Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording

• URL: http://arxiv.org/abs/2011.08982v1
• Date: Sat, 14 Nov 2020 03:45:17 GMT
• Title: Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording
• Authors: Zaid Bin Tariq, Arun Iyengar, Lara Marcuse, Hui Su, B\"ulent Yener
• Abstract summary: We propose a Siamese neural network based seizure prediction method that takes a wavelet transformed EEG tensor as an input with convolutional neural network (CNN) as the base network for detecting change-points in EEG. Our method only needs one seizure for training which translates to less than ten minutes of preictal and interictal data while still getting comparable results to models which utilize multiple seizures for seizure prediction.
• Score: 16.395309518579914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 significant number of patient-specific seizures and periodic retraining of the model because of non-stationary EEG creates difficulties for designing practical device for a patient. To mitigate this process, we propose a Siamese neural network based seizure prediction method that takes a wavelet transformed EEG tensor as an input with convolutional neural network (CNN) as the base network for detecting change-points in EEG. Compared to the solutions in the literature, which utilize days of EEG recordings, our method only needs one seizure for training which translates to less than ten minutes of preictal and interictal data while still getting comparable results to models which utilize multiple seizures for seizure prediction.

Related papers

• From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals [0.8182812460605992]
  One-third of people suffering from mesial temporal lobe epilepsy exhibit drug resistance. Key part in anti-seizure medication development is the capability of detecting and quantifying epileptic seizures. In this study, we introduced a seizure detection pipeline based on deep learning models applied to raw EEG signals.
  arXiv  Detail & Related papers  (2024-10-04T12:52:37Z)
• SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using SincNet and Variational Autoencoder [0.0]
  This work proposes a semi-supervised approach for detecting epileptic seizures from EEG data, utilizing a novel Deep Learning-based method called SincVAE. Results indicate that SincVAE improves seizure detection in EEG data and is capable of identifying early seizures during the preictal stage as well as monitoring patients throughout the postictal stage.
  arXiv  Detail & Related papers  (2024-06-25T13:21:01Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Path Signatures for Seizure Forecasting [0.6282171844772422]
  We consider the automated discovery of predictive features (or biomarkers) to forecast seizures in a patient-specific way. We use the path signature, a recent development in the analysis of data streams, to map from measured time series to seizure prediction.
  arXiv  Detail & Related papers  (2023-08-18T05:19:18Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals [62.997667081978825]
  We develop a novel statistical point process model-called driven temporal point processes (DriPP) We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses.
  arXiv  Detail & Related papers  (2021-12-08T13:07:21Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• An End-to-End Deep Learning Approach for Epileptic Seizure Prediction [4.094649684498489]
  We propose an end-to-end deep learning solution using a convolutional neural network (CNN) Overall sensitivity, false prediction rate, and area under receiver operating characteristic curve reaches 93.5%, 0.063/h, 0.981 and 98.8%, 0.074/h, 0.988 on two datasets respectively.
  arXiv  Detail & Related papers  (2021-08-17T05:49:43Z)
• Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures [58.720142291102135]
  Detailed analysis of seizure semiology is critical for management of epilepsy patients. We present GESTURES, a novel architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We show that an STCNN trained on a HAR dataset can be used in combination with an RNN to accurately represent arbitrarily long videos of seizures.
  arXiv  Detail & Related papers  (2021-06-22T18:40:31Z)
• Patient-independent Epileptic Seizure Prediction using Deep Learning Models [39.19336481493405]
  The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects.
  arXiv  Detail & Related papers  (2020-11-18T23:13:48Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.