论文标题
长期IEEG录音的癫痫发作预测：我们可以从数据非机构性中学到什么？
Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?
论文作者
论文摘要
反复进行的癫痫发作会损害全球约6500万人，成功的癫痫发作可以极大地帮助患有难治性癫痫的患者。对于两只具有颅内脑电脑术（IEEG）录音的狗，我们研究了时间序列非组织性对使用内部开发的机器学习算法进行癫痫发作预测的影响。我们观察到IEEG时间序列数周或数月的长期演变，可能表示为在某些元国家之间切换。为了更好地预测即将来临的癫痫发作，需要对预测算法进行重新训练，并且应将再训练时间表调整为元国家的变化。有证据表明，无癫痫发作夹的性质也随着元态之间的过渡而变化，ACC已显示与癫痫发作预测有关。
Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly help patients suffering from refractory epilepsy. For two dogs with yearlong intracranial electroencephalography (iEEG) recordings, we studied the influence of time series nonstationarity on the performance of seizure prediction using in-house developed machine learning algorithms. We observed a long-term evolution on the scale of weeks or months in iEEG time series that may be represented as switching between certain meta-states. To better predict impending seizures, retraining of prediction algorithms is therefore necessary and the retraining schedule should be adjusted to the change in meta-states. There is evidence that the nature of seizure-free interictal clips also changes with the transition between meta-states, accwhich has been shown relevant for seizure prediction.
