Supratak Akara
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4184-7. doi: 10.1109/EMBC.2014.6944546.
Scalp electroencephalogram (EEG), a recording of the brain's electrical activity, has been used to diagnose and detect epileptic seizures for a long time. However, most researchers have implemented seizure detectors by manually hand-engineering features from observed EEG data, and used them in seizure detection, which might not scale well to new patterns of seizures. In this paper, we investigate the possibility of utilising unsupervised feature learning, the recent development of deep learning, to automatically learn features from raw, unlabelled EEG data that are representative enough to be used in seizure detection. We develop patient-specific seizure detectors by using stacked autoencoders and logistic classifiers. A two-step training consisting of the greedy layer-wise and the global fine-tuning was used to train our detectors. The evaluation was performed by using labelled dataset from the CHB-MIT database, and the results showed that all of the test seizures were detected with a mean latency of 3.36 seconds, and a low false detection rate.
头皮脑电图（EEG），即大脑电活动的记录，长期以来一直用于诊断和检测癫痫发作。然而，大多数研究人员通过从观察到的脑电图数据中手动设计特征来实现癫痫发作检测器，并将其用于癫痫发作检测，这可能无法很好地扩展到新的癫痫发作模式。在本文中，我们研究了利用无监督特征学习（深度学习的最新发展）从原始的、未标记的脑电图数据中自动学习足够有代表性以用于癫痫发作检测的特征的可能性。我们使用堆叠自编码器和逻辑分类器开发了针对特定患者的癫痫发作检测器。采用由贪婪逐层训练和全局微调组成的两步训练来训练我们的检测器。使用来自CHB-MIT数据库的标记数据集进行评估，结果表明所有测试癫痫发作均被检测到，平均延迟为3.36秒，误检率较低。
