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脑电图配准的统计模式识别在认知障碍和痴呆评估中的诊断准确性。

Diagnostic accuracy of statistical pattern recognition of electroencephalogram registration in evaluation of cognitive impairment and dementia.

机构信息

Memory Clinic, Geriatric Department, National University Hospital, Reykjavík, Iceland.

出版信息

Dement Geriatr Cogn Disord. 2012;34(1):51-60. doi: 10.1159/000339996. Epub 2012 Aug 23.

DOI:10.1159/000339996
PMID:22922592
Abstract

BACKGROUND

There is still a need for simple, noninvasive, and inexpensive methods to diagnose the causes of cognitive impairment and dementia. In this study, contemporary statistical methods were used to classify the clinical cases of cognitive impairment based on electroencephalograms (EEG).

METHODS

An EEG database was established from seven different groups of subjects with cognitive impairment and dementia as well as healthy controls. A classifier was created for each possible pair of groups using statistical pattern recognition (SPR).

RESULTS

A good-to-excellent separation was found when differentiating cases of degenerative disorders from controls, vascular disorders, and depression but this was less so when the likelihood of comorbidity was high.

CONCLUSIONS

Using EEG with SPR seems to be a reliable method for diagnosing the causes of cognitive impairment and dementia, but comorbidity must be taken into account.

摘要

背景

目前仍需要简单、无创且廉价的方法来诊断认知障碍和痴呆的病因。本研究使用现代统计方法基于脑电图(EEG)对认知障碍的临床病例进行分类。

方法

我们从认知障碍和痴呆以及健康对照组的七个不同组的受试者中建立了一个脑电图数据库。使用统计模式识别(SPR)为每对可能的组创建一个分类器。

结果

退行性疾病与对照组、血管性疾病和抑郁症之间的区分效果较好,但当共病的可能性较高时,区分效果则较差。

结论

使用 SPR 进行脑电图似乎是诊断认知障碍和痴呆病因的可靠方法,但必须考虑到共病情况。

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