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.
翻译:缉获类型分类是评估提交缉获的个人的临床过程的关键步骤,它决定了临床诊断和治疗过程,其影响超出了临床领域的范围,包括癫痫研究与新疗法的发展。自动识别缉获类型可能有助于了解该疾病,而缉获检测和预测是最近研究的重点,研究的目的是利用机器学习和深层学习结构的好处。然而,尚没有实现缉获类型分类自动化的确定性解决办法,目前必须由一名专家流行病学家完成这项任务。在神经记忆网络最近的进展的启发下,我们采用了一种新的方法,利用电子生理数据对缉获类型进行分类。我们首先探索使用进化和经常性神经网络的传统深层学习技术的性能,并通过利用外部记忆模块和可训练的神经造型塑料来增强这些结构。我们发现,我们的模型在TUH EEEG 缉获公司(NMNN)最近的进展下,我们采用了一种新的方法,利用电子物理数据对缉获类型进行分类。我们首先探索的是使用进化和经常神经网络的传统深层学习技术的性,并使用外部记忆模块和可训练的神经塑料塑料塑料塑料塑料塑料塑料塑料塑料塑料结构。我们展示的实地研究中取得了最新的研究潜力。