TOWARDS INTERPRETABLE SEIZURE DETECTION USING WEARABLES.
TOWARDS INTERPRETABLE SEIZURE DETECTION USING WEARABLES.
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DOI：
10.1109/icassp49357.2023.10097091
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发表时间：
2023-06-01
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通讯作者：
Mitchell, Cassie S
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作者：
Al-Hussaini, Irfan;Mitchell, Cassie S
使用机器学习进行癫痫发作检测是及时干预和管理癫痫的关键问题。我们提出SeizFt，一个强大的癫痫发作检测框架，使用EEG从可穿戴设备。它使用与树的集合配对的特征，从而能够进一步解释模型的结果。底层的增强和类平衡策略的有效性也得到了证明。这项研究是为2023年癫痫发作检测挑战赛（ICASSP大挑战赛）进行的。
Seizure detection using machine learning is a critical problem for the timely intervention and management of epilepsy. We propose SeizFt, a robust seizure detection framework using EEG from a wearable device. It uses features paired with an ensemble of trees, thus enabling further interpretation of the model's results. The efficacy of the underlying augmentation and class-balancing strategy is also demonstrated. This study was performed for the Seizure Detection Challenge 2023, an ICASSP Grand Challenge.