{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T18:55:09Z","timestamp":1725562509555},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1016\/j.inffus.2023.03.022","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T19:39:10Z","timestamp":1680291550000},"page":"252-268","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":38,"special_numbering":"C","title":["Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals"],"prefix":"10.1016","volume":"96","author":[{"given":"Irem","family":"Tasci","sequence":"first","affiliation":[]},{"given":"Burak","family":"Tasci","sequence":"additional","affiliation":[]},{"given":"Prabal D.","family":"Barua","sequence":"additional","affiliation":[]},{"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[]},{"given":"Elizabeth Emma","family":"Palmer","sequence":"additional","affiliation":[]},{"given":"Hamido","family":"Fujita","sequence":"additional","affiliation":[]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.inffus.2023.03.022_bib0001","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0140-6736(18)32596-0","article-title":"Epilepsy in adults","volume":"393","author":"Thijs","year":"2019","journal-title":"Lancet"},{"key":"10.1016\/j.inffus.2023.03.022_bib0002","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1111\/epi.12550","article-title":"ILAE official report: a practical clinical definition of epilepsy","volume":"55","author":"Fisher","year":"2014","journal-title":"Epilepsia"},{"key":"10.1016\/j.inffus.2023.03.022_bib0003","series-title":"Introduction to the Epilepsy Syndrome Papers","first-page":"1330","author":"Wirrell","year":"2022"},{"key":"10.1016\/j.inffus.2023.03.022_bib0004","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1111\/epi.13709","article-title":"ILAE classification of the epilepsies: position paper of the ILAE commission for classification and terminology","volume":"58","author":"Scheffer","year":"2017","journal-title":"Epilepsia"},{"key":"10.1016\/j.inffus.2023.03.022_bib0005","first-page":"1","article-title":"Epilepsy","volume":"4","author":"Orrin","year":"2018","journal-title":"Nat. Rev. Disease Primers"},{"key":"10.1016\/j.inffus.2023.03.022_bib0006","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.pediatrneurol.2022.10.004","article-title":"Levetiracetam versus Phenobarbital for Neonatal Seizures: a Retrospective Cohort Study","volume":"138","author":"B\u00e4ttig","year":"2023","journal-title":"Pediatr. Neurol."},{"key":"10.1016\/j.inffus.2023.03.022_bib0007","first-page":"23","article-title":"Epilepsi Tan\u0131 ve Tedavisinde Ektroensefalografinin (EEG) Yeri","author":"YAVUZ","year":"2010","journal-title":"Klinik Geli\u015fim Dergisi"},{"key":"10.1016\/j.inffus.2023.03.022_bib0008","doi-asserted-by":"crossref","first-page":"321","DOI":"10.29309\/TPMJ\/2015.22.03.1349","article-title":"Computed tomography (CT) Scan: ring enhancing lesions on brain","volume":"22","author":"Shahwani","year":"2015","journal-title":"The Professional Med. J."},{"key":"10.1016\/j.inffus.2023.03.022_bib0009","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1212\/01.wnl.0000285084.93652.43","article-title":"Practice parameter: evaluating an apparent unprovoked first seizure in adults (an evidence-based review):[RETIRED]: report of the quality standards subcommittee of the american academy of neurology and the American epilepsy society","volume":"69","author":"Krumholz","year":"2007","journal-title":"Neurology"},{"key":"10.1016\/j.inffus.2023.03.022_bib0010","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/S0140-6736(98)03543-0","article-title":"Epileptology of the first-seizure presentation: a clinical, electroencephalographic, and magnetic resonance imaging study of 300 consecutive patients","volume":"352","author":"King","year":"1998","journal-title":"The Lancet"},{"key":"10.1016\/j.inffus.2023.03.022_bib0011","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.yebeh.2014.06.023","article-title":"Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy","volume":"37","author":"Ramgopal","year":"2014","journal-title":"Epilepsy & Behav."},{"key":"10.1016\/j.inffus.2023.03.022_bib0012","doi-asserted-by":"crossref","DOI":"10.1152\/jn.00024.2022","article-title":"Background suppression of electrical activity is a potential biomarker of subsequent brain injury in a rat model of neonatal hypoxia-ischemia","author":"Zayachkivsky","year":"2022","journal-title":"J. Neurophysiol."},{"key":"10.1016\/j.inffus.2023.03.022_bib0013","article-title":"Epileptic EEG activity detection for children using entropy-based biomarkers","volume":"2","author":"Kbah","year":"2022","journal-title":"Neurosci. Inf."},{"key":"10.1016\/j.inffus.2023.03.022_bib0014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-26590-4","article-title":"Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy","volume":"13","author":"Bauer","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.inffus.2023.03.022_bib0015","article-title":"EEG based classification of children with learning disabilities using shallow and deep neural network","volume":"82","author":"Seshadri","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0016","first-page":"1","article-title":"Deep learning based epileptic seizure detection with EEG data","author":"Poorani","year":"2023","journal-title":"Int. J. Syst. Assurance Eng. Manag."},{"key":"10.1016\/j.inffus.2023.03.022_bib0017","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.inffus.2022.12.019","article-title":"Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques","volume":"92","author":"Hassan","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2023.03.022_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104519","article-title":"CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features","volume":"82","author":"Assali","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107277","article-title":"Automatic seizure detection by convolutional neural networks with computational complexity analysis","volume":"229","author":"Cimr","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.inffus.2023.03.022_bib0020","series-title":"2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)","first-page":"675","article-title":"Epileptic seizure detection using bidimensional empirical mode decomposition and distance metric learning on scalogram","author":"Sheoran","year":"2020"},{"key":"10.1016\/j.inffus.2023.03.022_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102987","article-title":"A novel deep learning based method for COVID-19 detection from CT image","volume":"70","author":"JavadiMoghaddam","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0022","article-title":"A deep learning based ensemble learning method for epileptic seizure prediction","volume":"136","author":"Usman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.inffus.2023.03.022_bib0023","doi-asserted-by":"crossref","first-page":"7661","DOI":"10.3390\/app11167661","article-title":"A hybrid DenseNet-LSTM model for epileptic seizure prediction","volume":"11","author":"Ryu","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.inffus.2023.03.022_bib0024","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13538-022-01231-3","article-title":"Analytic representation vs. Angle modulation of hilbert transform of fast Walsh-Hadamard coefficients (HTFWHC) in epileptic EEG classification","volume":"53","author":"Goshvarpour","year":"2023","journal-title":"Brazilian J. Phys."},{"key":"10.1016\/j.inffus.2023.03.022_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104684","article-title":"HVD-LSTM based recognition of epileptic seizures and normal human activity","volume":"136","author":"Khan","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.inffus.2023.03.022_bib0026","doi-asserted-by":"crossref","DOI":"10.1080\/19420889.2022.2153648","article-title":"A DM-ELM based classifier for EEG brain signal classification for epileptic seizure detection","volume":"16","author":"Mishra","year":"2023","journal-title":"Commun. Integr. Biol."},{"key":"10.1016\/j.inffus.2023.03.022_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104022","article-title":"Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing transform","volume":"79","author":"Amiri","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0028","doi-asserted-by":"crossref","first-page":"13521","DOI":"10.1007\/s10586-018-1995-4","article-title":"Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition","volume":"22","author":"Kumar","year":"2019","journal-title":"Cluster Comput."},{"key":"10.1016\/j.inffus.2023.03.022_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104250","article-title":"Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals","volume":"131","author":"Zarei","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.inffus.2023.03.022_bib0030","first-page":"1","article-title":"A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data","volume":"9","author":"Rashed-Al-Mahfuz","year":"2021","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"10.1016\/j.inffus.2023.03.022_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104566","article-title":"Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network","volume":"82","author":"Shen","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104441","article-title":"Interactive local and global feature coupling for EEG-based epileptic seizure detection","volume":"81","author":"Zhao","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104652","article-title":"A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal","volume":"83","author":"Qiu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0034","doi-asserted-by":"crossref","first-page":"773","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":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.inffus.2023.03.022_bib0035","doi-asserted-by":"crossref","DOI":"10.1002\/epi4.12686","article-title":"Status of epilepsy in the tropics: an overlooked perspective","author":"Liu","year":"2023","journal-title":"Epilepsia Open"},{"key":"10.1016\/j.inffus.2023.03.022_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.yebeh.2022.109063","article-title":"Prevalence of headache disorders in patients living with epilepsy in rural region in western part of India","volume":"139","author":"Chovatiya","year":"2023","journal-title":"Epilepsy & Behav."},{"key":"10.1016\/j.inffus.2023.03.022_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104449","article-title":"Patient-specific method for predicting epileptic seizures based on DRSN-GRU","volume":"81","author":"Xu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0038","series-title":"Fusion of Multivariate EEG Signals for Schizophrenia Detection Using CNN and Machine Learning Techniques","author":"Hassan","year":"2022"},{"key":"10.1016\/j.inffus.2023.03.022_bib0039","first-page":"1542","article-title":"Epileptic state classification by fusing hand-crafted and deep learning EEG features","volume":"68","author":"Hu","year":"2020","journal-title":"IEEE Trans. Circuits and Syst. II: Express Briefs"},{"key":"10.1016\/j.inffus.2023.03.022_bib0040","series-title":"Deep Learning For Anomaly Detection in Multivariate Time Series: Approaches, Applications, and Challenges","author":"Li","year":"2022"},{"key":"10.1016\/j.inffus.2023.03.022_bib0041","doi-asserted-by":"crossref","first-page":"7784","DOI":"10.1109\/JSEN.2016.2602840","article-title":"Channel state reconstruction using multilevel discrete wavelet transform for improved fingerprinting-based indoor localization","volume":"16","author":"Fang","year":"2016","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.inffus.2023.03.022_bib0042","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.eswa.2018.06.031","article-title":"Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms","volume":"113","author":"Raghu","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.inffus.2023.03.022_bib0043","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia,"},{"key":"10.1016\/j.inffus.2023.03.022_bib0044","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/MSP.2021.3110108","article-title":"Augmented\/mixed reality audio for hearables: sensing, control, and rendering","volume":"39","author":"Gupta","year":"2022","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.inffus.2023.03.022_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2021.103645","article-title":"An automated location detection method in multi-storey buildings using environmental sound classification based on a new center symmetric nonlinear pattern: cS-LBlock-Pat","volume":"125","author":"Okaba","year":"2021","journal-title":"Automation in Construction"},{"key":"10.1016\/j.inffus.2023.03.022_bib0046","first-page":"1","article-title":"A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning","author":"Subasi","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"10.1016\/j.inffus.2023.03.022_bib0047","series-title":"Multimodal AI in Healthcare","first-page":"245","article-title":"Predicting ICU admissions for hospitalized COVID-19 patients with a factor graph-based model","author":"Cao","year":"2023"},{"key":"10.1016\/j.inffus.2023.03.022_bib0048","first-page":"2173","volume":"34","author":"Kuncan","year":"2019"},{"key":"10.1016\/j.inffus.2023.03.022_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104867","article-title":"PrimePatNet87: prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition","volume":"138","author":"Dogan","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.inffus.2023.03.022_bib0050","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3389\/fnins.2016.00196","article-title":"The temple university hospital EEG data corpus","volume":"10","author":"Obeid","year":"2016","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.inffus.2023.03.022_bib0051","doi-asserted-by":"crossref","first-page":"728","DOI":"10.3390\/s22030728","article-title":"Epileptic-net: an improved epileptic seizure detection system using dense convolutional block with attention network from EEG","volume":"22","author":"Islam","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.inffus.2023.03.022_bib0052","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065720500744","article-title":"Automated adult epilepsy diagnostic tool based on interictal scalp electroencephalogram characteristics: a six-center study","volume":"31","author":"Thomas","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.inffus.2023.03.022_bib0053","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065721500325","article-title":"Time\u2013frequency decomposition of scalp electroencephalograms improves deep learning-based epilepsy diagnosis","volume":"31","author":"Thangavel","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.inffus.2023.03.022_bib0054","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.3390\/brainsci12121731","article-title":"EEG microstate features as an automatic recognition model of high-density epileptic EEG using support vector machine","volume":"12","author":"Yang","year":"2022","journal-title":"Brain Sci."},{"key":"10.1016\/j.inffus.2023.03.022_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103248","article-title":"Classification and identification of epileptic EEG signals based on signal enhancement","volume":"71","author":"Jing","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.inffus.2023.03.022_bib0056","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1109\/TNSRE.2020.3035836","article-title":"Seizure prediction using directed transfer function and convolution neural network on intracranial EEG","volume":"28","author":"Wang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabilitation Eng."},{"key":"10.1016\/j.inffus.2023.03.022_bib0057","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/JBHI.2020.2984238","article-title":"Identification of children at risk of schizophrenia via deep learning and EEG responses","volume":"25","author":"Ahmedt-Aristizabal","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.inffus.2023.03.022_bib0058","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neucom.2018.10.108","article-title":"Scalp EEG epileptogenic zone recognition and localization based on long-term recurrent convolutional network","volume":"396","author":"Liang","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.inffus.2023.03.022_bib0059","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.neunet.2020.01.017","article-title":"EEG based multi-class seizure type classification using convolutional neural network and transfer learning","volume":"124","author":"Raghu","year":"2020","journal-title":"Neural Networks"},{"key":"10.1016\/j.inffus.2023.03.022_bib0060","series-title":"TSD: Transformers for Seizure Detection","author":"Ma","year":"2023"},{"key":"10.1016\/j.inffus.2023.03.022_bib0061","doi-asserted-by":"crossref","first-page":"2852","DOI":"10.1109\/JBHI.2020.2971610","article-title":"Adversarial representation learning for robust patient-independent epileptic seizure detection","volume":"24","author":"Zhang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.inffus.2023.03.022_bib0062","series-title":"Unsupervised Multivariate Time-Series Transformers for Seizure Identification On EEG","author":"Potter","year":"2023"},{"key":"10.1016\/j.inffus.2023.03.022_bib0063","doi-asserted-by":"crossref","first-page":"15857","DOI":"10.1007\/s00521-018-3889-z","article-title":"A deep convolutional neural network model for automated identification of abnormal EEG signals","volume":"32","author":"Y\u0131ld\u0131r\u0131m","year":"2020","journal-title":"Neural Comput. App."},{"key":"10.1016\/j.inffus.2023.03.022_bib0064","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.patrec.2020.03.009","article-title":"Automated detection of abnormal EEG signals using localized wavelet filter banks","volume":"133","author":"Sharma","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.inffus.2023.03.022_bib0065","series-title":"2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","first-page":"1","article-title":"A deep learning-based method for automatic detection of epileptic seizure in a dataset with both generalized and focal seizure types","author":"Einizade","year":"2020"},{"key":"10.1016\/j.inffus.2023.03.022_bib0066","first-page":"2022","article-title":"An efficient signal processing algorithm for detecting abnormalities in EEG signal using CNN","author":"Syamsundararao","year":"2022","journal-title":"Contrast Media Mol. Imaging"},{"key":"10.1016\/j.inffus.2023.03.022_bib0067","series-title":"Epilepsy-Net: Attention-Based 1D-Inception Network Model For Epilepsy Detection Using One-Channel and Multi-Channel EEG Signals","first-page":"1","author":"Lebal","year":"2022"},{"key":"10.1016\/j.inffus.2023.03.022_bib0068","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.inffus.2023.03.022_bib0069","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107161","article-title":"Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011\u20132022)","author":"Loh","year":"2022","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253523001112?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253523001112?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T11:38:39Z","timestamp":1682163519000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253523001112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":69,"alternative-id":["S1566253523001112"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2023.03.022","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2023.03.022","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}