BackgroundIn this study, nonlinear based time–frequency (TF) and time domain investigations are employed for the analysis of electroencephalogram (EEG) signals of mild cognitive impairment–Alzheimer’s disease (MCI–AD) patients and healthy controls. This study attempts to comprehend the cognitive decline of MCI–AD under both resting and cognitive task conditions.ResultsWavelet-based synchrosqueezing transform (SST) alleviates the smearing of energy observed in the spectrogram around the central frequencies in short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A precise TF representation is assured due to the reassignment of scale variable to the frequency variable. It is discerned from the studies of time domain measures encompassing fractal dimension (FD) and approximate entropy (ApEn), that the parietal lobe is the most affected in MCI–AD under both resting and cognitive states. Alterations in asymmetry in the brain hemispheres are analysed using the homologous areas inter-hemispheric symmetry (HArS).ConclusionTime and time–frequency domain analysis of EEG signals have been used for distinguishing various brain states. Therefore, EEG analysis is highly useful for the screening of AD in its prodromal phase.

中文翻译：

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MCI-AD大脑活动的时频重分配和同源半球间不对称性分析的探索

背景在这项研究中，基于非线性的时频 (TF) 和时域研究用于分析轻度认知障碍 - 阿尔茨海默病 (MCI-AD) 患者和健康对照的脑电图 (EEG) 信号。本研究试图了解 MCI-AD 在休息和认知任务条件下的认知衰退。 结果基于小波的同步压缩变换 (SST) 减轻了在短时傅立叶变换 (STFT) 中中心频率周围的频谱图中观察到的能量拖尾, 和连续小波变换 (CWT)。由于将比例变量重新分配给频率变量，因此可以确保精确的 TF 表示。从包括分形维数 (FD) 和近似熵 (ApEn) 的时域度量研究中可以看出，顶叶在 MCI-AD 中在静息和认知状态下受到的影响最大。使用同源区域半球间对称性 (HArS) 分析大脑半球不对称的变化。结论 EEG 信号的时间和时频域分析已用于区分各种大脑状态。因此，脑电图分析对于筛查处于前驱期的 AD 非常有用。