Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin Mohd; Ahmad, Siti Anom; Escudero, Javier

doi:10.1109/icabme.2017.8167557

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…It represents the central and temporal lobe regions. Our findings confirm the study by Al-Qazzaz et al [28,29], which investigated the signal complexity in stroke patients related cognitive impairment, showing that there was a significant reduction in signal in the central and temporal regions. We presumed that stroke-related dementia patients have damaged a number of neurons and synapses in these regions.…”

Section: Discussionsupporting

confidence: 92%

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Hemispheric Coherence Analysis of Wide Band EEG Signals for Characterization of Post-Stroke Patients with Dementia

Hadiyoso¹,

Zakaria²,

Ong³

et al. 2021

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Post-stroke dementia (PSD) is a type of vascular dementia (VaD) that might be occurred in post-stroke patients. Memory, language and behavior tests can be used for the analysis of cognitive impairment caused by PSD. Often a supporting clinical examination such as an electroencephalogram (EEG) is used to support the diagnosis or analyze the characteristic changes that occur in the brain. Conventional analysis or visual inspection of EEG signals can be very difficult, since the nature of the signal tends to be non-stationer. Therefore, this study proposes a quantitative analysis for the characterization of EEG signals in stroke survivors with dementia. It is thought that it has different characteristics with the normal subject so that this study can be used as a reference in supporting dementia detection in post-stroke survivors. The quantitative analysis used in this study is coherence analysis. Coherence analysis was performed on EEG signals recorded from six poststroke patients with dementia and then compared with ten normal healthy subjects. Analysis of coherence between brain areas includes inter and intra-hemispheric coherence. Validation was carried out by using the independent t-test where the confidence level was 95%, indicating that the p-value <0.05 had a significant difference. The test results show that in general the coherence of the electrode pairs in patients with dementia is lower than in the normal healthy group. It is notably, i) In interhemispheric, the C3-C4, T3-T4, and T5-T6 pairs generate significant differences, ii) the highest decrease in intrahemispheric coherence was found in C3-T5 with p = 0.0005. The coherence study presented in this paper is expected to be used for early detection of PSD in the future.

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Section: Discussionsupporting

confidence: 92%

“…Since the risk factor of post-stroke dementia is very high, the supporting criteria for detection of the disease are crucial. Recently, in 2014-2018, Al-Qazzaz et al [28][29][30]. observed EEG signals in patients with vascular dementia and stroke-related patients with mild cognitive impairment (MCI).…”

Section: Related Workmentioning

confidence: 99%

Hemispheric Coherence Analysis of Wide Band EEG Signals for Characterization of Post-Stroke Patients with Dementia

Hadiyoso¹,

Zakaria²,

Ong³

et al. 2021

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show abstract

“…To overcome these cons, many automatic methods have been used such as linear time-frequency analysis and non-linear such as largest Laypunov exponent (LLE) and correlation dimension (CD) [12,13]. Furthermore, several types of EEG measurements have been used due to complexity of the brain and the signal such as approximate entropy (AE) [14], Kolmogorov entropy [15], sample entropy [16,17]. In addition, many available system today such as long term epilepsy, cEEG Intensive care unit (ICU) monitoring and sleep testing for the most flexible and comprehensive recording system and long legacy.…”

Section: Introductionmentioning

confidence: 99%

Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

Jaafer¹

2021

alkej

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The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this disease in children. Generally, this study aims to provide an evident of a reliable biomarker to detect epilepsy in children to give them an additional chance to live a high quality life.

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“…Features like the correlation dimension, Lyapunov exponent [17,18], approximate entropy [19], Hurst exponent, Hjorth Parameters [20] and Fractal dimension [21] have been widely applied to investigate EEG signals. Moreover, spectrum analysis has been utilized to identified any EEG-signal-related anomalies through the investigation of the EEG frequency bands [15,22].…”

Section: Introductionmentioning

confidence: 99%

Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

Al-Qazzaz

Sabir

Ali

et al. 2021

IJIE

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Identifying gender has become essential specially to support automatic human-computer interface applications and to customize interactions based on affective responses. The electroencephalogram (EEG) has been adopted for recording the neuronal information as waveforms from the scalp. The objective of this study was twofold. First, to identify genders from four different emotional states using spectral relative power biomarkers. Second, to develop Spectro-spatial profiles that afford additional information for gender identification using emotional-based EEGs. The dataset has been collected from ten healthful volunteer students from the University of Vienna while watching short emotional audio-visual clips of angry, happiness, sadness, and neutral emotions. Wavelet (WT) has been used as a denoising technique, the spectral relative power features of delta ( ), theta ( ), alpha ( ), beta ( ) and gamma ( ) were extracted from each recorded EEG channel. In the subsequent steps, analysis of variance (ANOVA) and Pearson’s correlation analysis were performed to characterize the emotional-based EEG biomarkers towards developing the Spectro-spatial profile to identify gender differences. The results show that the spectral set of features may provide and convey reliable biomarkers for identifying Spectro-spatial profiles from four different emotional states. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects and as an intervention on the brain. The results revealed that almost high relative powers from all emotional states appear in females compared to males. Particularly, was the most prominent for anger, and were widely observed in happiness, was the most appears in sadness, and were the powers that appears widely in neutral. Moreover, in females, neut was correlated with and _ang, _neut was mostly correlated with _ang. Besides, _neut was correlated with _ang, _neut was correlated with _ang, _neut was mostly correlated with _sad. Moreover, in males, _neut showed a very strong correlation with _sadness whereas _neut was correlated with _hap and _neut wa s correlated with _hap. Therefore, the proposed system using the WT denoising method, spectral relative power markers, and the spectro-spatial profile plays a crucial role in characterizing the emotional-based EEGs towards gender identification. The classification results were 89.46% for SVM and 90% for the KNN. Therefore, the proposed system using the WT denoising method, spectral relative powers features, SVM, and KNN classifiers were crucial in gender identification and characterizing the emotional EEG signals.

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Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

Cited by 13 publications

References 23 publications

Hemispheric Coherence Analysis of Wide Band EEG Signals for Characterization of Post-Stroke Patients with Dementia

Hemispheric Coherence Analysis of Wide Band EEG Signals for Characterization of Post-Stroke Patients with Dementia

Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

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