Epilepsy is one of the most common neurological disorders that greatly impair patient' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. Nowadays, there are many systems helping the neurologists to quickly find interesting segments of the lengthy signal by automatic seizure detection. However, we notice that it is very difficult, if not impossible, to obtain long-term EEG data with seizure activities for epilepsy patients in areas lack of medical resources and trained neurologists. Therefore, we propose to study automated epileptic diagnosis using interictal EEG data that is much easier to collect than ictal data. The authors are not aware of any report on automated EEG diagnostic system that can accurately distinguish patients' interictal EEG from the EEG of normal people. The research presented in this paper, therefore, aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. Such a system should also detect seizure activities for further investigation by doctors and potential patient monitoring. To develop such a system, we extract four classes of features from the EEG data and build a Probabilistic Neural Network (PNN) fed with these features. Leave-one-out cross-validation (LOO-CV) on a widely used epileptic-normal data set reflects an impressive 99.5% accuracy of our system on distinguishing normal people's EEG from patient's interictal EEG. We also find our system can be used in patient monitoring (seizure detection) and seizure focus localization, with 96.7% and 77.5% accuracy respectively on the data set.
Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
Communication Engineering Department National Engineering School of Tunis Tunis El Manar University；REGIM-Lab, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Tunisia National Engineering School of Tunis Tunis El Manar University
DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation
Aliyun School of Big Data, Changzhou University（阿里云大数据学院，长洲大学）；Department of Computing, Xi’an JiaoTong-Liverpool University（计算系，西安交通大学-利物浦大学）；Department of Computer Science, University of Liverpool（计算机科学系，利物浦大学）；Center for Artificial Intelligence in Agriculture, Fujian Agriculture and Forestry University（农业人工智能中心，福建农林大学）
Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraints
Pro2Future GmbH（Pro2Future公司）；Institute for Machine Learning（机器学习研究所）；Johannes Kepler University（约瑟夫·约瑟夫·凯普勒大学）；Institute of Pervasive Computing（普及计算研究所）
Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction
Maastricht University Medical Centre+（马斯特里赫特大学医学中心+）；GROW Research Institute for Oncology and Reproduction（肿瘤与生殖研究GROW研究所）；University of Edinburgh（爱丁堡大学）；Muir Maxwell Epilepsy Centre（缪尔·马克斯韦尔癫痫中心）；Centre for Medical Informatics（医学信息学中心）；Usher Institute（乌舍研究所）；Medical School（医学院）；Institute for Imaging, Data and Communications（影像、数据与通信研究所）；School of Engineering（工程学院）
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）
Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy. Given the unexpected nature of an epileptic seizure, its prediction would improve patient care, optimizing the quality of life and the treatment of epilepsy. Predicting an epileptic seizure implies the identification of two distinct states of EEG in a patient with epilepsy: the preictal and the interictal. In this paper, we developed two deep lear…
EPFL（洛桑联邦理工学院）；University of Applied Sciences Western Switzerland (HEIG-VD/HES-SO)（瑞士西部应用科学大学（日内瓦湖高等学院/瑞士西部应用科学与艺术大学））；Centre Hospitalier Universitaire Vaudois (CHUV)（沃州大学中心医院）；University of Lausanne（洛桑大学）；University of Lyon（里昂大学）；Lyon’s Neurosciences Research Center (INSERM U1028/CNRS UMR 5292)（里昂神经科学研究中心（法国国家健康与医学研究院U1028/法国国家科学研究中心UMR 5292））
While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have tackled this issue by developing interpretable DL models in the context of online detection of epileptic seizure, based on EEG signal. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused…
Texas Tech University（德州理工大学）；Jiangsu Provincial Hospital of Chinese Medicine（江苏省中医院）；Texas Tech University Health Sciences Center（德州理工大学健康科学中心）
Approximately over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much…
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis
Rashtriya Raksha University（印度国家防卫大学）；National Institute of Technology, Durgapur（杜尔加普尔国家理工学院）；Guru Nanak Institute of Technology（古鲁那纳克技术学院）；University of Malta（马耳他大学）
School of Life Science, Beijing Institute of Technology（北京理工大学生命学院）
As the second most common neurodegenerative disease, Parkinson's disease has caused serious problems worldwide. However, the cause and mechanism of PD are not clear, and no systematic early diagnosis and treatment of PD have been established. Many patients with PD have not been diagnosed or misdiagnosed. In this paper, we proposed an EEG-based approach to diagnosing Parkinson's disease. It mapped the frequency band energy of electroencephalogram(EEG) signals to 2-dimensional images using the interpolation method and identified classification using capsule network(CapsNet) and achieved 89.34% c…
Several high specificity and sensitivity seizure prediction methods with convolutional neural networks (CNNs) are reported. However, CNNs are computationally expensive and power hungry. These inconveniences make CNN-based methods hard to be implemented on wearable devices. Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed in this work. This approach uses a designed gaussian random discrete encoder to generate spike sequences from the EEG samples and make predictions in a spiking convolutional neural network (S…
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）
BioSensics LLC（BioSensics有限责任公司）；Brown University（布朗大学）；University of Southern California（南加州大学）
Epilepsy is one of the most common neurological disorders, typically observed via seizure episodes. Epileptic seizures are commonly monitored through electroencephalogram (EEG) recordings due to their routine and low expense collection. The stochastic nature of EEG makes seizure identification via manual inspections performed by highly-trained experts a tedious endeavor, motivating the use of automated identification. The literature on automated identification focuses mostly on supervised learning methods requiring expert labels of EEG segments that contain seizures, which are difficult to obt…
