Spatio-Temporal Attention Network for Epileptic Seizure Prediction

• URL: http://arxiv.org/abs/2511.02846v1
• Date: Fri, 24 Oct 2025 01:45:25 GMT
• Title: Spatio-Temporal Attention Network for Epileptic Seizure Prediction
• Authors: Zan Li, Kyongmin Yeo, Wesley Gifford, Lara Marcuse, Madeline Fields, Bülent Yener,
• Abstract summary: We present a deep learning framework that learns complex-temporal correlation structures of EEG signals through a S-Temporal Attention Network (STAN) for accurate predictions of onset seizures for Epilepsy patients.<n>The framework reliably detects preictal states at least 15 minutes before an onset with patient-specific windows to 45 minutes, providing sufficient intervention time for clinical applications.
• Score: 4.750750705838807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we present a deep learning framework that learns complex spatio-temporal correlation structures of EEG signals through a Spatio-Temporal Attention Network (STAN) for accurate predictions of onset of seizures for Epilepsy patients. Unlike existing methods, which rely on feature engineering and/or assume fixed preictal durations, our approach simultaneously models spatio-temporal correlations through STAN and employs an adversarial discriminator to distinguish preictal from interictal attention patterns, enabling patient-specific learning. Evaluation on CHB-MIT and MSSM datasets demonstrates 96.6\% sensitivity with 0.011/h false detection rate on CHB-MIT, and 94.2% sensitivity with 0.063/h FDR on MSSM, significantly outperforming state-of-the-art methods. The framework reliably detects preictal states at least 15 minutes before an onset, with patient-specific windows extending to 45 minutes, providing sufficient intervention time for clinical applications.

Related papers

• Neural Architecture for Fast and Reliable Coagulation Assessment in Clinical Settings: Leveraging Thromboelastography [11.141462411413059]
  Real-time coagulation monitoring can enable early detection and prompt remediation of risks.<n>Traditional Thromboelastography (TEG) can only provide such outputs after nearly 1 hour of measurement.<n>We present Physiological State Reconstruc-tion (PSR), a new algorithm specifically designed to take ad-vantage of dynamic changes between individuals.
  arXiv  Detail & Related papers  (2026-01-12T15:03:53Z)
• Adversarial Spatio-Temporal Attention Networks for Epileptic Seizure Forecasting [4.750750705838807]
  We present an Adversarial Attention Network that models brain connectivity and temporal neural dynamics cascaded attention blocks with alternating spatial temporal modules.<n>A continuous 90-minute pre-seizure monitoring reveals the learned EEG attention patterns that enable early detection.
  arXiv  Detail & Related papers  (2025-11-03T06:48:54Z)
• From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification [8.113866195465976]
  We propose a prototype-driven framework for generating sparse counterfactual explanations tailored to 12-lead ECG classification models.<n>Our method employs SHAP-based thresholds to identify critical signal segments and convert them into interval rules.<n>We evaluate three variants of our approach, Original, Sparse, and Aligned Sparse, with class-specific performance ranging from 98.9% validity for MI to challenges with hypertrophy (HYP) detection.
  arXiv  Detail & Related papers  (2025-10-22T12:09:50Z)
• Detection of high-frequency oscillations using time-frequency analysis [0.0]
  High-frequency oscillations (HFOs) are a new biomarker for identifying the epileptogenic zone.<n>Visual identification of HFOs is time-consuming, labor-intensive, and subjective.<n>We developed a novel method for detecting HFOs in the ripple and fast ripple frequency bands (80-500 Hz)
  arXiv  Detail & Related papers  (2025-10-08T20:50:02Z)
• EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network [1.9662978733004604]
  We propose a detection framework combining domain adversarial training with a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM)<n> evaluation using EEG recordings from 20 patients with focal epilepsy demonstrated superior performance over non-adversarial methods.<n>The integration of adversarial training with temporal modeling enables robust cross-patient seizure detection.
  arXiv  Detail & Related papers  (2025-05-21T07:27:55Z)
• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [46.56667527672019]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data.<n>Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction [0.0]
  We develop a competitive deep learning model for seizure prediction using scalp electroencephalogram (EEG) signals. We trained and evaluated our model on 19 pediatric patients of the open-access CHB-MIT dataset in a subject-specific manner. Using the OPP of each patient, preictal and interictal segments were correctly identified with an average sensitivity of 99.31%, specificity of 95.34%, AUC of 99.35%, and F1- score of 97.46%.
  arXiv  Detail & Related papers  (2024-07-20T13:49:14Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.