Seizure Detection Using Power Spectral Density via Hyperdimensional Computing
Seizure Detection Using Power Spectral Density via Hyperdimensional Computing
复制标题
通过超维计算使用功率谱密度进行癫痫发作检测
DOI：
10.1109/icassp39728.2021.9414083
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Parhi, Keshab K.
中科院分区：
文献类型：
--
作者：
Ge, Lulu;Parhi, Keshab K.
超维（HD）计算有望对两组数据进行分类。本文研究了基于功率谱密度（PSD）特征的HD计算在癫痫患者脑电（EEG）中的癫痫发作检测。公开可用的颅内脑电图（iEEG）数据收集从4只狗和8名人类患者在Kaggle癫痫发作检测比赛。本文探讨了两种分类方法。首先，在HD分类的上下文中使用来自先前分类的少量通道的几个排名的PSD特征。其次，从所有通道提取的所有PSD特征被用作HD分类的特征。结果表明，对于大约一半的主题，少量的功能优于所有功能的背景下，HD分类，和另一半，所有功能优于少量的功能。12名受试者中有6名的HD分类准确率达到95%以上，4名受试者的准确率在85-95%之间。对于两个主题，使用HD计算的分类精度不如经典的方法，如支持向量机分类器。
Hyperdimensional (HD) computing holds promise for classifying two groups of data. This paper explores seizure detection from electroencephalogram (EEG) from subjects with epilepsy using HD computing based on power spectral density (PSD) features. Publicly available intra-cranial EEG (iEEG) data collected from 4 dogs and 8 human patients in the Kaggle seizure detection contest are used in this paper. This paper explores two methods for classification. First, few ranked PSD features from small number of channels from a prior classification are used in the context of HD classification. Second, all PSD features extracted from all channels are used as features for HD classification. It is shown that for about half the subjects small number features outperform all features in the context of HD classification, and for the other half, all features outperform small number of features. HD classification achieves above 95% accuracy for six of the 12 subjects, and between 85-95% accuracy for 4 subjects. For two subjects, the classification accuracy using HD computing is not as good as classical approaches such as support vector machine classifiers.