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）
Ramya Sundaram, Muhammad Aslam Jarwar, M Uma, Sunder Ali Khowaja, Qurat-Ul-Ain Mastoi
癫痫发作的预测与检测是癫痫临床治疗的关键任务。脑电图（EEG）与颅内脑电图（iEEG）因具备高时间分辨率捕捉可区分脑活动的能力，被广泛用于癫痫分析。然而现有研究通常仅单独分析EEG或iEEG的癫痫发作活动：EEG分析已得到充分研究，而越来越多研究关注iEEG，因其在空间层面具有更高特异性与信号质量。当前多数现有癫痫发作预测系统本质为单模态，限制了其在多种临床场景的应用，因此需开发可同时分析EEG与iEEG信号的通用框架。本文提出一种多模态癫痫发作分类新框架，整合来自不同受试者与来源的异构EEG与iEEG记录；开发了名为统一多模态信号协调（Unified Multimodal Signal Harmonization，UMSH）的特定框架，用于协调不同采样率与记录条件的信号。此外，为处理数据集内极端类别不平衡问题，采用基于条件生成对抗网络（Conditional Generative Adversarial Network，cGAN）的数据增强技术生成合成样本以平衡类别分布。该框架采用EEGNet-视觉Transformer（EEGNet-ViT）作为特征提取与分类网络，学习多模态癫痫数据中的局部可区分模式与全局上下文信息。实验结果证明了所提框架用于鲁棒癫痫发作分类的有效性，凸显了EEG-iEEG多模态整合学习在推进下一代癫痫监测与临床决策支持系统方面的潜力。
英文摘要：
Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG are widely used to analyze epilepsy due to its capability of capturing discriminative brain activities at high time resolution. However, existing works generally analyze either EEG or iEEG seizure activity separately, where EEG analysis has been well-researched, while more and more works consider iEEG for its higher specificity at the spatial level and signal quality. Currently, most of the existing seizure prediction systems are single-modal in nature, which limits its application to a variety of clinical conditions. Therefore, a common framework to analyze seizures with both EEG and iEEG signals needs to be developed. This paper presents a new framework of multimodal seizure classification integrating the heterogeneous EEG and iEEG recordings from different subjects and sources. A specific framework, called Unified Multimodal Signal Harmonization (UMSH), is developed for harmonizing signals with varied sampling rates and recording conditions. Moreover, to handle the extremely class-imbalanced problem in the data sets, we use a Conditional Generative Adversarial Network (cGAN) based data augmentation technique to generate synthetic samples to balance the class distribution. The proposed framework uses the EEGNet-Vision Transformer (EEGNet-ViT) as the feature extraction and classification network to learn the local discriminative patterns as well as global contextual information in the multimodal seizure data. Experimental results demonstrate the effectiveness of the proposed framework for robust seizure classification, emphasizing the potential of integrated EEG-iEEG multimodal learning in advancing next-generation epilepsy monitoring and clinical decision-support systems.
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…
Epilepsy is a chronic neurological disorder affecting 1\% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for accurate epileptic seizure (ES) prediction, thereby benefiting patients suffering from epilepsy. To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms. However, the scarcity of seizure onsets leads to significant insufficiency of data for training the DL algorithms. To overcome this data insufficiency, in this paper, we propose…
The University of Sydney（悉尼大学）；Swinburne University of Technology（斯威本科技大学）；The University of Melbourne（墨尔本大学）；University of Queensland（昆士兰大学）
In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which is more accessible. We also suggest the use of data fusion to further improve the seizure prediction accuracy. Data fusion in our vision includes EEG signals, cardiogram signals, body temperature and time. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of seizure onset is disregarded. The trained Discriminator of the GAN is then used as feat…
Monash University（莫纳什大学）；Information Technology University (ITU)-Punjab（旁遮普信息技术大学）
Prediction of seizure before they occur is vital for bringing normalcy to the lives of patients. Researchers employed machine learning methods using hand-crafted features for seizure prediction. However, ML methods are too complicated to select the best ML model or best features. Deep Learning methods are beneficial in the sense of automatic feature extraction. One of the roadblocks for accurate seizure prediction is scarcity of epileptic seizure data. This paper addresses this problem by proposing a deep convolutional generative adversarial network to generate synthetic EEG samples. We use tw…
Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-threatening. Modern systems monitoring electroencephalography (EEG) signals are being currently developed with the view to detect epileptic seizures in order to alert caregivers and reduce the impact of seizures on patients' quality of life. Such seizure detection systems employ state-of-the-art machine learning algorithms that require a conside…
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…
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain unaddressed in prior automated seizure detection and classification studies: (1) representing non-Euclidean data structure in EEGs, (2) accurately classifying rare seizure types, and (3) lacking a quantitative interpretability approach to measure model ability to localize seizures. In this study, we address these challenges by (1) representing the spatiotemporal dependencies in EEGs using a graph neural network (GNN) a…
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
University College Cork（科克大学）；University of Parma（帕尔马大学）
EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG d…
Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
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）
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）
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）
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on-device applications. Therefore in this work, for the first time, we extensively compare mul…
Department of Electrical and Computer Engineering, McGill University（麦吉尔大学电气与计算机工程系）；Mila - Quebec AI Institute（Mila-魁北克人工智能研究所）
Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-super…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Mount Sinai Hospital（西奈山医院）
Electroencephalogram (EEG) is a prominent way to measure the brain activity for studying epilepsy, thereby helping in predicting seizures. Seizure prediction is an active research area with many deep learning based approaches dominating the recent literature for solving this problem. But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier. The increase in sensitivity and specificity for seizure prediction using the machine learning models is noteworthy. However, the need for a signi…
The University of Western Australia（西澳大学）；University of Jeddah（吉达大学）
Seizure type identification is essential for the treatment and management of epileptic patients. However, it is a difficult process known to be time consuming and labor intensive. Automated diagnosis systems, with the advancement of machine learning algorithms, have the potential to accelerate the classification process, alert patients, and support physicians in making quick and accurate decisions. In this paper, we present a novel multi-path seizure-type classification deep learning network (MP-SeizNet), consisting of a convolutional neural network (CNN) and a bidirectional long short-term me…
University College Cork（科克大学）；INFANT Research Centre（INFANT研究中心）
A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representation employed in traditional machine learning based solutions. The seizure detection system utilises only convolutional layers in order to process the multichannel time domain signal and is designed to exploit the large amount of weakly labelled data in the training stage. The system performance is assessed on a large database of continuous EEG reco…
IBM Research Australia（IBM澳大利亚研究院）；Google Health（谷歌健康）
Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of drugs but also the range of activities a patient can safely engage in. With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification. We used the recently released TUH EEG seizure cor…
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）
Bharati Vidyapeeth’s College of Engineering（巴拉蒂维迪亚佩特工程学院）
The electroencephalogram (EEG) is one of the most precious technologies to understand the happenings inside our brain and further understand our body's happenings. Automatic prediction of oncoming seizures using the EEG signals helps the doctors and clinical experts and reduces their workload. This paper proposes an end-to-end deep learning algorithm to fully automate seizure prediction's laborious task without any heavy pre-processing on the EEG data or feature engineering. The proposed deep learning network is a blend of signal processing and deep learning pipeline, which automates the seizu…
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…
Centre for Informatics and Systems of the University of Coimbra（科英布拉大学信息与系统中心）；University of Coimbra（科英布拉大学）
Refractory epileptic patients can suffer a seizure at any moment. Seizure prediction would substantially improve their lives. In this work, based on scalp EEG and its transformation into images, the likelihood of an epileptic seizure occurring at any moment is computed using an average of the softmax layer output (the likelihood) of a CNN, instead of the output of the classification layer. Results show that by analyzing the likelihood and thresholding it, prediction has higher sensitivity or a lower FPR/h. The best threshold for the likelihood was higher than 50% for 5 patients, and was lower …
Hiroshima University（广岛大学）；Okayama University Hospital（冈山大学医院）
In this paper, we propose a time-series stochastic model based on a scale mixture distribution with Markov transitions to detect epileptic seizures in electroencephalography (EEG). In the proposed model, an EEG signal at each time point is assumed to be a random variable following a Gaussian distribution. The covariance matrix of the Gaussian distribution is weighted with a latent scale parameter, which is also a random variable, resulting in the stochastic fluctuations of covariances. By introducing a latent state variable with a Markov chain in the background of this stochastic relationship,…
Northeast Yucai Foreign Language School（东北育才外国语学校）；Shenyang Pharmaceutical University（沈阳药科大学）；Northwestern University（西北大学）
The prediction of epileptic seizure has always been extremely challenging in medical domain. However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting. Applying machine learning model onto the predication of epileptic seizure could help us obtain a better result and there have been plenty of scientists who have been doing such works so that there are sufficient medical data provided for researchers to do training of machine learning models.
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）
Università di Siena（锡耶纳大学）；Università Sapienza di Roma（罗马 Sapienza 大学）
Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 0.5--0.8\% of the world population. Several studies investigated the relationship between seizures and brainwave synchronization patterns, pursuing the possibility of identifying interictal, preictal, ictal and postictal states. In this work, we introduce a graph-based model of the brain interactions developed to study synchronization patterns in the electroencephalogram (EEG) signals. The aim is to develop a patient-specific approach, also for a real-time use, for the prediction…
Epilepsy which is characterized by seizures is studied using EEG signals by recording the electrical activity of the brain. Different types of communication between different parts of the brain are characterized by many state of the art connectivity measures which can be directed and undirected. We propose to employ a set of undirected (spectral matrix, the inverse of the spectral matrix, coherence, partial coherence, and phaselocking value) and directed features (directed coherence, the partial directed coherence) to learn a deep neural network that detects whether a particular data window be…
Electroencephalography (EEG) analysis extracts critical information from brain signals, which has provided fundamental support for various applications, including brain-disease diagnosis and brain-computer interface. However, the real-time processing of large-scale EEG signals at high energy efficiency has placed great challenges for electronic processors on edge computing devices. Here, we propose the EEG opto-processor based on diffractive photonic computing units (DPUs) to effectively process the extracranial and intracranial EEG signals and perform epileptic seizure detection. The signals …
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）
Epileptic seizure prediction has gained considerable interest in the computational Epilepsy research community. This paper presents a Machine Learning based method for epileptic seizure prediction which outperforms state-of-the art methods. We compute a probability for a given epoch, of being pre-ictal against interictal using the Random Forest classifier and introduce new concepts to enhance the robustness of the algorithm to false alarms. We assessed our method on 20 patients of the benchmark scalp EEG CHB-MIT dataset for a seizure prediction horizon (SPH) of 5 minutes and a seizure occurren…
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）
Epilepsy is one of the most common and yet diverse set of chronic neurological disorders. This excessive or synchronous neuronal activity is termed seizure. Electroencephalogram signal processing plays a significant role in detection and prediction of epileptic seizures. In this paper we introduce an approach that relies upon the properties of wavelets for seizure detection. We utilise the Maximum Overlap Discrete Wavelet Transform which enables us to reduce signal noise Then from the variance exhibited in wavelet coefficients we develop connectivity and communication efficiency between the el…
We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG). The input to the neural network is a 126 feature vector containing 9 features for each of the 14 EEG channels obtained over 1-second, non-overlapping windows. The models in our experiments achieved high sensitivity and specificity on patient records not used in the training process. This is demonstrated using leave-one-out-cross-validation across patient records, where we hold out one patient's record as the test set and use all other patients' records for …
MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordings
Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology（东京农业科技大学电子与信息工程系）；Medical Science Data-driven Mathematics Team, RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences（RIKEN跨学科理论与数学科学中心医学科学数据驱动数学团队）；Medical Data Mathematical Reasoning Special Team, RIKEN Center for Integrative Medical Sciences（RIKEN整合医学科学中心医学数据数学推理特别团队）；Department of Artificial Intelligence Medicine, Chiba University（千叶大学人工智能医学系）；Department of Neurosurgery, Juntendo University School of Medicine（武藏野大学医学部神经外科）
Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification
Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院、图像信息处理与智能控制教育部重点实验室）
University of Coimbra（科英布拉大学）；CISUC（科英布拉大学信息与系统中心）
The time series captured by a single scalp electrode (plus the reference electrode) of refractory epileptic patients is used to forecast seizures susceptibility. The time series is preprocessed, segmented, and each segment transformed into an image, using three different known methods: Recurrence Plot, Gramian Angular Field, Markov Transition Field. The likelihood of the occurrence of a seizure in a future predefined time window is computed by averaging the output of the softmax layer of a CNN, differently from the usual consideration of the output of the classification layer. By thresholding …
Epilepsy is one of the most common brain diseases that affect more than 1\% of the world's population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and highly-specialized. Moreover, EEG manual evaluation is a process known to have a low inter-ra…
