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Scalp EEG signal reconstruction for detection of mild cognitive impairment and early Alzheimer's disease

机译：头皮EEG信号重建检测轻度认知障碍和早期阿尔茨海默病

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Mild cognitive impairment (MCI) is a neurological disease which is often comorbid with early stages of Alzheimer's disease (AD). This study explores the potential for detecting changes in neurological functional organization which may be indicative of MCI and early AD using neural network models for scalp EEG signal reconstruction. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)—15 normal controls (NC), 16 MCI, and 17 early-stage AD—are examined. Neural network models are trained to reconstruct artificially “deleted” samples of EEG using subsets of records from NC participants. Models are applied to EEG records and quality scores are assigned to reconstructions of individual channels. Principal components of regional average reconstruction quality scores are used in a support vector machine model to discriminate between groups. Analyses demonstrate accuracies of 90.3% for MCI vs. NC (p-value<0.0005), 90.6% for AD vs. NC (p-value<0.0003), and 87.5% for AD/MCI vs. NC (p-value<0.0003). Techniques developed here may be used to detect changes in EEG activity due to neurological degeneration associated with MCI and early AD.
机译：轻度认知障碍（MCI）是一种神经疾病，其通常是具有阿尔茨海默病（AD）的早期阶段的合并。本研究探讨了检测神经功能组织的变化的可能性，该组织可能指示MCI和早期广告，使用神经网络模型进行头皮EEG信号重建。从48岁匹配的参与者休息32通道EEG记录（平均75.7岁）-15正常控制（NC），16 MCI和17个早期的广告。使用来自NC参与者的记录子集，训练神经网络模型以重建人工“已删除”样本。模型应用于EEG记录，并且质量分数被分配给各个频道的重建。区域平均重建质量评分的主要成分用于支持向量机模型以区分组。分析证明了MCI与NC（P值<0.0005）的90.3％的准确度，对于AD与NC（P值<0.0003），90.6％，AD / MCI与NC的87.5％（P值<0.0003 ）。这里开发的技术可用于检测由于与MCI和早期广告相关的神经系统变性引起的脑电图活动的变化。

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