, M., Tekin, R., Y\u0131ld\u0131r\u0131m, S.: 1D-local binary pattern-based feature extraction for classification of epileptic EEG signals. Appl. Math. Comput. 243, 209\u2013219 (2014)","journal-title":"Appl. Math. Comput."},{"key":"3077_CR15","doi-asserted-by":"publisher","first-page":"7716","DOI":"10.1109\/ACCESS.2016.2585661","volume":"4","author":"A Sharmila","year":"2016","unstructured":"Sharmila, A., Geethanjali, P.: DWT based detection of epileptic seizure from EEG signals using naive bayes and k-NN classifiers. IEEE Access 4, 7716\u20137727 (2016)","journal-title":"IEEE Access"},{"key":"3077_CR16","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, A., Pachori, R., Upadhyay et al. A.: Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7(4). Article ID 385 (2017)","DOI":"10.3390\/app7040385"},{"key":"3077_CR17","doi-asserted-by":"crossref","unstructured":"Turk, O., Ozerdem, M.S.: Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sci. 9(5). Article ID 115 (2019)","DOI":"10.3390\/brainsci9050115"},{"key":"3077_CR18","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.bspc.2017.01.005","volume":"34","author":"AK Jaiswal","year":"2017","unstructured":"Jaiswal, A.K., Banka, H.: Local pattern transformatio based feature extraction techniques for classification of epileptic EEG signals. Biomed. Signal Process. Control 34, 81\u201392 (2017)","journal-title":"Biomed. Signal Process. Control"},{"key":"3077_CR19","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neucom.2019.01.053","volume":"335","author":"JT Oliva","year":"2019","unstructured":"Oliva, J.T., Rosa, J.L.G.: Classification for EEG report generation and epilepsy detection. Neurocomputing 335, 81\u201395 (2019)","journal-title":"Neurocomputing"},{"key":"3077_CR20","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.bspc.2017.12.006","volume":"41","author":"D Sikdar","year":"2018","unstructured":"Sikdar, D., Roy, R., Mahadevappa, M.: Epilepsy and seizure characterisation by multifractal analysis of EEG subbands. Biomed. Signal Process. Control 41, 264\u2013270 (2018)","journal-title":"Biomed. Signal Process. Control"},{"key":"3077_CR21","doi-asserted-by":"crossref","unstructured":"Zahra, A., Kanwal, N., ur Rehman, N., Ehsan, S., McDonald-Maier, K.D.: Seizure detection from EEG signals using multivariate empirical mode decomposition. Comput. Biol. Med. 88, 132\u2013141 (2017)","DOI":"10.1016\/j.compbiomed.2017.07.010"},{"key":"3077_CR22","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","volume":"100","author":"UR Acharya","year":"2018","unstructured":"Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270\u2013278 (2018)","journal-title":"Comput. Biol. Med."},{"key":"3077_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102938","volume":"69","author":"I Wijayanto","year":"2021","unstructured":"Wijayanto, I., Hartanto, R., Nugroho, H.A.: Multi-distance fluctuation based dispersion fractal for epileptic seizure detection in EEG signal. Biomed. Signal Process. Control 69, 102938 (2021)","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"3077_CR24","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indi- cations of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64(6), 061907 (2001)","journal-title":"Phys. Rev. E"},{"key":"3077_CR25","doi-asserted-by":"crossref","unstructured":"Daubechies, I., (Grace) Wang, Y., Wu, H.: ConceFT: Concentration of frequency and time via a multitapered synchrosqueezing transform. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 374(2065). Art. no. 20150193 (2016)","DOI":"10.1098\/rsta.2015.0193"},{"issue":"1","key":"3077_CR26","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/TBME.2016.2549048","volume":"64","author":"Y Lin","year":"2017","unstructured":"Lin, Y., Wu, H.: ConceFT for time-varying heart rate variability analysis as a measure of noxious stimulation during general anesthesia. IEEE Trans. Biomed. Eng. 64(1), 145\u2013154 (2017). https:\/\/doi.org\/10.1109\/TBME.2016.2549048","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"3077_CR27","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1109\/TBME.2014.2311996","volume":"61","author":"B Babadi","year":"2014","unstructured":"Babadi, B., Brown, E.N.: A review of multitaper spectral analysis. IEEE Trans. Biomed. Eng. 61(5), 1555\u20131564 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"3077_CR28","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.acha.2010.08.002","volume":"30","author":"I Daubechies","year":"2011","unstructured":"Daubechies, I., Lu, J., Wu, H.T.: Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal. 30, 243\u2013261 (2011)","journal-title":"Appl. Comput. Harmon. Anal."},{"issue":"5","key":"3077_CR29","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1109\/78.382394","volume":"43","author":"F Auger","year":"1995","unstructured":"Auger, F., Flandrin, P.: Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Trans. Signal Process. 43(5), 1068\u20131089 (1995)","journal-title":"IEEE Trans. Signal Process."},{"key":"3077_CR30","doi-asserted-by":"crossref","unstructured":"Chassande-Mottin E. et al.: Time-frequency\/time-scale reassignment. In: Wavelets and signal processing (ser. Appl. Numer. Harmon. Anal.). Boston, MA, USA: Birkhauser, pp. 233\u2013267 (2003)","DOI":"10.1007\/978-1-4612-0025-3_8"},{"key":"3077_CR31","volume-title":"Time-Frequency Analysis","author":"L Cohen","year":"1995","unstructured":"Cohen, L.: Time-Frequency Analysis. Prentice Hall, Englewood Cliffs, NJ (1995)"},{"issue":"4","key":"3077_CR32","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imag. 9(4), 611\u2013629 (2018)","journal-title":"Insights Imag."},{"key":"3077_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., Oliva, A.: Places: an image database for deep scene understanding.\u00a0arXiv preprint: arXiv:1610.02055\u00a0(2016)","DOI":"10.1167\/17.10.296"},{"key":"3077_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1\u20139)\u200f (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"3077_CR35","unstructured":"Iandola et al. F.N.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv preprint: arXiv:1602.07360 (2016)"},{"key":"3077_CR36","unstructured":"\u201cSqueezeNet,\u201d Papers With Code. [Online]. Available: https:\/\/paperswithcode.com\/lib\/torchvision\/squeezenet."},{"key":"3077_CR37","volume":"10","author":"G Tsivgoulis","year":"2022","unstructured":"Tsivgoulis, G., et al.: An improved SqueezeNet model for the diagnosis of lung cancer in CT scans. Mach. Learn. Appl. 10, 100399 (2022)","journal-title":"Mach. Learn. Appl."},{"issue":"260","key":"3077_CR38","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","volume":"47","author":"WH Kruskal","year":"1952","unstructured":"Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583\u2013621 (1952)","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03077-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03077-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03077-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T13:09:11Z","timestamp":1716469751000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03077-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,7]]},"references-count":38,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["3077"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03077-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2024,3,7]]},"assertion":[{"value":"25 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The experiments and data collection were approved by the local ethics committee as mentioned in []. The authors of this work have accepted the ethics rules and obeyed them during the research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The publisher has the permission from the authors to publish the paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The EEG signals of patients with epilepsy are recorded at University of Bonn, and they are publicly available for scientific research [].","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Information sharing"}}]}}