用相似脑电图匹配实现可解释的癫痫放电检测
This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN
- 基于训练集中的相似脑电图进行案例推理
- 达到顶尖检测准确率且可直观展示判断依据
- 适合需要理解模型决策的临床医生使用
脑电图中间期癫痫样放电（IED）是癫痫的关键生物标志物。即使受过训练的神经科医生也难以识别IED，促使许多从业者转向机器学习辅助。现有机器学习算法虽能取得高精度，但多数模型不可解释，无法说明判断理由。缺乏对模型推理的理解，医生无法识别错误预测并干预。为此，我们提出ProtoEEG-kNN，一种内在可解释的模型，采用简单的基于案例推理机制。该模型通过将待测脑电图与训练集中相似样本比对，从形态（形状）和空间分布（位置）两方面直观展示推理过程。实验表明，ProtoEEG-kNN在IED检测上达到当前最优性能，且其解释结果更受专家青睐。
原文摘要 · Abstract (English)
The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IEDs difficult, leading many practitioners to turn to machine learning for help. While existing machine learning algorithms can achieve strong accuracy on this task, most models are uninterpretable and cannot justify their conclusions. Absent the ability to understand model reasoning, doctors cannot leverage their expertise to identify incorrect model predictions and intervene accordingly. To improve the human-model interaction, we introduce ProtoEEG-kNN, an inherently interpretable model that follows a simple case-based reasoning process. ProtoEEG-kNN reasons by comparing an EEG to similar EEGs from the training set and visually demonstrates its reasoning both in terms of IED morphology (shape) and spatial distribution (location). We show that ProtoEEG-kNN can achieve state-of-the-art accuracy in IED detection while providing explanations that experts prefer over existing approaches.
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