用长期iEEG记录预测癫痫发作：我们能从数据非平稳性中学到什么？
Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?
摘要：
反复癫痫发作损害了全球约6500万人，成功预测癫痫发作可以显著帮助患有难治性癫痫的患者。对于两条狗长达一年的颅内脑电图（iEEG）记录，我们研究了时间序列非平稳性对癫痫发作预测性能的影响，使用内部开发的机器学习算法。我们在iEEG时间序列中观察到周或月尺度的长期演变，其可以表示为在某些元状态之间切换。为了更好地预测即将发生的癫痫发作，预测算法的再训练因此是必要的，并且再训练时间表应根据元状态的变化进行调整。有证据表明，无癫痫发作间期剪辑的性质也随着元状态之间的转换而变化，这已被证明与癫痫发作预测相关。
英文摘要：
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.
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