论文标题
通过脑电图的多通道成像评估癫痫发作风险评估
Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG
论文作者
论文摘要
屈光癫痫患者随时可能遭受癫痫发作。癫痫发作的预测将大大改善他们的生活。在这项工作中，基于头皮脑电图及其转换为图像，使用CNN的软极层输出（可能性）的平均值来计算癫痫发作的可能性，而不是分类层的输出。结果表明，通过分析可能性和阈值，预测具有较高的灵敏度或较低的FPR/H。对于5例患者来说，可能性的最佳阈值高于50％，而其余36例则较低。但是，需要进行更多的测试，尤其是在新的癫痫发作中，以更好地评估该方法的实际性能。这项工作是具有积极前景的概念证明。
Refractory epileptic patients can suffer a seizure at any moment. Seizure prediction would substantially improve their lives. In this work, based on scalp EEG and its transformation into images, the likelihood of an epileptic seizure occurring at any moment is computed using an average of the softmax layer output (the likelihood) of a CNN, instead of the output of the classification layer. Results show that by analyzing the likelihood and thresholding it, prediction has higher sensitivity or a lower FPR/h. The best threshold for the likelihood was higher than 50% for 5 patients, and was lower for the remaining 36. However, more testing is needed, especially in new seizures, to better assess the real performance of this method. This work is a proof of concept with a positive outlook.
