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EEG Wavelet Spectral Analysis during a Working Memory Tasks in StrokeRelated Mild Cognitive Impairment Patients
Noor Kamal Al-Qazzaz1,5, Sawal Ali1, Siti Anom Ahmad2, Md. Shabiul Islam3, Javier Escudero4
1
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan
Malaysia; UKM Bangi, Selangor 43600, Malaysia
2 Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia;UPM Serdang, Selangor 43400,
Malaysia
3 Institute of Microengineering and Nanoelectronics (IMEN); Universiti Kebangsaan Malaysia; UKM Bangi, Selangor 43600, Malaysia
4 Institute for Digital Communications; School of Engineering; The University of Edinburgh; Edinburgh EH9 3JL; United Kingdom
5 Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad 47146, Iraq
Abstract— The aim of this study was to analyse the electroencephalography (EEG) background activity of 10 strokerelated patients with mild cognitive impairment (MCI) using
spectral entropy (SpecEn) and spectral analysis. These spectral
features were used to test the hypothesis that the EEG dominant frequencies slowdown in MCI in comparison with 10 agematch control subjects. Nineteen channels were recorded during working memory and were grouped into 5 recording regions corresponding to scalp areas of the cerebral cortex. EEG
artifacts were removed using wavelet analysis (WT). The SpecEn analysis of the EEG data suggested a broad and flat spectrum in the normal EEG. The relative powers (RP) in delta
(δRP), theta (θRP), alpha (αRP), beta (βRP), and gamma (γRP)
were calculated. SpecEn was significantly lower in strokerelated MCI patients at parietal, occipital and central regions
(p-value < 0.05, Student’s t-test). Moreover, the other significant differences can be observed in increasing the δRP, θRP
and γRP and decreasing the αRP and βRP of the stroke-related
MCI group in all regions (p-value < 0.05, Student’s t-test). It
can be concluded that the SpecEn and spectral analysis are
useful tool to inspect the slowing in the EEG signals in poststroke MCI patients’ and the healthy controls’ EEG.
Keywords— Electroencephalography, Relative power, Spectral entropy, Wavelet, Mild cognitive impairment
I. INTRODUCTION
Cognitive and working memory impairment are common
after stroke. 30% of stroke patients are prone to develop
vascular dementia (VaD) within the first year of stroke
onset. VaD is the second case of dementia after Alzheimer's
disease (AD), between 1% and 4% of elderly people age of
65 years are suffer from VaD and the prevalence will be
double every 5-10 years after this age [1]. Clinically, mild
cognitive impairment (MCI) is defined as a decline in cognitive function greater than expected with respect to the
individual’s age and education level, but that does not interfere notably with the activities of daily life [2]. Traditional-
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ly, it is considered as a stage between early normal brain
cognition and late severe dementia. Attention and executive
function are the most affected domains due to vascular
lesion that results from ischemic and hemorrhagic stroke [3,
4].
For several decades, EEG has been considered an effective physiological technique which reflects the hidden cortical abnormalities by providing a quantitative insight to
diagnose or evaluate potential predictors of dementia severity [5] . In the last two decades, several attempts have been
made to quantify the EEG activity using computerized signal processing and analysis techniques in order to interpret
the degree of EEG abnormality and dementia [6]. Typically,
the clinical EEG wave forms have an amplitude around 10100 µv and frequency range of 1 to 100 Hz. EEG can be
classified into five frequency bands: Delta waves (δ), Theta
waves (θ), Alpha waves (α), Beta waves (β), and Gamma
waves (γ) [7].
However, the EEG is affected by non-cerebral sources
called artifacts that may mimic the brain pathological activity and therefore influence the analysis. Many artifacts can
have a physiological origin, like muscle activity, pulse and
eye blinking. Others are non-physiological, such as power
line interference. Numerous methods have been used to deal
with artifacts that affect the EEG recordings. Wavelet (WT)
is a time-frequency analysis that used to denoise the nonstationary bio-signals such as EEG [8]. Researchers have
used WT in different ways. For instance, WT has been used
to detect epileptic spike signals and to predict the changes in
patients with epilepsy and to separate burst in ECG waves.
Moreover, WT has been used to remove ocular artifacts and
tonic components for electromyography (EMG) signals.
Furthermore, WT can be an efficient technique to extracted
features from the EEG sub-bands as wavelet decomposition
[9].
In this paper, WT has been used as a pre-processing step
to denoise the EEG datasets. The spectral entropy (SpecEn)
and spectral analysis were extracted to examine the EEG
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background activity in MCI patients and control healthy
subjects.
II.
the band of the recorded EEG signals. The general block
diagram of our proposed system is shown in Figure 1.
METHODS AND MATERIALS
A. Subjects and EEG recording procedure
EEG datasets were recorded for ten healthy control subjects, aged 57.9 ± 5.7 years, and ten MCI patients, aged 58.2
± 8.7 years (mean ± standard deviation, SD). The patients
were recruited from the stroke ward in Pusat Perubatan
Universiti Kebangsaan Malaysia (PPUKM), the Medical
center of National University of Malaysia, Malaysia. The
post-stroke patient satisfied the National Institutes of Health
Stroke Scale (NIHSS) [10]. The healthy control had no
previous history of mental and neurological abnormalities.
Both groups were underwent cognitive evaluation including
Mini-Mental State Examination (MMSE), [11] and Montreal Cognitive Assessment (MoCA) [12], (stroke patients
MMSE 20.5±6.3, MoCA 16.35±6.9; normal control MMSE
29.6±0.7, MoCA 29.06±0.8, mean ± SD). All experiment
protocols were approved by the Human Ethics Committee
of the National University of Malaysia. An information
consent forms were also signed by the participants. The
EEG activity was recorded using the NicoletOne systems
(V32), VIASYS Healthcare Inc., USA. A total of 19 electrodes, plus the ground and system reference electrodes,
were positioned according to the 10-20 international system
(Fp1, Fp2, F7, F3, Fz, F4, F8, T3, T5, T4, T6, P3, Pz, P4,
C3, Cz, C4, O1, and O2). NicoLetOne EEG system is sampled at 256 Hz sampling frequency, impedance of electrode/skin was below 10 kOhms, sensitivity of 100 µv/cm,
low cut of 0.5 Hz and high cut of 70 Hz using referential
montage. The EEG was recorded for 60 seconds during
working memory task. Patients were asked to memorize
five words for 10 seconds [3]. Then, each patient was asked
to remember the five words while the EEG was recorded
with the eyes closed. After 1 min they were asked to open
the eyes and enumerate the five words they could remember. The 19 channels from the EEG datasets of the 10
healthy and the 10 stroke patients were grouped into 5 recording regions corresponding to the scalp area of the cerebral cortex. These are the frontal region (seven channels:
Fp1, Fp2, F3, F4, F7, F8 and Fz), the temporal region (four
channels: T3, T4, T5 and T6), the parietal region (three
channels: (P3, P4 and Pz), the occipital region (two channels: O1 and O2), and central region (three channels: C3,
C4 and Cz). Conventional filtering were used to process the
19 channels EEG data. Notch filter at (50 Hz) has been used
to remove the power line interference noise and a band pass
filter of (0.5-64 Hz) frequency range has been used to limit
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Fig. 1 The block diagram of the proposed method
Table I The EEG signal decomposition into five frequency bands
Decomposition
levels
Decomposed
Signals
1
2
3
4
5
5
D1
D2
D3
D4
D5
A5
EEG bands
Noises
Gamma
Beta
Alpha
Theta
Delta
Frequency bands
(Hz)
64-128
32-64
16-32
8-16
4-8
0-4
B. Wavelet Analysis
Wavelet transform is a popular denoising technique. It
has been introduced to process the non-stationary signals, as
EEG and EMG. The mathematical equation of the discrete
wavelet transform (DWT) can be processed by obtaining the
discrete value of the parameters a and b as in (1).
𝐷𝑊𝑇𝑚,𝑛 (𝑓) = 𝑎0
−𝑚⁄
2
∫ 𝑓(𝑡) 𝜓(𝑎0−𝑚 𝑡 − 𝑛𝑏0 )𝑑𝑡 (1)
Where a0 and b0 values are set to 2 and 1, respectively.
Where 𝜓(𝑡) is the mother wavelet (MWT) function which is
shifted by the location parameter (𝑏) and dilated or contracted by scaling parameter (𝑎), as in 2
𝜓𝑎,𝑏 (𝑡) =
1
√𝑎
𝜓(
𝑡−𝑏
𝑎
) , 𝑎𝜖𝑅 + , 𝑏𝜖𝑅
(2)
Discrete wavelet transform (DWT) was used for denoising purposes, the symlets orthogonal MWT family of
order 9 ‘sym9’ was used due to its high compatibility with
the recorded EEG datasets which produce the best denoising
results [13]. In this study, five decomposition levels were
chosen to decompose the EEG signals, since the sampling
frequency used in this study was 256 Hz. The decomposition
coefficients represented the frequency content from the
EEG signal. The SURE threshold, is an adaptive soft
thresholding method, which is finding the threshold limit
for each level based on Stein’s unbiased risk estimation [14]
and commonly used value in [13, 15, 16]. Once the
thresholded coefficients have been extracted from each level,
the effects of the noises on the EEG signals are removed.
Finally, the signals at each level have to be reconstructed
using inverse discrete wavelet transform (IDWT). The first
reconstructed details D1 is considered to be mainly
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the noise components of the EEG signal, the four reconstruction details of the sub-band signals D2–D5 and the
reconstruction approximation of the sub-band signal A5
yielded signal information related to each EEG frequency
band as shown in Table I. These bands provided a compact
representation of the EEG signal and they were used to extract the EEG spectral features.
C. Feature Extraction
Spectral analysis has been used extensively to detect abnormalities in the spectra of dementia patients’ EEGs. Spectral entropy (SpecEn) measures the flatness of the signal
spectrum and it considered as a convenient way that suitable
in quantify slowing in frequency due to dementia. In the
present work, to quantify EEG changes, SpecEn and the
relative power (RP) in delta (δRP), theta (θRP), alpha (αRP),
beta (βRP), and gamma (γRP) were calculated to the WT
decomposed signals to distinguish stroke-related MCI patients EEGs’ from the normal age-match healthy subjects. In
order to estimate the SpecEn, the PSD was normalized to a
scale from 0 to 1 to get normalized PSD (PSDn ) so that
∑ PSDn (f) = 1, afterwards, SpecEn is computed applying
the Shannon's entropy to the PSDn as shown in 3 [17]:
64 Hz
−1
𝑆𝑝𝑒𝑐𝐸𝑛 =
∑ log[PSDn (f)]
log(N)
MCI patients than the control subjects at parietal, occipital
and central regions are found, achieving significant differences (p-value < 0.05). The spectral analysis of the RP
showed significant increases in δRP, θRP and γRP activities
for the MCI patients in all regions (p-value < 0.05). It can
also be observed the decrease in both αRP and βRP activities
in MCI patients significantly in all regions of the MCI patients (p-value < 0.05). Our findings agreed other studies.
For instance, Klimesch described the changes in the brain
activity which are strongly associated with cognitive and
attentional working memory performance as decreasing in
both alpha and beta but increasing in both delta and theta in
[18]. Gevins et al. attributed the changes during working
memory task to alpha and theta. Finally, Lundqvist et al.
correlated the changes in brain activity to encoding one or
more items in WM and these changes have associated with
increase in theta and gamma and decrease in alpha and beta
power [19, 20].
Table II The average values (Mean ± SD) of EEGs for the MCI patients and the control subjects for all the five scalp regions. Significant
group differences are marked with an asterisk
Features
(3)
f=0.5Hz
The RP for each selected frequency band δ, θ, α, β, and γ
can be calculated using equation 4
𝑅𝑃(%) =
∑ 𝑆𝑒𝑙𝑒𝑐𝑡𝑒𝑑 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑟𝑎𝑛𝑔𝑒
∑ 𝑇𝑜𝑡𝑎𝑙 𝑟𝑎𝑛𝑔𝑒 (0.5 − 64 𝐻𝑧)
SpecEn
(4)
δRP
D. Statistical Analysis
Normality was assessed with Kolmogrov-Smirnov test,
whereas homoscedasticity was verified with Levene’s test.
Therefore, the student’s t-test was applied to compare between the features of the two groups of MCI patients and
control subjects using SPSS 22. These comparisons were
done for each feature according to regions separately between the two groups. First of all, the statistical difference
between the spectral features of the stroke-related MCI and
healthy subjects were evaluated. Second, the EEG bands RP
for the two groups were assessed for each band separately.
Differences were considered statistically significant if the pvalue was lower than 0.05.
θRP
αRP
βRP
γRP
III.
RESULTS AND DISCUSSION
In Table II, the SpecEn and the RP values for the MCI patients and age-match control subjects in the five scalp regions are given. It is evident that lower SpecEn values in the
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Regions
Frontal
Temporal
Parietal
Occipital
Central
Frontal
Temporal
Parietal
Occipital
Central
Frontal
Temporal
Parietal
Occipital
Central
Frontal
Temporal
Parietal
Occipital
Central
Frontal
Temporal
Parietal
Occipital
Central
Frontal
Temporal
Parietal
Occipital
Central
MCI
(Mean ± SD)
0.75±0.09
0.767±0.088
0.764±0.083
0.74±0.089
0.787±0.084
0.51±0.218
0.424±0.232
0.42±0.216
0.426±0.249
0.378±0.229
0.125±0.067
0.147±0.088
0.14±0.077
0.172±0.093
0.148±0.083
0.145±0.098
0.193±0.121
0.216±0.139
0.221±0.151
0.198±0.103
0.107±0.068
0.118±0.074
0.117±0.075
0.095±0.055
0.135±0.084
0.122±0.066
0.123±0.059
0.107±0.102
0.085±0.067
0.141±0.13
Control
(Mean ± SD)
0.778±0.082
0.798±0.065
0.789±0.035
0.759±0.047
0.807±0.038
0.438±0.185
0.359±0.17
0.341±0.113
0.274±0.145
0.325±0.126
0.102±0.035
0.11±0.03
0.114±0.037
0.106±0.044
0.124±0.034
0.201±0.108
0.252±0.128
0.306±0.109
0.417±0.174
0.27±0.102
0.137±0.055
0.157±0.044
0.141±0.028
0.124±0.033
0.165±0.04
0.114±0.093
0.118±0.108
0.099±0.05
0.08±0.048
0.116±0.053
p-value
0.203
0.101
0.002*
0.046*
0.001*
0.045*
0.024*
0.001*
0.02*
0.001*
0.05*
0.05*
0.05*
0.05*
0.05*
0.408
0.779
0.179
0.324
0.847
0.034*
0.006*
0.001*
0.03*
0.002*
0.035*
0.026*
0.031*
0.102
0.011*
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IV. CONCLUSIONS
In the current study ‘sym9’ MWT basis function has been
used as a pre-processing to denoise the EEG datasets of both
control subject and the post-stroke patient using SURE
thresholding method. Nineteen channels from different regions on the scalp were recorded during working memory
and were grouped into 5 recording regions corresponding to
the scalp area of the cerebral cortex. Spectral analysis has
been used to detect abnormalities in the spectra of strokerelated MCI patients EEGs’. The SpecEn and the relative
powers of both low and high frequencies reflected the slowing in the electrical brain activity in MCI patients which
results in shifting their power spectrum profiles. It can be
noticed an increase in δRP, θRP and γRP activities for the
MCI patients in all regions and decrease in both αRP and
βRP activities in MCI patients in all regions. As the EEG has
been widely used as a potential screening technique in clinical practice due to its low cost and portability, it could become a reference in planning and customizing an optimal
therapeutic program to address the changes associated with
MCI and dementia. This study suggests that the spectral
analysis of EEG background activity in stroke-related MCI
patients using SpecEn and the relative powers might be helpful in providing useful diagnoses indexes using EEG.
CONFLICT OF INTEREST
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
The authors report no conflicts of interest in this work.
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Author: Noor Kamal Al-Qazzaz
Institute: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
Street: Bangi
City: Selangor 43600
Country: Malaysia
Email: noorbmemsc@gmail.com