Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data.

Graphical abstract

临床脑电图数据中的癫痫性癫痫发作检测和分类仍然是一个挑战，使用市售的癫痫发作检测工具只能实现低灵敏度和高误报率，这些工具通常是患者非特异性的。癫痫患者由于不可预测的癫痫发作而遭受严重的有害影响，例如身体伤害或抑郁。但是，即使在医院中，由于误报率很高，癫痫发作警报系统对患者的帮助也很差，因为癫痫发作检测工具大多是针对不切实际的干净数据进行训练的，几乎没有噪音，并且是在受控实验室条件下获得的，在这种情况下，患者群体是同质的，例如就年龄或癫痫发作类型而言。在这项研究中，作者介绍了使用脑电图的临床数据和对大脑同步和功率谱特征进行训练的卷积神经网络对癫痫发作进行检测和分类的方法。应用了各种深度学习方法，并在非常异构的临床脑电图数据集上训练了网络。总共考虑了八种不同类型的癫痫发作，并且患者年龄，健康状况各异，并在临床条件下进行了观察。尽管如此，本文中提出的分类器仍实现了等于0.68和0.67的灵敏度和特异性，与临床数据的已知结果相比，这是一个重大改进。

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