Research Center for Advanced Science and Technology, The University of Tokyo, Japan.
Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, Japan.
Comput Biol Med. 2019 Jul;110:227-233. doi: 10.1016/j.compbiomed.2019.05.025. Epub 2019 Jun 5.
Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among patients makes it difficult to develop universal features that are applicable for automatic seizure detection in all cases, and the rarity of seizures results in a lack of sufficient training data for classifiers.
To overcome these problems, we utilized an autoencoder (AE), which is often used for anomaly detection in the field of machine learning, to perform seizure detection. We hypothesized that multichannel EEG signals are compressible by AE owing to their spatio-temporal coupling and that the AE should be able to detect seizures as anomalous events from an interictal EEG.
Through experiments, we found that the AE error was able to classify seizure and nonseizure states with a sensitivity of 100% in 22 out of 24 available test subjects and that the AE was better than the commercially available software BESA and Persyst for half of the test subjects.
These results suggest that the AE error is a feasible candidate for a universal seizure detection feature.
癫痫学家可能受益于一种能够自动检测癫痫发作的诊断支持系统，因为长时间的脑电图（EEG）的视觉检查极其耗时。然而，患者之间的癫痫发作类型多样，使得开发适用于所有情况的自动癫痫发作检测的通用特征变得困难，而且癫痫发作的罕见性导致分类器缺乏足够的训练数据。
为了克服这些问题，我们利用自动编码器（AE）来进行癫痫发作检测，自动编码器常用于机器学习领域的异常检测。我们假设多通道 EEG 信号由于其时空耦合而具有可压缩性，并且 AE 应该能够将癫痫发作检测为从间期 EEG 中异常的事件。
通过实验，我们发现 AE 错误能够以 100%的灵敏度对 24 名可用于测试的对象中的 22 名进行癫痫发作和非癫痫发作状态的分类，并且在一半的测试对象中，AE 优于商业上可用的软件 BESA 和 Persyst。
这些结果表明，AE 错误是通用癫痫发作检测特征的一个可行候选者。
