Long‐term electroencephalography (EEG) monitoring is time‐consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short‐term window size. Therefore, our method can be utilized to interpret long‐term EEG results and detect momentary seizure waveforms in diagnostic systems.

中文翻译：

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基于隐马尔可夫模型和脑电信号的小波系数计数平均斜率特征的自动癫痫发作波形检测方法

长期脑电图（EEG）监视非常耗时，需要专家解释脑电图信号以检测患者的癫痫发作。在本文中，我们提出了一种新颖的自动方法，称为小波系数计数在各种阈值上的自适应斜率（ASCOT），以将患者发作分类为发作波形。ASCOT涉及通过计算每个频率子带中各种阈值上的小波系数计数的平均斜率来提取特征矩阵。我们使用自己的数据库和公共数据库验证了我们的方法，以避免过度调整。实验结果表明，该方法在我们自己的数据库（98.93％）和公共数据库（99.78％）中均达到了可靠且有希望的准确性。最后，我们在考虑各种窗口大小的情况下评估了该方法的性能。结论，所提出的方法在短期窗口大小下实现了可靠的癫痫发作检测性能。因此，我们的方法可用于解释长期脑电图结果并检测诊断系统中的瞬时癫痫发作波形。