Exploration of EEG features of Alzheimer’s disease using continuous wavelet transform

Abstract

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.

Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

“…Moreover, the data from channel P 3 was chosen in the simulations due to its closeness to the parietal region, which is one of the regions to be later affected by AD. Wavelet digital filter [58,59] were used to extract the four EEG frequency bands, i.e., delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Analogous to previous cases, as all time series have the same length (T = 1, 024), we used Q = 2(1, 024) 1/3 � 20 and k = 1, 2, .…”

“…Nonetheless, the diagnosis of AD from EEG data is still open research topic, and it comes without surprise the wealth of methods proposed in the medical and related literature. These methods, based on Fast Fourier Transform (FFT) [17][18][19], Wavelet Transform (WT) [20][21][22][23], Phase-Space Reconstruction [24][25][26], Eigenvector Methods (EMs) [27,28], Time Frequency Distributions (TFDs) [29], and the Auto-Regressive Method (ARM) [30], generally require from the input signal one or more of the following assumptions: stationarity, high time or frequency resolution, and/or a high signal-to-noise ratio.…”

Analysis of long range dependence in the EEG signals of Alzheimer patients

“…Functional disconnection (Tóth et al 2014), reduced corpus callosum cross-sectional area (Pogarell et al 2005) and decreased Relative Wavelet Energy (RWE) (Jeong et al 2016) helps to identify AD. Statistical pattern recognition (Snaedal et al 2010(Snaedal et al , 2012, Automatic recognition (Kim et al 2005), Machine learning approach (Podgorelec 2012), Discrete wavelet transform (DWT) (Ghorbanian et al 2013), Continuous wavelet transform (CWT) (Ghorbanian et al 2015) are some of the techniques used for AD analysis.…”

Effects of Exercise on EEG Activity and Standard Tools Used to Assess Concussion

“…EEG signal DC offsets were removed independently for each recording epoch. Large amplitude artifacts (greater than 4.5 standard deviation) were identified and replaced with surrogate data derived from FFT interpolation of preceding and trailing data [22, 26]. This approach successfully removed the majority of eye-blink artifact and other large amplitude artifacts while preserving the power spectrum (Supplemental Figure 1).…”