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首页> 外文会议>Asilomar Conference on Signals, Systems, and Computers >A deep Learning Scheme for Automatic Seizure Detection from Long-Term Scalp EEG
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A deep Learning Scheme for Automatic Seizure Detection from Long-Term Scalp EEG

机译：长期头皮EEG自动癫痫发作检测的深度学习方案

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摘要

Epilepsy is a chronic brain disorder that is expressed by seizures. Monitoring brain activity via electroencephalogram (EEG) is an established method for epilepsy diagnosis and for monitoring epilepsy patients. Yet, it is not favorable to visually inspect EEG signals to diagnose epilepsy, especially in the case of long-term recordings. This process is time consuming and tedious error-prone exercise. In recent years, the sub-field of machine learning called deep learning has achieved remarkable success in various artificial intelligence research areas. In this paper, we present a method based on the deep convolutional neural networks (CNNs) to perform unsupervised feature learning framework for automated seizure onset detection. The proposed system was evaluated on 526 hours duration of scalp EEG data, including 181 seizures of 23 pediatric patients. The different parameters of CNNs were optimized through 4-fold nested cross-validation. The resulting generalized CNN seizure detection model achieved an average sensitivity of 86.29%, an average false detection rate of 0.74 h-1 and an average detection latency of 2.1 sec.
机译：癫痫是一种由癫痫发作表达的慢性脑病。通过脑电图（EEG）监测脑活动是癫痫诊断和监测癫痫患者的既定方法。然而，目视检查EEG信号是否诊断癫痫，特别是在长期记录的情况下。这个过程是耗时和繁琐的错误锻炼。近年来，机器学习子领域称为深度学习在各种人工智能研究领域取得了显着成功。在本文中，我们介绍了一种基于深度卷积神经网络（CNNS）的方法，以对自动癫痫发作检测执行无监督的特征学习框架。拟议的系统是在第526小时的头皮EEG数据持续时间内进行评估，包括181例儿科患者癫痫发作。 CNN的不同参数通过4倍嵌套交叉验证优化。所得到的广义CNN癫痫发作检测模型实现了86.29℃的平均灵敏度，平均假检出率为0.74H-1，平均检测延迟为2.1秒。

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