Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) exhibit drug resistance, urging the need to develop new treatments. A key part in anti-seizure medication (ASM) development is the capability of detecting and quantifying epileptic seizures occurring in electroencephalogram (EEG) signals, which is crucial for treatment efficacy evaluation. In this study, we introduced a seizure detection pipeline based on deep learning models applied to raw EEG signals. This pipeline integrates: a new pre-processing technique which segments continuous raw EEG signals without prior distinction between seizure and seizure-free activities; a post-processing algorithm developed to reassemble EEG segments and allow the identification of seizures start/end; and finally, a new evaluation procedure based on a strict seizure events comparison between predicted and real labels. Models training have been performed using a data splitting strategy which addresses the potential for data leakage. We demonstrated the fundamental differences between a seizure classification and a seizure detection task and showed the differences in performance between the two tasks. Finally, we demonstrated the generalization capabilities across species of our best architecture, combining a Convolutional Neural Network and a Transformer encoder. The model was trained on animal EEGs and tested on human EEGs with a F1-score of 93% on a balanced Bonn dataset.
Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy
Frankfurt Institute for Advanced Studies (FIAS)（法兰克福高等研究院）；Goethe University Frankfurt（法兰克福大学）；Center for Personalized Translational Epilepsy Research (CePTER)（个性化转化癫痫研究中心）
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
AITRICS；Massachusetts Institute of Technology（麻省理工学院）；The Cooper Union for the Advancement of Science and Art（库珀科学与艺术促进联盟）；National Health Insurance Service Ilsan Hospital（国民健康保险服务一山医院）；Severance Children’s Hospital, Yonsei University College of Medicine（延世大学医学院世福兰斯儿童医院）；KAIST（韩国科学技术院）
Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer
Institute of Neuroinformatics, University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）；Delft University of Technology（代尔夫特理工大学）；School of Electronic Science and Technology, Nanjing University（南京大学电子科学与技术学院）
EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers
São Carlos School of Engineering（圣卡洛斯工程学院）；University of São Paulo（圣保罗大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；Medical School of Ribeirão Preto（里贝朗普雷图医学院）
Principle components analysis for seizures prediction using wavelet transform
Shaheed Zulfikar Ali Bhutto Institute of Science and Technology（沙希德·祖尔菲卡尔·阿里·布托科学技术学院）；FAST National University of Computer and Emerging Sciences（FAST国家计算机与新兴科学大学）
Pre-Ictal Seizure Prediction Using Personalized Deep Learning
University of Maryland School of Medicine（马里兰大学医学院）；Thomas Jefferson High School for Science and Technology（托马斯杰斐逊科技高中）；The R Adam Cowley Shock Trauma Center, University of Maryland School of Medicine（马里兰大学医学院R·亚当·考利休克创伤中心）
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Monash University（莫纳什大学）；Alfred Health（阿尔弗雷德健康）；The University of Melbourne（墨尔本大学）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；St. Vincent’s Hospital, The University of Melbourne（墨尔本大学圣文森特医院）；Turner Institute for Brain and Mental Health, Monash University（莫纳什大学特纳脑与心理健康研究所）；Monash Biomedical Imaging, Monash University（莫纳什大学生物医学成像）；Wellcome Centre for Human Neuroimaging, UCL（伦敦大学学院人类神经成像威尔康姆中心）
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals
University of Strathclyde（思克莱德大学）；SRM Institute of Science and Technology（SRM科学技术学院）；Sheffield Hallam University（谢菲尔德哈勒姆大学）；Dublin City University（都柏林城市大学）；University of the West of England（西英格兰大学）
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；The University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Earable Inc.（Earable公司）
SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction
Institute for the Wireless Internet of Things, Northeastern University, Boston, MA, U.S.A.（无线互联网研究所，东北大学，波士顿，马萨诸塞州，美国）；University of Louisville, Louisville, KY, U.S.A.（路易斯维尔大学，路易斯维尔，肯塔基州，美国）
EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network
Graduate School of Advanced Science and Engineering, Hiroshima University（Hiroshima大学先进科学与工程研究生院）；Department of Pediatric Neurology, Okayama University Hospital（Okayama大学医院儿科神经科）
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy Detection
Computer Science Department, Federal University of Ouro Preto（欧鲁普雷图联邦大学计算机科学系）
Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
Frankfurt Institute for Advanced Studies (FIAS)（法兰克福高等研究院（FIAS））；Goethe University Frankfurt（法兰克福歌德大学）；Center for Personalized Translational Epilepsy Research (CePTER)（个性化转化癫痫研究中心（CePTER））；Oscar Langendorff Institute of Physiology, Rostock University Medical Center（罗斯托克大学医学中心奥斯卡·朗根多夫生理研究所）；Royal College of Surgeons Ireland（爱尔兰皇家外科医学院）
Random Forest classifier for EEG-based seizure prediction
Paris Brian Institute（巴黎大脑研究所）；CentraleSupélec（巴黎中央理工-高等电力学院）；National Centre for scientific research（法国国家科学研究中心）；Hôpital de La Pitié Salpêtrière（皮提耶-萨尔佩特里埃医院）
Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification
Huazhong University of Science and Technology（华中科技大学）；Dongfeng Corporation Research & Development Institute（东风公司研发院）；China Electronic System Technology Co., Ltd.（中国电子系统技术有限公司）；Southern University of Science and Technology（南方科技大学）
Interpretable Multimodal EEG-ECG Framework for Neonatal Seizure Prediction and Patient-Adaptive Transfer Learning
Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka（电子与电信工程系，乌尔塔瓦大学，斯里兰卡）；Department of Biomedical Engineering, University of Melbourne, Australia（生物医学工程系，墨尔本大学，澳大利亚）；Department of Electrical and Computer Engineering, University of British Columbia, Canada（电气与计算机工程系，不列颠哥伦比亚大学，加拿大）；Department of Pediatrics, Faculty of Medicine, University of Colombo, Sri Lanka（儿科系，医学院，科伦坡大学，斯里兰卡）
Long-Term EEG Partitioning for Seizure Onset Detection
SANKEN, Osaka University（大阪大学 SANKEN）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carle Illinois College of Medicine, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校卡尔伊利诺伊医学院）
