Seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature. Joint spectral-temporal features are believed to contain sufficient and powerful feature information for absence seizure detection. However, the resulting high-dimensional features involve redundant information and require heavy computational load. Here, we discover significant low-dimensional spectral-temporal features in terms of mean-standard deviation of wavelet transform coefficient (MS-WTC), based on which a novel absence seizure detection framework is developed. The EEG signals are transformed into the spectral-temporal domain, with their low-dimensional features fed into a convolutional neural network. Superior detection performance is achieved on the widely-used benchmark dataset as well as a clinical dataset from the Chinese 301 Hospital. For the former, seven classification tasks were evaluated with the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a mean accuracy of 94.7%, overwhelming other methods with low-dimensional temporal and spectral features. Experimental results on two seizure datasets demonstrate reliability, efficiency and stability of our proposed MS-WTC method, validating the significance of the extracted low-dimensional spectral-temporal features.
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）
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）
Spatio-Temporal Attention Network for Epileptic Seizure Prediction
Department of Computer Science, Rensselaer Polytechnic Institute（计算机科学系，拉瑟拉尔理工学院）；IBM T.J. Watson Research Center（IBM 沃森研究中心）；Department of Neurology, Icahn School of Medicine at Mount Sinai（神经病学系，辛格恩医学院）
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）
Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior
Anhui University（安徽大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Sanbo Brain Hospital Capital Medical University（首都医科大学三博脑科医院）；Peking University（北京大学）
Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor
State Key Laboratory of Digital Medical Engineering（数字医学工程国家重点实验室）；Key Laboratory of Biomedical Engineering of Hainan Province（海南省生物医学工程重点实验室）；School of Biomedical Engineering Hainan University（海南大学生物医学工程学院）；SynSense Co.Ltd.（SynSense有限公司）；SynSense AG（SynSense股份公司）
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
Carnegie Mellon University（卡内基梅隆大学）；Kyung Hee University（Kyung Hee大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；University of Pittsburgh（匹兹堡大学）
