{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:53:48Z","timestamp":1742921628816,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031741821"},{"type":"electronic","value":"9783031741838"}],"license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-74183-8_2","type":"book-chapter","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T07:04:34Z","timestamp":1728371074000},"page":"16-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Batch-Balancing Improvement with\u00a0Data Augmentation Techniques for\u00a0Clinical Electroencephalographic Data"],"prefix":"10.1007","author":[{"given":"David","family":"Fern\u00e1ndez-Madera Gonz\u00e1lez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6652-9287","authenticated-orcid":false,"given":"Fernando","family":"Moncada Martins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0937-1882","authenticated-orcid":false,"given":"V\u00edctor M.","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6024-9527","authenticated-orcid":false,"given":"Jos\u00e9 R.","family":"Villar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1364-2603","authenticated-orcid":false,"given":"Beatriz","family":"Garc\u00eda L\u00f3pez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana Isabel","family":"G\u00f3mez-Men\u00e9ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Abdelhameed, A., Bayoumi, M.: A deep learning approach for automatic seizure detection in children with epilepsy. Front. Comput. Neurosci. 15, 29 (2021). https:\/\/doi.org\/10.3389\/fncom.2021.650050","DOI":"10.3389\/fncom.2021.650050"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Chakrabarti, S., Swetapadma, A., Pattnaik, P.K.: A channel independent generalized seizure detection method for pediatric epileptic seizures. Comput. Methods and Programs in Biomed. 209, 106335 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2021.106335, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260721004090","DOI":"10.1016\/j.cmpb.2021.106335"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Garc\u00eda, E., Villar, M., F\u00e1\u00f1ez, M., Villar, J.R., de la Cal, E., Cho, S.B.: Towards effective detection of elderly falls with CNN-LSTM neural networks. Neurocomputing 500, 231\u2013240 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.06.102, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231222006440","DOI":"10.1016\/j.neucom.2021.06.102"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"He, Z., et al.: Single-channel EEG sleep staging based on data augmentation and cross-subject discrepancy alleviation. Comput. Biol. Med. 149, 106044 (2022). 10.1016\/j.compbiomed.2022.106044, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0010482522007612","DOI":"10.1016\/j.compbiomed.2022.106044"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Hossain, M.S., Amin, S.U., Alsulaiman, M., Muhammad, G.: Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Trans. Multimedia Comput. Commun. Appl. 15(1s) (2019). https:\/\/doi.org\/10.1145\/3241056.","DOI":"10.1145\/3241056"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Ibrahim, F.E., et al.: Deep-learning-based seizure detection and prediction from electroencephalography signals. Int. J. Numer. Methods Biomed. Eng. 38(6), e3573 (2022). https:\/\/doi.org\/10.1002\/cnm.3573, https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/cnm.3573","DOI":"10.1002\/cnm.3573"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Ismail\u00a0Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1367\u20131376 (2018). https:\/\/doi.org\/10.1109\/BigData.2018.8621990","DOI":"10.1109\/BigData.2018.8621990"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Iwana, B.K., Uchida, S.: An empirical survey of data augmentation for time series classification with neural networks. PLoS ONE 16(7) (2021). https:\/\/doi.org\/10.1371\/journal.pone.0254841","DOI":"10.1371\/journal.pone.0254841"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Jacaruso, L.C.: Accuracy improvement for fully convolutional networks via selective augmentation with applications to electrocardiogram data. Inf. Med. Unlocked 26, 100729 (2021). 10.1016\/j.imu.2021.100729, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352914821002082","DOI":"10.1016\/j.imu.2021.100729"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Kalitzin, S., Parra, J., Velis, D., Lopes\u00a0da Silva, F.: Enhancement of phase clustering in the EEG\/MEG gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with know visual sensitivity. IEEE Trans. Bio-med. Eng. 49, 1279\u201386 (2002). https:\/\/doi.org\/10.1109\/TBME.2002.804593","DOI":"10.1109\/TBME.2002.804593"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Martins, F.M., Gonz\u00e1lez, V.M., Garc\u00eda, B., \u00c1lvarez, V., Villar, J.R.: A comparison of machine learning techniques for the detection o type-4 photoparoxysmal responses in electroencephalographic signals. In: Hybrid Artificial Intelligent Systems, pp. 3\u201313. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-15471-3_1","DOI":"10.1007\/978-3-031-15471-3_1"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Martins, F.M., Su\u00e1rez, V.M.G., Flecha, J.R.V., L\u00f3pez, B.G.: Data augmentation effects on highly imbalanced EEG datasets for automatic detection of photoparoxysmal responses. Sensors 23(4) (2023). https:\/\/doi.org\/10.3390\/s23042312, https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2312","DOI":"10.3390\/s23042312"},{"issue":"8","key":"2_CR13","doi-asserted-by":"publisher","first-page":"5643","DOI":"10.1007\/s00521-022-06940-z","volume":"35","author":"F Moncada","year":"2022","unstructured":"Moncada, F., et al.: Virtual reality and machine learning in the automatic photoparoxysmal response detection. Neural Comput. Appl. 35(8), 5643\u20135659 (2022). https:\/\/doi.org\/10.1007\/s00521-022-06940-z","journal-title":"Neural Comput. Appl."},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Rasheed, K., et al.: Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev. Biomed. Eng. 14, 139\u2013155 (2021). https:\/\/doi.org\/10.1109\/RBME.2020.3008792","DOI":"10.1109\/RBME.2020.3008792"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Shu, K., Zhao, Y., Wu, L., Liu, A., Qian, R., Chen, X.: Data augmentation for seizure prediction with generative diffusion model (2023)","DOI":"10.1109\/TCDS.2024.3489357"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Strigaro, G., Gori, B., Varrasi, C., Fleetwood, T., Cantello, G., Cantello, R.: Flash-evoked high-frequency EEG oscillations in photosensitive epilepsies. Epilepsy Res. 172, 106597 (2021). https:\/\/doi.org\/10.1016\/j.eplepsyres.2021.106597, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0920121121000504","DOI":"10.1016\/j.eplepsyres.2021.106597"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: Seizure detection using wearable sensors and machine learning: setting a benchmark. Epilepsia 62(8), 1807\u20131819 (2021). 10.1111\/epi.16967, https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/epi.16967","DOI":"10.1111\/epi.16967"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"International Federation of Clinical Neurophysiology: Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 10(2) (1958). https:\/\/doi.org\/10.1016\/0013-4694(58)90053-1","DOI":"10.1016\/0013-4694(58)90053-1"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Trenit\u00e9, D.G.N., Binnie, C.D., Harding, G.F., Wilkins, A.: Photic stimulation: Standardization of screening methods. Epilepsia 40(9) (1999). https:\/\/doi.org\/10.1111\/j.1528-1157.1999.tb00911.x","DOI":"10.1111\/j.1528-1157.1999.tb00911.x"},{"key":"2_CR20","unstructured":"Trenit\u00e9, D.K.N.: Photosensitivity in epilepsy. Electrophisiological and clinical correlates. Acta Neurol. Scand. Suppl. 125, 3\u2013149 (1989)"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Waltz, S., Christen, H.J., Doose, H.: The different patterns of the photoparoxysmal response - a genetic study. Electroencephalogr. Clin. Neurophysiol. 83(2) (1992). https:\/\/doi.org\/10.1016\/0013-4694(92)90027-F","DOI":"10.1016\/0013-4694(92)90027-F"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74183-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T06:34:03Z","timestamp":1732862043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74183-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"ISBN":["9783031741821","9783031741838"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74183-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,9]]},"assertion":[{"value":"9 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University Hospital of Burgos (Protocol Code CEIm2467, 23 February 2021). Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the patients to publish this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}