Papers Seizure Detection
“Seizure Detection” 태그가 달린 논문 220편 · 필터 해제
Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach
Most existing seizure detection algorithms require extensive pre-processing of the data and rely on heuristic or currently unexplainable machine learning approaches. These approaches often struggle with balancing detecti…
Seizure DetectionMultimodal Pretraining for Generalizable EEG Representation Learning
Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks. This limited approach can make it challenging to apply these models across different datasets or in various situatio…
Self-Supervised LearningRepresentation LearningSeizure DetectionFrom Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks
Electroencephalogram (EEG) analysis remains the clinical gold standard for epilepsy diagnosis and seizure detection. While Deep Learning (DL) has significantly advanced automated EEG interpretation, its transition from c…
Seizure DetectionA Neurosymbolic Framework for Interpretable Skeleton-Based Seizure Detection via Concept-Driven Logical Reasoning
Video-based seizure detection is essential for the management of epilepsy patients, offering a non-invasive complement to electroencephalography. While several deep learning approaches have been developed for video-based…
Logical ReasoningSeizure DetectionLLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or l…
Representation LearningSeizure DetectionGraph LearningA Multimodal Pre-trained Network for Integrated EEG-Video Seizure Detection
Reliable seizure detection in mouse models is essential for preclinical epilepsy research, yet manual review of synchronized video-EEG recordings is labor-intensive and single-modality systems fail for complementary reas…
Representation LearningSeizure DetectionClassification of Epileptic iEEG using Topological Machine Learning
Epileptic seizure detection from EEG signals remains challenging due to the high dimensionality and nonlinear, potentially stochastic, dynamics of neural activity. In this work, we investigate whether features derived fr…
Dimensionality ReductionSeizure DetectionOptimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach
Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, recent methods construct dynamic graphs via statistical correlations, …
Self-Supervised LearningRepresentation LearningSeizure DetectionEpileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events. Electroencephalogram (EEG) signals are widely used for seizur…
Seizure DetectionLearning Cross-Joint Attention for Generalizable Video-Based Seizure Detection
Automated seizure detection from long-term clinical videos can substantially reduce manual review time and enable real-time monitoring. However, existing video-based methods often struggle to generalize to unseen subject…
Seizure DetectionExplainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring
The use of wearables in medicine and wellness, enabled by AI-based models, offers tremendous potential for real-time monitoring and interpretable event detection. Explainable AI (XAI) is required to assess what models ha…
Seizure DetectionForecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning
Epileptic seizure forecasting is a clinically important yet challenging problem in epilepsy research. Existing approaches predominantly rely on neural signals such as electroencephalography (EEG), which require specializ…
Transfer LearningSeizure DetectionSynthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
Deep learning has achieved transformative performance across diverse domains, largely driven by large-scale and high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentall…
Synthetic Data GenerationSeizure DetectionBridging the Compression-Precision Paradox: A Hybrid Architecture for Clinical EEG Report Generation with Guaranteed Measurement Accuracy
Automated EEG monitoring requires clinician-level precision for seizure detection and reporting. Clinical EEG recordings exceed LLM context windows, requiring extreme compression (400:1+ ratios) that destroys fine-graine…
parameter-efficient fine-tuningSeizure DetectionText GenerationNeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Image
Accurate and timely seizure detection from Electroencephalography (EEG) is critical for clinical intervention, yet manual review of long-term recordings is labor-intensive. Recent efforts to encode EEG signals into large…
Seizure DetectionGeometry- and Relation-Aware Diffusion for EEG Super-Resolution
Recent electroencephalography (EEG) spatial super-resolution (SR) methods, while showing improved quality by either directly predicting missing signals from visible channels or adapting latent diffusion-based generative …
Emotion RecognitionSeizure DetectionThe Powers of Precision: Structure-Informed Detection in Complex Systems -- From Customer Churn to Seizure Onset
Emergent phenomena -- onset of epileptic seizures, sudden customer churn, or pandemic outbreaks -- often arise from hidden causal interactions in complex systems. We propose a machine learning method for their early dete…
Seizure DetectionRAICL: Retrieval-Augmented In-Context Learning for Vision-Language-Model Based EEG Seizure Detection
Electroencephalogram (EEG) decoding is a critical component of medical diagnostics, rehabilitation engineering, and brain-computer interfaces. However, contemporary decoding methodologies remain heavily dependent on task…
Seizure DetectionBrain DecodingRL-BioAug: Label-Efficient Reinforcement Learning for Self-Supervised EEG Representation Learning
The quality of data augmentation serves as a critical determinant for the performance of contrastive learning in EEG tasks. Although this paradigm is promising for utilizing unlabeled data, static or random augmentation …
Representation LearningReinforcement LearningContrastive LearningSeizure DetectionConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection
Epilepsy is a chronic neurological disorder marked by recurrent seizures that can severely impact quality of life. Electroencephalography (EEG) remains the primary tool for monitoring neural activity and detecting seizur…
Seizure Detection