Department of Electrical Engineering, National Institute of Technology Calicut, Calicut, Kerala, India.
Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India.
BMC Neurosci. 2019 Jul 31;20(1):38. doi: 10.1186/s12868-019-0519-3.
In 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.
Wavelet-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).
Time 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.
在这项研究中，采用基于非线性的时频（TF）和时域分析方法，对轻度认知障碍-阿尔茨海默病（MCI-AD）患者和健康对照者的脑电图（EEG）信号进行分析。本研究试图在静息和认知任务状态下理解 MCI-AD 的认知下降。
基于小波的同步挤压变换（SST）缓解了短时傅里叶变换（STFT）和连续小波变换（CWT）中在中央频率附近的能量图谱中观察到的能量弥散现象。由于将尺度变量重新分配到频率变量，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）的时域测量研究中可以看出，在静息和认知状态下，顶叶是 MCI-AD 中受影响最大的区域。使用同源区域半球间对称性（HArS）分析大脑半球的不对称性变化。
对 EEG 信号的时频域分析已被用于区分各种大脑状态。因此，EEG 分析对于在 AD 的前驱期进行 AD 的筛查非常有用。
