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Semi-automatic attenuation of cochlear implant artifacts for the evaluation of late auditory evoked potentials

机译:半自动衰减人工耳蜗植入物,用于评估晚期听觉诱发电位

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Electrical artifacts caused by the cochlear implant (CI) contaminate electroencephalographic (EEG) recordings from implanted individuals and corrupt auditory evoked potentials (AEPs). Independent component analysis (ICA) is efficient in attenuating the electrical CI artifact and AEPs can be successfully reconstructed. However the manual selection of CI artifact related independent components (ICs) obtained with ICA is unsatisfactory, since it contains expert-choices and is time consuming.We developed a new procedure to evaluate temporal and topographical properties of ICs and semi-automatically select those components representing electrical CI artifact. The CI Artifact Correction (CIAC) algorithm was tested on EEG data from two different studies. The first consists of published datasets from 18 CI users listening to environmental sounds. Compared to the manual IC selection performed by an expert the sensitivity of CIAC was 91.7% and the specificity 92.3%. After CIAC-based attenuation of CI artifacts, a high correlation between age and N1-P2 peak-to-peak amplitude was observed in the AEPs, replicating previously reported findings and further confirming the algorithm's validity.In the second study AEPs in response to pure tone and white noise stimuli from 12 CI users that had also participated in the other study were evaluated. CI artifacts were attenuated based on the IC selection performed semi-automatically by CIAC and manually by one expert. Again, a correlation between N1 amplitude and age was found. Moreover, a high test-retest reliability for AEP N1 amplitudes and latencies suggested that CIAC-based attenuation reliably preserves plausible individual response characteristics.We conclude that CIAC enables the objective and efficient attenuation of the CI artifact in EEG recordings, as it provided a reasonable reconstruction of individual AEPs. The systematic pattern of individual differences in N1 amplitudes and latencies observed with different stimuli at different sessions, strongly suggests that CIAC can overcome the electrical artifact problem. Thus CIAC facilitates the use of cortical AEPs as an objective measurement of auditory rehabilitation.
机译:人工耳蜗(CI)引起的电伪影污染了植入人的脑电图(EEG)记录,并破坏了听觉诱发电位(AEP)。独立分量分析(ICA)可有效衰减电CI伪影,并且可以成功重建AEP。但是,通过ICA手动选择与CI工件相关的独立组件(IC)并不令人满意,因为它包含专家选择并且很耗时。我们开发了一种新的程序来评估IC的时间和地形特性,并半自动选择这些组件表示电气CI伪影。 CI伪影校正(CIAC)算法在来自两项不同研究的EEG数据上进行了测试。第一个由来自18位CI用户收听环境声音的已发布数据集组成。与专家进行的手动IC选择相比,CIAC的敏感性为91.7%,特异性为92.3%。在基于CIAC的CI伪影衰减之后,在AEP中观察到了年龄与N1-P2峰-峰幅度之间的高度相关性,从而复制了先前报道的发现并进一步证实了该算法的有效性。还评估了也参加了另一项研究的12名CI用户的音调和白噪声刺激。 CI伪影基于CIAC半自动执行的IC选择,并由一位专家手动进行了IC选择。再次,发现了N1振幅与年龄之间的相关性。此外,对AEP N1振幅和潜伏期的高重测可靠性表明,基于CIAC的衰减可靠地保留了合理的个体响应特征。我们得出结论,CIAC可以客观有效地衰减EEG记录中的CI伪影,因为它提供了合理的条件重建单个AEP。在不同的阶段在不同的刺激下观察到的N1振幅和潜伏期个体差异的系统模式,强烈表明CIAC可以克服电伪像问题。因此,CIAC促进了将皮质AEP用作听觉康复的客观指标。

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