IEEE Trans Biomed Eng. 2021 Aug;68(8):2435-2446. doi: 10.1109/TBME.2020.3042574. Epub 2021 Jul 16.
Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-threatening. Modern systems monitoring electroencephalography (EEG) signals are being currently developed with the view to detect epileptic seizures in order to alert caregivers and reduce the impact of seizures on patients' quality of life. Such seizure detection systems employ state-of-the-art machine learning algorithms that require a large amount of labeled personal data for training. However, acquiring EEG signals during epileptic seizures is a costly and time-consuming process for medical experts and patients. Furthermore, this data often contains sensitive personal information, presenting privacy concerns. In this work, we generate synthetic seizure-like brain electrical activities, i.e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for sensitive recorded data. Our experiments show that the synthetic seizure data generated with our GAN model succeeds at preserving the privacy of the patients without producing any degradation in performance during seizure monitoring.
癫痫是一种影响全球超过 6500 万人的慢性神经系统疾病，其特征是反复发作的无诱因癫痫发作。癫痫发作的不可预测性不仅降低了患者的生活质量，而且还可能危及生命。目前正在开发用于监测脑电图 (EEG) 信号的现代系统，以期检测癫痫发作，从而提醒护理人员并降低癫痫发作对患者生活质量的影响。此类癫痫检测系统采用最先进的机器学习算法，需要大量经过标记的个人数据进行训练。然而，获取癫痫发作期间的 EEG 信号对于医学专家和患者来说是一个昂贵且耗时的过程。此外，这些数据通常包含敏感的个人信息，引发了隐私问题。在这项工作中，我们生成类似于癫痫发作的脑电活动，即 EEG 信号，可以用于训练癫痫检测算法，从而减轻对敏感记录数据的需求。我们的实验表明，我们的 GAN 模型生成的合成癫痫数据成功地保护了患者的隐私，而在癫痫监测过程中不会降低性能。
