TOWARDS INTERPRETABLE SEIZURE DETECTION USING WEARABLES.

使用磨损进行可解释的癫痫发作检测。

• 发表时间: 2023 Jun
• 来源期刊: Proc IEEE Int Conf Acoust Speech Signal Process
• DOI: 10.1109/icassp49357.2023.10097091
• PMID: 38682049
• 文章类型: Journal Article
• 作者列表: Al-Hussaini I,Mitchell CS

关键词: augmentation; eeg; electroencephalogram; imbalanced classes; interpretability; seizure; xai

Abstract

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.

摘要

使用机器学习进行癫痫发作检测是癫痫及时干预和管理的关键问题。我们提议SeizFt,使用可穿戴设备的EEG的强大癫痫发作检测框架。它使用与树木合奏配对的功能，从而可以进一步解释模型的结果。还证明了潜在的增强和类平衡策略的有效性。这项研究是针对2023年癫痫发作检测挑战进行的，这是一项ICASP大挑战。