Neural Memory Networks for Seizure Type Classification

12/10/2019
by   David Ahmedt-Aristizabal, et al.
0

Classification of seizure type is a key step in the clinical process for evaluating an individual who presents with seizures. It determines the course of clinical diagnosis and treatment, and its impact stretches beyond the clinical domain to epilepsy research and the development of novel therapies. Automated identification of seizure type may facilitate understanding of the disease, and seizure detection and prediction has been the focus of recent research that has sought to exploit the benefits of machine learning and deep learning architectures. Nevertheless, there is not yet a definitive solution for automating the classification of seizure type, a task that must currently be performed by an expert epileptologist. Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data. We first explore the performance of traditional deep learning techniques which use convolutional and recurrent neural networks, and enhance these architectures by using external memory modules with trainable neural plasticity. We show that our model achieves a state-of-the-art weighted F1 score of 0.945 for seizure type classification on the TUH EEG Seizure Corpus with the IBM TUSZ preprocessed data. This work highlights the potential of neural memory networks to support the field of epilepsy research, along with biomedical research and signal analysis more broadly.

READ FULL TEXT
research
12/10/2019

Neural Memory Networks for Robust Classification of Seizure Type

Classification of seizure type is a key step in the clinical process for...
research
01/11/2018

Deep Classification of Epileptic Signals

Electrophysiological observation plays a major role in epilepsy evaluati...
research
02/04/2019

Machine Learning for Seizure Type Classification: Setting the benchmark

Accurate classification of seizure types plays a crucial role in the tre...
research
03/18/2022

Analyzing EEG Data with Machine and Deep Learning: A Benchmark

Nowadays, machine and deep learning techniques are widely used in differ...
research
03/06/2018

Learning Memory Access Patterns

The explosion in workload complexity and the recent slow-down in Moore's...
research
12/28/2017

Deep Architectures for Automated Seizure Detection in Scalp EEGs

Automated seizure detection using clinical electroencephalograms is a ch...
research
10/06/2022

Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children

This paper presents a deep learning framework for image classification a...

Please sign up or login with your details

Forgot password? Click here to reset