{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:30:14Z","timestamp":1771468214620,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Speech Technol"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10772-021-09855-7","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T09:02:45Z","timestamp":1625562165000},"page":"559-570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Machine learning based classification of EEG signal for detection of child epileptic seizure without snipping"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-6715","authenticated-orcid":false,"given":"Prabira Kumar","family":"Sethy","sequence":"first","affiliation":[]},{"given":"Millee","family":"Panigrahi","sequence":"additional","affiliation":[]},{"given":"K.","family":"Vijayakumar","sequence":"additional","affiliation":[]},{"given":"Santi Kumari","family":"Behera","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,30]]},"reference":[{"key":"9855_CR1","doi-asserted-by":"crossref","unstructured":"Aghazadeh, R., Frounchi, J., Montagna, F., & Benatti, S. (2020). Scalable and energy-efficient seizure detection based on direct use of compressively-sensed EEG data on an ultra low power multi-core architecture. Computers in Biology and Medicine 125, 104004.","DOI":"10.1016\/j.compbiomed.2020.104004"},{"key":"9855_CR2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.seizure.2018.09.013","volume":"68","author":"M Amengual-Gual","year":"2019","unstructured":"Amengual-Gual, M., Ulate-Campos, A., & Loddenkemper, T. (2019). Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients. Seizure, 68, 31\u201337.","journal-title":"Seizure"},{"key":"9855_CR3","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.bspc.2018.10.017","volume":"52","author":"A Anuragi","year":"2019","unstructured":"Anuragi, A., & Sisodia, D. S. (2019). Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform. Biomedical Signal Processing and Control, 52, 384\u2013393.","journal-title":"Biomedical Signal Processing and Control"},{"issue":"2","key":"9855_CR4","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.clinph.2014.05.022","volume":"126","author":"M Bandarabadi","year":"2015","unstructured":"Bandarabadi, M., Teixeira, C. A., Rasekhi, J., & Dourado, A. (2015). Epileptic seizure prediction using relative spectral power features. Clinical Neurophysiology, 126(2), 237\u2013248.","journal-title":"Clinical Neurophysiology"},{"issue":"3","key":"9855_CR5","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1109\/PROC.1981.11969","volume":"69","author":"RE Crochiere","year":"1981","unstructured":"Crochiere, R. E., & Rabiner, L. R. (1981). Interpolation and decimation of digital signals\u2014A tutorial review. Proceedings of the IEEE, 69(3), 300\u2013331.","journal-title":"Proceedings of the IEEE"},{"issue":"1","key":"9855_CR6","first-page":"5","volume":"70","author":"J Engel","year":"2006","unstructured":"Engel, J. (2006). ILAE classification of epilepsy syndromes. EpilepsyResearch, 70(1), 5\u201310.","journal-title":"EpilepsyResearch"},{"key":"9855_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-017-8914-1_1","author":"RS Fisher","year":"2014","unstructured":"Fisher, R. S., Scharfman, H. E., & deCurtis, M. (2014). How can we identify Ictal and interictal abnormal activity? Advances in Experimental Medicine and Biology. https:\/\/doi.org\/10.1007\/978-94-017-8914-1_1","journal-title":"Advances in Experimental Medicine and Biology"},{"issue":"10","key":"9855_CR8","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1016\/j.clinph.2012.03.001","volume":"123","author":"K Gadhoumi","year":"2012","unstructured":"Gadhoumi, K., Lina, J., & Gotman, J. (2012). Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clinical Neurophysiology, 123(10), 1906\u20131916.","journal-title":"Clinical Neurophysiology"},{"issue":"23","key":"9855_CR9","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"A Goldberger","year":"2000","unstructured":"Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215\u2013e220.","journal-title":"Circulation"},{"key":"9855_CR10","doi-asserted-by":"crossref","unstructured":"Hosseinzadeh, M., Koohpayehzadeh, J., Bali, A. O., Asghari, P., Souri, A., Mazaherinezhad, A., Bohlouli, M., & Rawassizadeh, R. (2020). A diagnostic prediction model for chronic kidney disease in the internet of things platform. Multimedia Tools and Applications 1\u201318.","DOI":"10.1007\/s11042-020-09049-4"},{"issue":"9","key":"9855_CR11","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.1016\/S1388-2457(03)00123-8","volume":"114","author":"B Kemp","year":"2013","unstructured":"Kemp, B., & Olivan, J. (2013). European data format \u201cplus\u201d (EDF+), an EDF alike standard format for the exchange of physiological data. Clinical Neurophysiology, 114(9), 1755\u20131761.","journal-title":"Clinical Neurophysiology"},{"key":"9855_CR12","doi-asserted-by":"crossref","unstructured":"Klotz, K. A., Sag, Y., Sch\u00f6nberger, J., & Jacobs, J. (2020). Scalp ripples can predict development of epilepsy after first unprovoked seizure in childhood. Annals of Neurology.","DOI":"10.1002\/ana.25939"},{"key":"9855_CR13","doi-asserted-by":"crossref","unstructured":"Kumar, N., Kumar, R., Murmu, G., & Sethy, P. K. (2021). Extraction of melody from polyphonic music using modified morlet wavelet. Microprocessors and Microsystems 80, 103612.","DOI":"10.1016\/j.micpro.2020.103612"},{"key":"9855_CR14","doi-asserted-by":"crossref","unstructured":"Kumar, S., Singh, S., Agarwal, P., Acharya, U. K., Sethy, P. K., & Pandey, C. (2020). Speech quality evaluation for different pitch detection algorithms in LPC speech analysis\u2013synthesis system. International Journal of Speech Technology 1\u20137.","DOI":"10.1007\/s10772-020-09765-0"},{"key":"9855_CR15","unstructured":"Lu, D., & Triesch, J. (2019). Residual deep convolutional neural network for EEG signal classification in epilepsy. arXiv:1903.08100."},{"issue":"4","key":"9855_CR16","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1038\/s41593-018-0108-2","volume":"21","author":"J Parvizi","year":"2018","unstructured":"Parvizi, J., & Kastner, S. (2018). Promises and limitations of human intracranial electroencephalography. Nature Neuroscience, 21(4), 474\u2013483. https:\/\/doi.org\/10.1038\/s41593-018-0108-2","journal-title":"Nature Neuroscience"},{"key":"9855_CR17","doi-asserted-by":"crossref","unstructured":"Rajaguru, H., & Prabhakar, S. K. (2017, October). Time-frequency analysis (dB2 and dB4) for Epilepsy classification with LDA classifier. In 2017 2nd international conference on communication and electronics systems (ICCES) (pp. 708\u2013711). IEEE.","DOI":"10.1109\/CESYS.2017.8321172"},{"issue":"1\u20132","key":"9855_CR18","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2013.03.019","volume":"217","author":"J Rasekhi","year":"2013","unstructured":"Rasekhi, J., Mollaei, M. R. K., Bandarabadi, M., Teixeira, C. A., & Dourado, A. (2013). Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. Journal of Neuroscience Methods, 217(1\u20132), 9\u201316.","journal-title":"Journal of Neuroscience Methods"},{"issue":"11","key":"9855_CR19","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1109\/34.730550","volume":"20","author":"SJ Roberts","year":"1998","unstructured":"Roberts, S. J., Husmeier, D., Rezek, I., & Penny, W. (1998). Bayesian approaches to gaussian mixture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1133\u20131142.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"9855_CR20","first-page":"99","volume":"41","author":"SK Satapathy","year":"2017","unstructured":"Satapathy, S. K., Jagadev, A. K., & Dehuri, S. (2017). Weighted majority voting based ensemble of classifiers using different machine learning techniques for classification of EEG signal to detect an epileptic seizure. Informatica, 41(1), 99.","journal-title":"Informatica"},{"issue":"11","key":"9855_CR21","doi-asserted-by":"publisher","first-page":"5703","DOI":"10.1007\/s12652-020-01938-8","volume":"11","author":"PK Sethy","year":"2020","unstructured":"Sethy, P. K., Barpanda, N. K., Rath, A. K., & Behera, S. K. (2020b). Nitrogen deficiency prediction of rice crop based on convolutional neural network. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5703\u20135711.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"9855_CR22","doi-asserted-by":"crossref","unstructured":"Sethy, P. K., Barpanda, N. K., Rath, A. K., & Behera, S. K. (2020). Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture, 175, 105527.","DOI":"10.1016\/j.compag.2020.105527"},{"key":"9855_CR23","doi-asserted-by":"crossref","unstructured":"Sreeja, S. R., & Samanta, D. (2020). Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications. Multimedia Tools and Applications 1\u201319.","DOI":"10.1007\/s11042-019-08602-0"},{"issue":"12","key":"9855_CR24","doi-asserted-by":"publisher","first-page":"8659","DOI":"10.1016\/j.eswa.2010.06.065","volume":"37","author":"A Subasi","year":"2010","unstructured":"Subasi, A., & Gursoy, M. I. (2010). EEG signal classification using PCA, ICA, LDA, and support vector machines. Expert Systems with Applications, 37(12), 8659\u20138666.","journal-title":"Expert Systems with Applications"},{"key":"9855_CR25","doi-asserted-by":"publisher","first-page":"9871","DOI":"10.1007\/s11042-019-08359-6","volume":"79","author":"K Sujatha","year":"2020","unstructured":"Sujatha, K. (2020). Automatic epilepsy detection using hybrid decomposition with multiclass support vector method. Multimedia Tools Application, 79, 9871\u20139890. https:\/\/doi.org\/10.1007\/s11042-019-08359-6","journal-title":"Multimedia Tools Application"},{"issue":"3","key":"9855_CR26","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.cmpb.2014.02.007","volume":"114","author":"CA Teixeira","year":"2014","unstructured":"Teixeira, C. A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valderrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., & Dourado, A. (2014a). Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine, 114(3), 324\u2013336.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"5","key":"9855_CR28","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/brainsci9050115","volume":"9","author":"\u00d6 T\u00fcrk","year":"2019","unstructured":"T\u00fcrk, \u00d6., & \u00d6zerdem, M. S. (2019). Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sciences, 9(5), 115.","journal-title":"Brain Sciences"},{"key":"9855_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/9074759","volume":"2017","author":"SM Usman","year":"2017","unstructured":"Usman, S. M., Usman, M., & Fong, S. (2017). Epileptic seizures prediction using machine learning methods. Computational and Mathematical Methods in Medicine, 2017, 1\u201310.","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"9855_CR30","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1227\/NEU.0b013e318285b4ae","volume":"72","author":"S Vadera","year":"2013","unstructured":"Vadera, S., Mullin, J., Bulacio, J., Najm, I., Bingaman, W., & Gonzalez-Martinez, J. (2013). Stereo electroencephalography following subdural grid placement for difficult to localize epilepsy. Neurosurgery, 72, 723\u2013729.","journal-title":"Neurosurgery"},{"key":"9855_CR31","doi-asserted-by":"crossref","unstructured":"Venkataraman, V., Vlachos, I., Faith, A., Krishnan, B., Tsakalis, K., Treiman, D., & Iasemidis, L. (2014). 36th annual international conference of the IEEE engineering in medicine and biology society. Brain Dynamics Based Automated Epileptic Seizure Detection (pp. 946\u2013949)","DOI":"10.1109\/EMBC.2014.6943748"},{"key":"9855_CR32","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.measurement.2016.02.059","volume":"86","author":"H Wang","year":"2016","unstructured":"Wang, H., & Zhang, Y. (2016). Detection of motor imagery EEG signals employing Na\u00efve Bayes based learning process. Measurement, 86, 148\u2013158.","journal-title":"Measurement"},{"issue":"2","key":"9855_CR33","doi-asserted-by":"publisher","first-page":"140","DOI":"10.3390\/e22020140","volume":"22","author":"J Wu","year":"2020","unstructured":"Wu, J., Zhou, T., & Li, T. (2020). Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient boosting. Entropy, 22(2), 140.","journal-title":"Entropy"},{"issue":"5","key":"9855_CR34","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1109\/TBME.2012.2237399","volume":"60","author":"S Zandi","year":"2013","unstructured":"Zandi, S., Tafreshi, R., Javidan, M., & Dumont, G. A. (2013). Predicting epileptic seizures in scalp EEG based on a variational bayesian gaussian mixture model of zero-crossing intervals. IEEE Transactions on Biomedical Engineering, 60(5), 1401\u20131413.","journal-title":"IEEE Transactions on Biomedical Engineering"}],"container-title":["International Journal of Speech Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-021-09855-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10772-021-09855-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-021-09855-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T14:09:12Z","timestamp":1699625352000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10772-021-09855-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,30]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["9855"],"URL":"https:\/\/doi.org\/10.1007\/s10772-021-09855-7","relation":{},"ISSN":["1381-2416","1572-8110"],"issn-type":[{"value":"1381-2416","type":"print"},{"value":"1572-8110","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,30]]},"assertion":[{"value":"16 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}