677 research outputs found
Performance Analysis of Deep-Learning and Explainable AI Techniques for Detecting and Predicting Epileptic Seizures
Epilepsy is one of the most common neurological diseases globally. Notably, people in low to middle-income nations could not get proper epilepsy treatment due to the cost and availability of medical infrastructure. The risk of sudden unpredicted death in Epilepsy is considerably high. Medical statistics reveal that people with Epilepsy die more prematurely than those without the disease. Early and accurately diagnosing diseases in the medical field is challenging due to the complex disease patterns and the need for time-sensitive medical responses to the patients. Even though numerous machine learning and advanced deep learning techniques have been employed for the seizure stages classification and prediction, understanding the causes behind the decision is difficult, termed a black box problem. Hence, doctors and patients are confronted with the black box decision-making to initiate the appropriate treatment and understand the disease patterns respectively. Owing to the scarcity of epileptic Electroencephalography (EEG) data, training the deep learning model with diversified epilepsy knowledge is still critical. Explainable Artificial intelligence has become a potential solution to provide the explanation and result interpretation of the learning models. By applying the explainable AI, there is a higher possibility of examining the features that influence the decision-making that either the patient recorded from epileptic or non-epileptic EEG signals. This paper reviews the various deep learning and Explainable AI techniques used for detecting and predicting epileptic seizures using EEG data. It provides a comparative analysis of the different techniques based on their performance
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models
Multivariate signals are prevalent in various domains, such as healthcare,
transportation systems, and space sciences. Modeling spatiotemporal
dependencies in multivariate signals is challenging due to (1) long-range
temporal dependencies and (2) complex spatial correlations between sensors. To
address these challenges, we propose representing multivariate signals as
graphs and introduce GraphS4mer, a general graph neural network (GNN)
architecture that captures both spatial and temporal dependencies in
multivariate signals. Specifically, (1) we leverage Structured State Spaces
model (S4), a state-of-the-art sequence model, to capture long-term temporal
dependencies and (2) we propose a graph structure learning layer in GraphS4mer
to learn dynamically evolving graph structures in the data. We evaluate our
proposed model on three distinct tasks and show that GraphS4mer consistently
improves over existing models, including (1) seizure detection from
electroencephalography signals, outperforming a previous GNN with
self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from
polysomnography signals, a 4.1 points improvement in macro-F1 score compared to
existing sleep staging models; and (3) traffic forecasting, reducing MAE by
8.8% compared to existing GNNs and by 1.4% compared to Transformer-based
models
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Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference. On the CHB-MIT dataset, we demonstrate that the proposed method can generalize well in a 6 fold leave-4-patient-out evaluation. Moreover, it is shown that our algorithm can achieve as much as 5% absolute improvement in performance compared to previous data-driven methods. This is achieved while the computational complexity of the proposed technique is a fraction of the complexity of prior work, making it suitable for real-time seizure detection
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion,
abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods
involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these,
neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate
the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS)
based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of
DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased
CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also,
descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic
seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented,
which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation
of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison
has been carried out between research on epileptic seizure detection and prediction. The challenges in
epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In
addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL,
rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which
summarizes the significant findings of the paper
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection
Wearable devices for seizure monitoring detection could significantly improve
the quality of life of epileptic patients. However, existing solutions that
mostly rely on full electrode set of electroencephalogram (EEG) measurements
could be inconvenient for every day use. In this paper, we propose a novel
knowledge distillation approach to transfer the knowledge from a sophisticated
seizure detector (called the teacher) trained on data from the full set of
electrodes to learn new detectors (called the student). They are both providing
lightweight implementations and significantly reducing the number of electrodes
needed for recording the EEG. We consider the case where the teacher and the
student seizure detectors are graph neural networks (GNN), since these
architectures actively use the connectivity information. We consider two cases
(a) when a single student is learnt for all the patients using preselected
channels; and (b) when personalized students are learnt for every individual
patient, with personalized channel selection using a Gumbelsoftmax approach.
Our experiments on the publicly available Temple University Hospital EEG
Seizure Data Corpus (TUSZ) show that both knowledge-distillation and
personalization play significant roles in improving performance of seizure
detection, particularly for patients with scarce EEG data. We observe that
using as few as two channels, we are able to obtain competitive seizure
detection performance. This, in turn, shows the potential of our approach in
more realistic scenario of wearable devices for personalized monitoring of
seizures, even with few recordings
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