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基于ICA方法去除人工耳蝸ERP信號偽跡的研究

Removal of cochlear implant artifacts of ERP based on independent component analysis

作者: 閆立麗  張旭  陳雪清  傅新星  劉斌  錢柏霖                          
單位:                                 首都醫(yī)科大學生物醫(yī)學工程學院,臨床生物力學應用基礎研究北京市重點實驗室(北京 100069)            
關鍵詞:                               獨立成分分析;人工耳蝸;事件相關電位;偽跡;去噪              
分類號:
出版年·卷·期(頁碼):2015·34·2(111-117)
摘要:

目的 人工耳蝸植入者的聽覺誘發(fā)電位含有較大的偽跡信號,影響了其在人工耳蝸植入后的效果評估。功能成像方法由于安全問題和介入性等特點,不適用于人工耳蝸植入者。本文利用獨立成分分析(independent component analysis, ICA)去除人工耳蝸偽跡,為進一步利用聽覺誘發(fā)電位信號客觀評價人工耳蝸植入者言語識別能力和人工耳蝸植入效果提供便利。方法 采用經典Oddball模式,分別以言語聲/ba/和/da/為標準刺激和偏差刺激,測量人工耳蝸植入者的聽覺事件相關電位 (event-related potential, ERP),采用ICA方法去除ERP信號中人工耳蝸造成的偽跡,并分析其獨立成分的時域波形和腦地形圖特征。本文對10例人工耳蝸植入6個月的受試者進行ERP測試,并比較了Infomax和 Jade兩種算法去除人工耳蝸偽跡的效果。結果 根據(jù)獨立成分的時域波形和腦地形圖特征,可以將人工耳蝸偽跡對應的獨立成分識別出來。人工耳蝸偽跡獨立成分的時域波形類似于一個基座,其腦地形圖顯示在植入側有較高的電位。去除人工耳蝸偽跡后的ERP波形顯示出原始的形態(tài)。Infomax算法能夠更有效地去除ERP信號中的人工耳蝸偽跡。結論 ICA方法可以有效地將人工耳蝸偽跡從人工耳蝸植入者的ERP信號中分離出來。

Objective The auditory evoked potentials (AEP) of cochlear implant (CI) users contained CI-related artifact, which restrict the assessment for implantation effect. Functional imaging methods are not suitable for CI users due to the safety and invasiveness. The primary objective of this study is to investigate noise-reduction of event-related potentials (ERP) for CI users based on independent component analysis (ICA). It can provide more information on assessment for speech perception and implantation effect of CI users with AEP. Methods The standard (/ba/) and deviant stimuli (/da/) were presented in an oddball paradigm and the ERPs of cochlear implant users were recorded. ICA was used to remove the cochlear implant artifacts in ERP. The characteristic of waveform and brain topographic map of cochlear implant artifacts were analyzed. Ten CI users with device ‘switch on’ for six months took part in this experiment and the effect of artifacts removal with Infomax and Jade algorithms were compared. Results The cochlear implant artifacts could be recognized by the characteristic of the waveform and brain topographic maps of independent components. The waveform of cochlear implant artifacts resembled a pedestal and its brain topographic map showed higher potential on the implantation side. The ERP waveforms displayed reasonable morphologies after the removal of cochlear implant artifacts. The Imfomax algorithm was better to remove the CI-related artifacts in ERP. Conclusions The cochlear implant artifacts could be removed from ERP of cochlear implant users based on ICA.

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