2,642 research outputs found
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Automatic classification of epileptic seizure types in electroencephalograms
(EEGs) data can enable more precise diagnosis and efficient management of the
disease. This task is challenging due to factors such as low signal-to-noise
ratios, signal artefacts, high variance in seizure semiology among epileptic
patients, and limited availability of clinical data. To overcome these
challenges, in this paper, we present SeizureNet, a deep learning framework
which learns multi-spectral feature embeddings using an ensemble architecture
for cross-patient seizure type classification. We used the recently released
TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of
SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of
up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross
validation for scalp EEG based multi-class seizure type classification. We also
show that the high-level feature embeddings learnt by SeizureNet considerably
improve the accuracy of smaller networks through knowledge distillation for
applications with low-memory constraints
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Epilepsy is a neurological disorder and for its detection, encephalography
(EEG) is a commonly used clinical approach. Manual inspection of EEG brain
signals is a time-consuming and laborious process, which puts heavy burden on
neurologists and affects their performance. Several automatic techniques have
been proposed using traditional approaches to assist neurologists in detecting
binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal.
These methods do not perform well when classifying ternary case e.g. ictal vs.
normal vs. inter-ictal; the maximum accuracy for this case by the
state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a
system based on deep learning, which is an ensemble of pyramidal
one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model,
the bottleneck is the large number of learnable parameters. P-1D-CNN works on
the concept of refinement approach and it results in 60% fewer parameters
compared to traditional CNN models. Further to overcome the limitations of
small amount of data, we proposed augmentation schemes for learning P-1D-CNN
model. In almost all the cases concerning epilepsy detection, the proposed
system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page
A novel framework using deep auto-encoders based linear model for data classification
This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes. Keywords: deep sparse auto-encoders, medical diagnosis, linear model, data classification, PSO algorithmpublishedVersio
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
Predicting epileptic seizures with a stacked long short-term memory network
Despite advancements, seizure detection algorithms are trained using only the data recorded frompast epileptic seizures. This one-dimensional approach has led to an excessive false detection rate,where common movements are incorrectly classified. Therefore, a new method of detection isrequired that can distinguish between the movements observed during a generalized tonic-clonic(GTC) seizure and common everyday activities. For this study, eight healthy participants and twodiagnosed with epilepsy simulated a series of activities that share a similar set of spatialcoordinates with an epileptic seizure. We then trained a stacked, long short-term memory (LSTM)network to classify the different activities. Results show that our network successfullydifferentiated the types of movement, with an accuracy score of 94.45%. These findings present amore sophisticated method of detection that correlates a wearers movement against 12 seizurerelated activities prior to formulating a prediction
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