[论文解读] Principal Component Analysis for seizure characterization in EEG signals

Melisa Maidana Capitán, Nuria Cámpora+2 more|arXiv (Cornell University)|Feb 28, 2019

EEG and Brain-Computer Interfaces被引用 3

一句话总结

该论文提出了一种无监督主成分分析（PCA）方法，通过分析频带内的功率波动，对颅内脑电图（EEG）信号中的癫痫发作进行表征。该方法实现了85%的精确率和88%的召回率，可在线检测癫痫发作，并识别出与意识丧失相关的生理指标，例如发作起始时α频带功率方差增加。

ABSTRACT

A large variety of methods has been proposed for automatic seizure detection in EEG signals. Those achieving maximal performance are based on machine-learning techniques, which require long training sessions with large labelled databases, and produce a verdict with no intuitive justification. As an alternative, we here explore an unsupervised algorithm applicable to intra-cranial EEG recordings that requires good sampling of the non-ictal periods, but imposes no demands on the amount of data during ictal activity. The algorithm analyses how the amount of power in each frequency band fluctuates ( an evaluation physicians are familiar with ) and can be implemented online. The analysis is performed electrode by electrode, thus providing the spatio-temporal sequence in which the affected regions are recruited into the crisis. We test it with 32 crisis registered in 5 patients, for which we also have the degree of loss of consciousness as determined from a behavioural analysis. The method can achieve a precision of 85% and a recall of 88% in the identification of seizures, and 77% and 88% in the identification of recruited electrodes. The intuitive nature of the analysis allows us to identify certain physiological features that are correlated with the degree of loss of consciousness. For example, epileptic crisis in which the variance of the power in the alpha band increases at around seizure onset are particularly likely to impair consciousness. We conclude that the PCA of the distribution of power in different frequency bands provides information both about the detection and the characterization of epileptic seizures.

研究动机与目标

• 开发一种无监督、可解释的方法，用于在无需大规模标注数据集的情况下，检测和表征颅内EEG中的癫痫发作。
• 识别EEG功率动态中与发作期间意识丧失程度相关的生理标志物。
• 实现实时、电极级别的分析，以追踪发作起始的时空演变过程。
• 通过利用直观的基于功率的特征，减少对复杂机器学习模型及其决策过程不透明性的依赖。

提出的方法

• 对颅内EEG记录中各频带（如δ、θ、α、β、γ）的功率分布应用主成分分析（PCA）。
• 分析每个电极在各频带上的功率时间波动，该特征为临床医生所熟悉。
• 基于每个电极处理数据，以重建大脑区域中发作起始的时空序列。
• 利用第一主成分捕捉功率变化的主导模式，特别是在发作起始附近。
• 聚焦于发作间期进行训练，对发作期数据需求极少，即使在发作样本有限的情况下也具有适用性。
• 实现算法在线化，以支持实时监测与检测。

实验结果

研究问题

• RQ1 对频带功率分布进行PCA能否在颅内EEG中以高准确率和可解释性检测癫痫发作？
• RQ2 在特定频带中，哪些功率动态与发作期间意识丧失的程度相关？
• RQ3 该方法在多大程度上能够追踪发作起始在各电极上的时空演变过程？
• RQ4 无监督方法能否在无需大规模标注数据集的情况下，实现与监督机器学习方法相当的性能？

主要发现

• 该方法在5名患者的32次癫痫发作中，实现了85%的精确率和88%的召回率。
• 在识别受累电极方面，该方法达到77%的精确率和88%的召回率，表明具有较强的定位准确性。
• 发作起始附近α频带功率方差的增加，与意识障碍可能性升高显著相关。
• 基于PCA的分析提供了一个可解释、直观的框架，与临床评估实践一致。
• 该方法仅需良好采样的发作间期数据和极少的发作期数据，因此适用于发作记录有限的数据集。
• 该方法支持在线实现，可实现实时监测癫痫动态。