{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T10:51:46Z","timestamp":1786099906205,"version":"3.56.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100004609","name":"Foundation of Henan Educational Committee","doi-asserted-by":"publisher","award":["24A320004"],"award-info":[{"award-number":["24A320004"]}],"id":[{"id":"10.13039\/501100004609","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Science and Technology Projects of Henan Province","award":["221100210500"],"award-info":[{"award-number":["221100210500"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-024-02845-0","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T12:41:04Z","timestamp":1736167264000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection"],"prefix":"10.1186","volume":"25","author":[{"given":"Xiaoshuai","family":"Cao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojie","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jincan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenna","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ganqin","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"2845_CR1","doi-asserted-by":"crossref","unstructured":"I. Tasci, \u201cEpilepsy detection in 121 patient populations using hypercube pattern from EEG signals,\u201d Information Fusion, 2023.","DOI":"10.1016\/j.inffus.2023.03.022"},{"key":"2845_CR2","doi-asserted-by":"publisher","first-page":"108126","DOI":"10.1109\/ACCESS.2023.3317241","volume":"11","author":"HM Emara","year":"2023","unstructured":"Emara HM, El-Shafai W, Algarni AD, Soliman NF, El-Samie FEA. A Hybrid Compressive Sensing and Classification Approach for Dynamic Storage Management of Vital Biomedical Signals. IEEE Access. 2023;11:108126\u201351. https:\/\/doi.org\/10.1109\/ACCESS.2023.3317241.","journal-title":"IEEE Access"},{"issue":"9","key":"2845_CR3","doi-asserted-by":"publisher","first-page":"5595","DOI":"10.1007\/s00521-018-3381-9","volume":"31","author":"MK Siddiqui","year":"2019","unstructured":"Siddiqui MK, Islam MZ, Kabir MA. A novel quick seizure detection and localization through brain data mining on ECoG dataset. Neural Comput & Applic. Sep.2019;31(9):5595\u2013608. https:\/\/doi.org\/10.1007\/s00521-018-3381-9.","journal-title":"Neural Comput & Applic"},{"issue":"1","key":"2845_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40708-020-00105-1","volume":"7","author":"MK Siddiqui","year":"2020","unstructured":"Siddiqui MK, Morales-Menendez R, Huang X, Hussain N. A review of epileptic seizure detection using machine learning classifiers. Brain Inf. Dec.2020;7(1):5. https:\/\/doi.org\/10.1186\/s40708-020-00105-1.","journal-title":"Brain Inf"},{"issue":"1","key":"2845_CR5","doi-asserted-by":"publisher","first-page":"16916","DOI":"10.1038\/s41598-024-67855-4","volume":"14","author":"J Zhang","year":"2024","unstructured":"Zhang J, Zheng S, Chen W, Du G, Fu Q, Jiang H. A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction. Sci Rep. Jul.2024;14(1):16916. https:\/\/doi.org\/10.1038\/s41598-024-67855-4.","journal-title":"Sci Rep"},{"key":"2845_CR6","doi-asserted-by":"publisher","unstructured":"M. K. Siddiqui, M. Z. Islam, and M. A. Kabir, \u201cAnalyzing Performance of Classification Techniques in Detecting Epileptic Seizure,\u201d in Advanced Data Mining and Applications, vol. 10604, G. Cong, W.-C. Peng, W. E. Zhang, C. Li, and A. Sun, Eds., in Lecture Notes in Computer Science, vol. 10604. , Cham: Springer International Publishing, 2017, pp. 386\u2013398. https:\/\/doi.org\/10.1007\/978-3-319-69179-4_27.","DOI":"10.1007\/978-3-319-69179-4_27"},{"key":"2845_CR7","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1109\/RBME.2020.2969915","volume":"14","author":"M-P Hosseini","year":"2021","unstructured":"Hosseini M-P, Hosseini A, Ahi K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev Biomed Eng. 2021;14:204\u201318. https:\/\/doi.org\/10.1109\/RBME.2020.2969915.","journal-title":"IEEE Rev Biomed Eng"},{"issue":"S2","key":"2845_CR8","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s12911-016-0310-7","volume":"16","author":"G Xun","year":"2016","unstructured":"Xun G, Jia X, Zhang A. Detecting epileptic seizures with electroencephalogram via a context-learning model. BMC Med Inform Decis Mak. Jul.2016;16(S2):70. https:\/\/doi.org\/10.1186\/s12911-016-0310-7.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"11","key":"2845_CR9","doi-asserted-by":"publisher","first-page":"5780","DOI":"10.3390\/ijerph18115780","volume":"18","author":"A Shoeibi","year":"2021","unstructured":"Shoeibi A, et al. Epileptic Seizures Detection Using Deep Learning Techniques: A Review. IJERPH. May2021;18(11):5780. https:\/\/doi.org\/10.3390\/ijerph18115780.","journal-title":"IJERPH"},{"key":"2845_CR10","doi-asserted-by":"publisher","unstructured":"W. Mardini, M. M. Bani Yassein, R. Al-Rawashdeh, S. Aljawarneh, Y. Khamayseh, and O. Meqdadi, \u201cEnhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers,\u201d IEEE Access, vol. 8, pp. 24046\u201324055, 2020, https:\/\/doi.org\/10.1109\/ACCESS.2020.2970012.","DOI":"10.1109\/ACCESS.2020.2970012"},{"issue":"4","key":"2845_CR11","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1007\/s12008-020-00715-3","volume":"14","author":"MK Siddiqui","year":"2020","unstructured":"Siddiqui MK, Huang X, Morales-Menendez R, Hussain N, Khatoon K. Machine learning based novel cost-sensitive seizure detection classifier for imbalanced EEG data sets. Int J Interact Des Manuf. Dec.2020;14(4):1491\u2013509. https:\/\/doi.org\/10.1007\/s12008-020-00715-3.","journal-title":"Int J Interact Des Manuf"},{"key":"2845_CR12","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.seizure.2015.01.012","volume":"26","author":"O Faust","year":"2015","unstructured":"Faust O, Acharya UR, Adeli H, Adeli A. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure. Mar.2015;26:56\u201364. https:\/\/doi.org\/10.1016\/j.seizure.2015.01.012.","journal-title":"Seizure"},{"key":"2845_CR13","doi-asserted-by":"publisher","first-page":"59597","DOI":"10.1109\/ACCESS.2021.3073728","volume":"9","author":"A Whata","year":"2021","unstructured":"Whata A, Chimedza C. Deep Learning for SARS COV-2 Genome Sequences. IEEE Access. 2021;9:59597\u2013611. https:\/\/doi.org\/10.1109\/ACCESS.2021.3073728.","journal-title":"IEEE Access"},{"key":"2845_CR14","doi-asserted-by":"publisher","unstructured":"M. K. Siddiqui and M. Z. Islam, \u201cData mining approach in seizure detection,\u201d in 2016 IEEE Region 10 Conference (TENCON), Singapore: IEEE, Nov. 2016, pp. 3579\u20133583. https:\/\/doi.org\/10.1109\/TENCON.2016.7848724.","DOI":"10.1109\/TENCON.2016.7848724"},{"issue":"5","key":"2845_CR15","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1177\/1550059417744890","volume":"49","author":"PPM Shanir","year":"2018","unstructured":"Shanir PPM, Khan KA, Khan YU, Farooq O, Adeli H. Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG. Clin EEG Neurosci. Sep.2018;49(5):351\u201362. https:\/\/doi.org\/10.1177\/1550059417744890.","journal-title":"Clin EEG Neurosci"},{"issue":"3","key":"2845_CR16","doi-asserted-by":"publisher","first-page":"e0173138","DOI":"10.1371\/journal.pone.0173138","volume":"12","author":"D Chen","year":"2017","unstructured":"Chen D, Wan S, Xiang J, Bao FS. A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG. PLoS ONE. Mar.2017;12(3): e0173138. https:\/\/doi.org\/10.1371\/journal.pone.0173138.","journal-title":"PLoS ONE"},{"key":"2845_CR17","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.bspc.2016.09.008","volume":"31","author":"M Li","year":"2017","unstructured":"Li M, Chen W, Zhang T. Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed Signal Process Control. Jan.2017;31:357\u201365. https:\/\/doi.org\/10.1016\/j.bspc.2016.09.008.","journal-title":"Biomed Signal Process Control"},{"issue":"10","key":"2845_CR18","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.3390\/brainsci12101275","volume":"12","author":"X Liu","year":"2022","unstructured":"Liu X, Wang J, Shang J, Liu J, Dai L, Yuan S. Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals. Brain Sci. Sep.2022;12(10):1275. https:\/\/doi.org\/10.3390\/brainsci12101275.","journal-title":"Brain Sci"},{"issue":"11","key":"2845_CR19","doi-asserted-by":"publisher","first-page":"2146","DOI":"10.1109\/TNSRE.2017.2697920","volume":"25","author":"LS Vidyaratne","year":"2017","unstructured":"Vidyaratne LS, Iftekharuddin KM. Real-Time Epileptic Seizure Detection Using EEG. IEEE Trans Neural Syst Rehabil Eng. Nov.2017;25(11):2146\u201356. https:\/\/doi.org\/10.1109\/TNSRE.2017.2697920.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"1","key":"2845_CR20","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/TNSRE.2015.2441835","volume":"24","author":"F Riaz","year":"2016","unstructured":"Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K. EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning. IEEE Trans Neural Syst Rehabil Eng. Jan.2016;24(1):28\u201335. https:\/\/doi.org\/10.1109\/TNSRE.2015.2441835.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"2845_CR21","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1109\/TNSRE.2021.3103210","volume":"29","author":"X Yang","year":"2021","unstructured":"Yang X, Zhao J, Sun Q, Lu J, Ma X. An Effective Dual Self-Attention Residual Network for Seizure Prediction. IEEE Trans Neural Syst Rehabil Eng. 2021;29:1604\u201313. https:\/\/doi.org\/10.1109\/TNSRE.2021.3103210.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"8","key":"2845_CR22","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. Aug.2017;25(8):1100\u20138. https:\/\/doi.org\/10.1109\/TNSRE.2016.2611601.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"2845_CR23","doi-asserted-by":"crossref","unstructured":"A. Anugraha, E. Vinotha, R. Anusha, S. Giridhar, and K. Narasimhan, \u201cA Machine Learning Application for Epileptic Seizure Detection,\u201d 2017.","DOI":"10.1109\/ICCIDS.2017.8272636"},{"key":"2845_CR24","unstructured":"Y. Zhang, Y. Chen, and N. V. Chawla. Automated Epileptic Seizure Detection Using Improved Correlation-based Feature Selection with Random Forest Classifier. 2017."},{"key":"2845_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/6849360","volume":"2017","author":"Y Wang","year":"2017","unstructured":"Wang Y, Li Z, Feng L, Zheng C, Zhang W. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. Comput Math Methods Med. 2017;2017:1\u201310. https:\/\/doi.org\/10.1155\/2017\/6849360.","journal-title":"Comput Math Methods Med"},{"key":"2845_CR26","doi-asserted-by":"crossref","unstructured":"L. Orosco, \u201cPatient non-specific algorithm for seizures detection in scalp EEG,\u201d Computers in Biology and Medicine, 2016.","DOI":"10.1016\/j.compbiomed.2016.02.016"},{"key":"2845_CR27","unstructured":"H. M. Emara, W. El-Shafai, A. D. Algarni, R. Alkanhel, and F. E. A. El-Samie, \u201cCervical Cancer Detection: A Comprehensive Evaluation of CNN Models, Vision Transformer Approaches, and Fusion Strategies,\u201d vol. 4, 2016."},{"issue":"4","key":"2845_CR28","doi-asserted-by":"publisher","first-page":"3914","DOI":"10.1007\/s11227-020-03426-4","volume":"77","author":"S Zhang","year":"2021","unstructured":"Zhang S, Chen D, Ranjan R, Ke H, Tang Y, Zomaya AY. A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement. J Supercomput. Apr.2021;77(4):3914\u201332. https:\/\/doi.org\/10.1007\/s11227-020-03426-4.","journal-title":"J Supercomput"},{"key":"2845_CR29","doi-asserted-by":"publisher","unstructured":"E. Tuncer and E. Do\u011fru Bolat, \u201cClassification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture,\u201d Biomedical Signal Processing and Control, vol. 73, p. 103462, Mar. 2022, https:\/\/doi.org\/10.1016\/j.bspc.2021.103462.","DOI":"10.1016\/j.bspc.2021.103462"},{"issue":"4","key":"2845_CR30","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1002\/ima.22199","volume":"26","author":"S Deivasigamani","year":"2016","unstructured":"Deivasigamani S, Senthilpari C, Yong WH. Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection. Int J Imaging Syst Technol. Dec.2016;26(4):277\u201383. https:\/\/doi.org\/10.1002\/ima.22199.","journal-title":"Int J Imaging Syst Technol"},{"key":"2845_CR31","doi-asserted-by":"publisher","first-page":"375","DOI":"10.3389\/fneur.2020.00375","volume":"11","author":"Y Gao","year":"2020","unstructured":"Gao Y, Gao B, Chen Q, Liu J, Zhang Y. Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification. Front Neurol. May2020;11:375. https:\/\/doi.org\/10.3389\/fneur.2020.00375.","journal-title":"Front Neurol"},{"key":"2845_CR32","doi-asserted-by":"publisher","unstructured":"E. Ali, R. K. Udhayakumar, M. Angelova, and C. Karmakar, \u201cPerformance Analysis of Entropy Methods in Detecting Epileptic Seizure from Surface Electroencephalograms,\u201d in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico: IEEE, Nov. 2021, pp. 1082\u20131085. https:\/\/doi.org\/10.1109\/EMBC46164.2021.9629538.","DOI":"10.1109\/EMBC46164.2021.9629538"},{"key":"2845_CR33","doi-asserted-by":"publisher","first-page":"607","DOI":"10.3389\/fphys.2020.00607","volume":"11","author":"ST Aung","year":"2020","unstructured":"Aung ST, Wongsawat Y. Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures. Front Physiol. Jun.2020;11:607. https:\/\/doi.org\/10.3389\/fphys.2020.00607.","journal-title":"Front Physiol"},{"key":"2845_CR34","doi-asserted-by":"crossref","unstructured":"M. S. Fathillah, R. Jaafar, K. Chellappan, R. Remli, W. Asyraf, and W. Zainal, \u201cMultiresolution analysis on nonlinear complexity measurement of EEG signal for epileptic discharge monitoring,\u201d vol. 14, no. 2, 2018.","DOI":"10.11113\/mjfas.v14n2.821"},{"issue":"22","key":"2845_CR35","doi-asserted-by":"publisher","first-page":"7710","DOI":"10.3390\/s21227710","volume":"21","author":"A Malekzadeh","year":"2021","unstructured":"Malekzadeh A, Zare A, Yaghoobi M, Kobravi H-R, Alizadehsani R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors. Nov.2021;21(22):7710. https:\/\/doi.org\/10.3390\/s21227710.","journal-title":"Sensors"},{"key":"2845_CR36","doi-asserted-by":"publisher","first-page":"104644","DOI":"10.1016\/j.bspc.2023.104644","volume":"83","author":"X Qin","year":"2023","unstructured":"Qin X, Xu D, Dong X, Cui X, Zhang S. EEG signal classification based on improved variational mode decomposition and deep forest. Biomed Signal Process Control. May2023;83: 104644. https:\/\/doi.org\/10.1016\/j.bspc.2023.104644.","journal-title":"Biomed Signal Process Control"},{"issue":"1\u20134","key":"2845_CR37","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/S0925-2312(01)00644-0","volume":"48","author":"JAK Suykens","year":"2002","unstructured":"Suykens JAK, De Brabanter J, Lukas L, Vandewalle J. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing. Oct.2002;48(1\u20134):85\u2013105. https:\/\/doi.org\/10.1016\/S0925-2312(01)00644-0.","journal-title":"Neurocomputing"},{"key":"2845_CR38","doi-asserted-by":"crossref","unstructured":"G. Altan and G. Inat, \u201cEEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform Ampirik Dalgac\u0131k D\u00f6n\u00fcs\u00b8\u00fcm\u00fcnde Zaman-Frekans \u00d6zniteliklerini Kullanarak EEG tabanl\u0131 Uzamsal Dikkat Kaymas\u0131 Tespiti,\u201d 2021.","DOI":"10.54856\/10.54856\/jiswa.202112181"},{"key":"2845_CR39","doi-asserted-by":"publisher","first-page":"104250","DOI":"10.1016\/j.compbiomed.2021.104250","volume":"131","author":"A Zarei","year":"2021","unstructured":"Zarei A, Asl BM. Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals. Comput Biol Med. Apr.2021;131: 104250. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104250.","journal-title":"Comput Biol Med"},{"issue":"7","key":"2845_CR40","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/3448250","volume":"64","author":"Y Bengio","year":"2021","unstructured":"Bengio Y, Lecun Y, Hinton G. Deep learning for AI. Commun ACM. Jul.2021;64(7):58\u201365. https:\/\/doi.org\/10.1145\/3448250.","journal-title":"Commun ACM"},{"issue":"1","key":"2845_CR41","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s12911-023-02180-w","volume":"23","author":"W Chen","year":"2023","unstructured":"Chen W, et al. An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy. BMC Med Inform Decis Mak. May2023;23(1):96. https:\/\/doi.org\/10.1186\/s12911-023-02180-w.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"16","key":"2845_CR42","doi-asserted-by":"publisher","first-page":"4639","DOI":"10.3390\/s20164639","volume":"20","author":"MdKI Molla","year":"2020","unstructured":"Molla MdKI, Hassan KM, Islam MdR, Tanaka T. Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography. Sensors. Aug.2020;20(16):4639. https:\/\/doi.org\/10.3390\/s20164639.","journal-title":"Sensors"},{"key":"2845_CR43","doi-asserted-by":"crossref","unstructured":"R. Bajpai, \u201cAutomated EEG pathology detection based on different convolutional neural network models: Deep learning approach,\u201d Computers in Biology and Medicine, 2021.","DOI":"10.1016\/j.compbiomed.2021.104434"},{"issue":"4","key":"2845_CR44","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1007\/s11063-021-10533-7","volume":"53","author":"G Altan","year":"2021","unstructured":"Altan G, Yay\u0131k A, Kutlu Y. Deep Learning with ConvNet Predicts Imagery Tasks Through EEG. Neural Process Lett. Aug.2021;53(4):2917\u201332. https:\/\/doi.org\/10.1007\/s11063-021-10533-7.","journal-title":"Neural Process Lett"},{"issue":"1","key":"2845_CR45","doi-asserted-by":"publisher","first-page":"14876","DOI":"10.1038\/s41598-023-41537-z","volume":"13","author":"X Wang","year":"2023","unstructured":"Wang X, Wang Y, Liu D, Wang Y, Wang Z. Automated recognition of epilepsy from EEG signals using a combining space\u2013time algorithm of CNN-LSTM. Sci Rep. Sep.2023;13(1):14876. https:\/\/doi.org\/10.1038\/s41598-023-41537-z.","journal-title":"Sci Rep"},{"key":"2845_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/9579422","volume":"2022","author":"F Hassan","year":"2022","unstructured":"Hassan F, Hussain SF, Qaisar SM. Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data. Journal of Healthcare Engineering. Nov.2022;2022:1\u201316. https:\/\/doi.org\/10.1155\/2022\/9579422.","journal-title":"Journal of Healthcare Engineering"},{"key":"2845_CR47","doi-asserted-by":"publisher","unstructured":"A. Subasi, J. Kevric, and M. Abdullah Canbaz, \u201cEpileptic seizure detection using hybrid machine learning methods,\u201d Neural Comput & Applic, vol. 31, no. 1, pp. 317\u2013325, Jan. 2019, https:\/\/doi.org\/10.1007\/s00521-017-3003-y.","DOI":"10.1007\/s00521-017-3003-y"},{"key":"2845_CR48","doi-asserted-by":"publisher","first-page":"102147","DOI":"10.1016\/j.bspc.2020.102147","volume":"63","author":"S Chen","year":"2021","unstructured":"Chen S, Chen L, Zhang X, Yang Z. Screening of cardiac disease based on integrated modeling of heart rate variability. Biomed Signal Process Control. Jan.2021;63: 102147. https:\/\/doi.org\/10.1016\/j.bspc.2020.102147.","journal-title":"Biomed Signal Process Control"},{"key":"2845_CR49","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.bspc.2017.05.002","volume":"38","author":"MN Tibdewal","year":"2017","unstructured":"Tibdewal MN, Dey HR, Mahadevappa M, Ray A, Malokar M. Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG. Biomed Signal Process Control. Sep.2017;38:158\u201367. https:\/\/doi.org\/10.1016\/j.bspc.2017.05.002.","journal-title":"Biomed Signal Process Control"},{"issue":"04","key":"2845_CR50","doi-asserted-by":"publisher","first-page":"1950016","DOI":"10.1142\/S0219843619500166","volume":"16","author":"D Wu","year":"2019","unstructured":"Wu D, et al. Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG. Int J Human Robot. Aug.2019;16(04):1950016. https:\/\/doi.org\/10.1142\/S0219843619500166.","journal-title":"Int J Human Robot"},{"key":"2845_CR51","doi-asserted-by":"publisher","unstructured":"H. Wang, W. Shi, and C.-S. Choy, \u201cIntegrating channel selection and feature selection in a real time epileptic seizure detection system,\u201d in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo: IEEE, Jul. 2017, pp. 3206\u20133211. https:\/\/doi.org\/10.1109\/EMBC.2017.8037539.","DOI":"10.1109\/EMBC.2017.8037539"},{"key":"2845_CR52","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.snb.2015.02.025","volume":"212","author":"K Yan","year":"2015","unstructured":"Yan K, Zhang D. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens Actuators, B Chem. Jun.2015;212:353\u201363. https:\/\/doi.org\/10.1016\/j.snb.2015.02.025.","journal-title":"Sens Actuators, B Chem"},{"key":"2845_CR53","unstructured":"Y. Kim et al., \u201cComparative studies for developing protein based cancer prediction model to maximise the ROC-AUC with various variable selection methods,\u201d South Korea."},{"key":"2845_CR54","doi-asserted-by":"publisher","first-page":"101091","DOI":"10.1016\/j.jestch.2021.101091","volume":"34","author":"G Altan","year":"2022","unstructured":"Altan G. DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images. Engineering Science and Technology, an International Journal. Oct.2022;34: 101091. https:\/\/doi.org\/10.1016\/j.jestch.2021.101091.","journal-title":"Engineering Science and Technology, an International Journal"},{"key":"2845_CR55","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","volume":"100","author":"UR Acharya","year":"2018","unstructured":"Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med. Sep.2018;100:270\u20138. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.09.017.","journal-title":"Comput Biol Med"},{"issue":"8","key":"2845_CR56","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Comput. Nov.1997;9(8):1735\u201380. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735.","journal-title":"Neural Comput"},{"key":"2845_CR57","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.05.019","volume":"99","author":"\u039a\u039c Tsiouris","year":"2018","unstructured":"Tsiouris \u039a\u039c, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI. A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med. Aug.2018;99:24\u201337. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.05.019.","journal-title":"Comput Biol Med"},{"key":"2845_CR58","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.bspc.2018.04.007","volume":"44","author":"AG Mahapatra","year":"2018","unstructured":"Mahapatra AG, Horio K. Classification of ictal and interictal EEG using RMS frequency, dominant frequency, root mean instantaneous frequency square and their parameters ratio. Biomed Signal Process Control. Jul.2018;44:168\u201380. https:\/\/doi.org\/10.1016\/j.bspc.2018.04.007.","journal-title":"Biomed Signal Process Control"},{"key":"2845_CR59","doi-asserted-by":"crossref","unstructured":"H. Al-Hadeethi, S. Abdulla, M. Diykh, and J. H. Green, \u201cDeterminant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection,\u201d 2022.","DOI":"10.3390\/diagnostics12010074"},{"key":"2845_CR60","unstructured":"J. Gwak, \u201cFuzzy-Based Automatic Epileptic Seizure Detection Framework,\u201d 2022."},{"key":"2845_CR61","doi-asserted-by":"crossref","unstructured":"L. Huang, \u201cAutomatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer,\u201d 2024.","DOI":"10.1186\/s12938-024-01244-w"},{"issue":"6","key":"2845_CR62","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.jksuci.2018.04.014","volume":"33","author":"V Harpale","year":"2021","unstructured":"Harpale V, Bairagi V. An adaptive method for feature selection and extraction for classification of epileptic EEG signal in significant states. Journal of King Saud University - Computer and Information Sciences. Jul.2021;33(6):668\u201376. https:\/\/doi.org\/10.1016\/j.jksuci.2018.04.014.","journal-title":"Journal of King Saud University - Computer and Information Sciences"},{"issue":"4","key":"2845_CR63","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1109\/TNSRE.2020.2973434","volume":"28","author":"Y Li","year":"2020","unstructured":"Li Y, Liu Y, Cui W-G, Guo Y-Z, Huang H, Hu Z-Y. Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network. IEEE Trans Neural Syst Rehabil Eng. Apr.2020;28(4):782\u201394. https:\/\/doi.org\/10.1109\/TNSRE.2020.2973434.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"2845_CR64","doi-asserted-by":"crossref","unstructured":"M. K. Alharthi, K. M. Moria, D. M. Alghazzawi, and H. O. Tayeb, \u201cEpileptic Disorder Detection of Seizures Using EEG Signals,\u201d 2022.","DOI":"10.3390\/s22176592"},{"key":"2845_CR65","doi-asserted-by":"crossref","unstructured":"C. S. L. Prasanna, Z. U. Rahman, and M. D. Bayleyegn, \u201cBrain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier,\u201d vol. 11, 2023.","DOI":"10.1109\/ACCESS.2023.3312187"},{"key":"2845_CR66","unstructured":"M. Wu, \u201cSeizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model\u201d."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02845-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02845-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02845-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T13:05:47Z","timestamp":1736168747000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02845-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2845"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02845-0","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"14 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}