重症监护病人脑电数据的自动聚类分析

重症监护病人脑电数据的自动聚类分析
Jin Jing;Emile D'angremonta;Senan Ebrahim;Mohammad Ghassemi;Eric Rosenthal;Sahar Zafar;M.Brandon Westover
【摘要】Seizures,status epilepticus,and seizure-like rhythmic or periodic activity are com-mon,pathological,and harmful states of brain electrical activity seen in the electroencephalo-gram (EEG)of patients during critical medical illnesses or acute brain injury. In this study, we aimed to develop a valid method to automatically discover a small number of homogeneous pattern clusters,to facilitate efficient interactive labelling by EEG experts. Long term continu-ous EEG of ten ICU patients at MGH were analysed,undergoing the pipeline of feature extrac-tion,PCA-based dimensionality reduction,and embedding through LE map. This research sug-gests that large EEG datasets can be automatically clustered into a small number of patterns de-scribed by standard ICU EEG pattern labels. We demonstrated efficient cluster labelling by in-specting only the centroids of clusters. Furthermore,LE visualizations support the hypothesis of an interictal-ictal continuum.%癫痫性发作、持续状态及痫样节律性活动是常见的病理性脑部放电状态,通常会在急性脑损伤患者的脑电图(EEG)中表现出来.完成此类病理性波形的有效标记,是进一步诊断与治疗相关疾病的重要前提.为辅助神经内科专家对不同病理波形进行快速标记,文中提出了一种全新的辅助检测标记系统.该系统分别采用特征提取、PCA降维和LE映射可视化等技术,实现EEG中同质模式簇的自动检测.所提方法对哈佛医学院/麻省总医院中10例ICU患者的长时程连续脑电图进行了系统分析.数值实验结果表明,海量脑电数据能够被有效地自动聚类
为多种ICU典型标准波形,而且仅通过观测类中心及若干同类成员就能够达到有效标记的目标.同时,LE可视化结果也进一步证实了"发作间期-发作期"连续统假设是成立的.
【期刊名称】《西北大学学报（自然科学版）》
【年(卷),期】2018(048)001
【总页数】4页(P6-9)
【关键词】聚类;重症监护;脑电图;发作;"发作间期-发作期"连续统假设;评分者间统一度
【作者】Jin Jing;Emile D'angremonta;Senan Ebrahim;Mohammad Ghassemi;Eric Rosenthal;Sahar Zafar;M.Brandon Westover
【作者单位】哈佛医学院/麻省总医院神经内科,美国波士顿 02114;乌得勒支大学理学院,荷兰乌得勒支 80125;哈佛医学院/麻省总医院神经内科,美国波士顿02114;麻省理工学院理学院,美国波士顿 02114;哈佛医学院/麻省总医院神经内科,美国波士顿 02114;哈佛医学院/麻省总医院神经内科,美国波士顿 02114;哈佛医学院/麻省总医院神经内科,美国波士顿 02114
【正文语种】中文
【中图分类】O29
1 Introduction
Seizures, status epilepticus, and seizure-like rhythmic or periodic activity are common, pathological, and harmful states of brain electrical activity
seen in the electroencephalogram (EEG) of patients during critical medical illnesses or acute brain injury[1-2]. A growing body of evidence shows that these states, when prolonged, cause neurological injury[3-4]. In this study, we aimed to develop a valid method to automatically discover a small number of homogeneous pattern clusters, to facilitate efficient interactive labelling by EEG experts.
2 Method
In this study, we analysed continuous EEG recordings from 10 different ICU patients at MGH. The duration of each recording is at least 12 hours, with a sampling rate of 200 Hz. Digital filters were applied to remove artifacts such as powerline interference, and baseline drift. In addition, spectrograms was prepared for frequency domain feature extraction[5-6].
In total, as listed in Table 1, we extracted 576 time and frequency domain features from each EEG recording.
Tab.1 Temporal and spectral features extracted from
EEG.TemporalfeatureFeaturecalculationLinelength[7]Kurtosis[8]Shannonent ropy[9]AbsolutevalueNonlinearenergyoperator[10]MeanandSDSpectralfeat ureRelativedelta，theta，alpha，betaMean，min，SD，
the95thpercentileDelta/theta，delta/alpha，theta/alpharatiosMean，min，SD，the95thpercentileDelta，theta，alpha，betakurtosis
After feature extraction, we applied principal component analysis (PCA)[11] with 90% variance retained to reduce the dimensionality for each feature array. It is followed by unsupervised clustering method K-means[12], to further split the data into 9 clusters using K-means. From each cluster we
took 9 random samples plus the cluster center, rendering 900 samples in total. Three experts independently labelled all samples into one of 6 standard pattern categories (seizures, GPDs, LPDs, LRDA, GRDA, burst suppression, other).
We compared two methods for labelling clusters: (1) “Labour i ntensive labelling” (LIL): assign the most frequent of 30 expert provided labels; (2) “Labour efficient labelling “(LEL): assign the most frequent of the 3 expert labels for the central sample. We compared interrater agreement (IRA) indexed by Gwet′s AC1[13] among experts vs. between each expert and consensus labels using LIL vs. LEL. Finally, we used Laplacian Eigenmaps (LE)[14] to visualize the data, as shown in Figure 1.
Fig.1 Laplacian Eigenmaps for 2-D visualization of high-D data.
3 Results
Median [IQR] expert-expert IRA for all label pairs across subjects was 0.65 [0.58, 0.75]. IRA for individual expert labels and the final consensus label was 0.76 [0.70, 0.82] using LIL, and 0.71 [0.63, 0.78] using LEL. The boxplots are shown in Figure 2. Differences between LIL and LEL were not statistically significant (p=0.34). As illustrated in Figures 3a-f, LE visualizations of the feature space generally revealed a continuum.
Fig.2 Boxplots of IRA Gwet′s AC1 index for expert-expert [Ex vs Ex], expert-LIL [Ex vs LIL], and expert-LEL [Ex vs LEL].
Fig.3 LE visualizations of the feature space generally revealed a continuum of EEG patterns.
4 Conclusion
This research suggests that large EEG datasets can be automatically clustered into a small number of patterns described by standard ICU EEG pattern labels. We demonstrated efficient cluster labelling by inspecting only the central most representative of each cluster. Furthermore, LE visualizations support the hypothesis of an interictal-ictal continuum. References:
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