利用脑电信号检测癫痫发作的新型深度学习框架

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Sayani Mallick, Veeky Baths
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引用次数: 0

摘要

导言癫痫是一种慢性神经系统疾病,其特点是大脑电活动异常,经常导致癫痫反复发作。全球有 5000 万癫痫患者,因此迫切需要高效、准确的方法来检测和诊断癫痫发作。脑电图(EEG)信号已成为检测癫痫和其他神经系统疾病的重要工具。传统上,为检测癫痫发作而分析脑电图信号的过程依赖于专家的人工检查,这种方法耗时、耗力,而且容易出现人为错误。为了解决这些局限性,研究人员转向了机器学习和深度学习技术,以实现癫痫发作检测过程的自动化。方法在这项工作中,我们提出了一种用于癫痫发作检测的新方法,充分利用了一维卷积层的力量,将双向长短期记忆(LSTM)和门控循环单元(GRU)以及平均池化层组合为一个单元。该单元在拟议模型中被反复用于提取特征。然后将这些特征传递给密集层,以预测脑电图波形的类别。拟议模型的性能在波恩数据集上得到了验证。为了评估我们提出的架构的鲁棒性和通用性,我们采用了五倍交叉验证。通过将数据集分为五个子集,并在这些子集的不同组合上反复训练和测试模型,我们获得了稳健的性能指标,包括准确性、灵敏度和特异性。讨论我们提出的模型在二元分类和多类分类的性能指标方面取得了显著改善。我们利用不同长度的脑电信号证明了所提出的架构在从脑电信号中准确检测癫痫发作方面的有效性。结果表明,它有潜力成为自动检测癫痫发作的可靠而高效的工具,为改善癫痫的诊断和管理铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel deep learning framework for detection of epileptic seizures using EEG signals
IntroductionEpilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals have emerged as a valuable tool in detecting epilepsy and other neurological disorders. Traditionally, the process of analyzing EEG signals for seizure detection has relied on manual inspection by experts, which is time-consuming, labor-intensive, and susceptible to human error. To address these limitations, researchers have turned to machine learning and deep learning techniques to automate the seizure detection process.MethodsIn this work, we propose a novel method for epileptic seizure detection, leveraging the power of 1-D Convolutional layers in combination with Bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and Average pooling Layer as a single unit. This unit is repeatedly used in the proposed model to extract the features. The features are then passed to the Dense layers to predict the class of the EEG waveform. The performance of the proposed model is verified on the Bonn dataset. To assess the robustness and generalizability of our proposed architecture, we employ five-fold cross-validation. By dividing the dataset into five subsets and iteratively training and testing the model on different combinations of these subsets, we obtain robust performance measures, including accuracy, sensitivity, and specificity.ResultsOur proposed model achieves an accuracy of 99–100% for binary classifications into seizure and normal waveforms, 97.2%–99.2% accuracy for classifications into normal-ictal-seizure waveforms, 96.2%–98.4% accuracy for four class classification and accuracy of 95.81%–98% for five class classification.DiscussionOur proposed models have achieved significant improvements in the performance metrics for the binary classifications and multiclass classifications. We demonstrate the effectiveness of the proposed architecture in accurately detecting epileptic seizures from EEG signals by using EEG signals of varying lengths. The results indicate its potential as a reliable and efficient tool for automated seizure detection, paving the way for improved diagnosis and management of epilepsy.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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