EEG-Based Diagnosis of Alzheimer's Disease Using Kolmogorov Complexity

Puri, Digambar; Nalbalwar, Sanjay L.; Nandgaonkar, Anil; Wagh, Abhay

doi:10.1007/978-981-16-2008-9_15

Cited by 26 publications

(11 citation statements)

References 11 publications

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“…Kolmogorov Complexity, on the other hand, examines the shortest algorithmic length of a string 41 . In our analysis, it's used to characterize the minimum representation length of an EEG signal, with higher values indicating more intricate activity 42,43 . To compute Kolmogorov complexity, we used a threshold at 0 for binarization, enabling an effective evaluation of complexity metrics in the EEG data.…”

Section: Linear Analysis In Time Sequencementioning

confidence: 99%

EEG Signatures of Cognitive Decline after Mild SARS- CoV-2 Infection: An Age-Dependent Study

Sun,

Chen

et al. 2024

Preprint

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Background and Objectives: Current research on the neurological impact of SARS-CoV-2 primarily focuses on the elderly or severely ill individuals. This study aims to explore the diverse neurological consequences of SARS-CoV-2 infection, with a particular focus on mildly affected children and adolescents. Methods A cohort study was conducted to collect pre- and post-infection resting-state EEG data from 185 participants and 181 structured questionnaires of long-term symptoms across four distinct age groups. The goal was to comprehensively evaluate the impact of SARS-CoV-2 infection on these different age demographics. The study analyzed EEG changes of SARS-CoV-2 by potential biomarkers across age groups using both spatial and temporal approaches. Results Spatial analysis indicated that children and adolescents exhibit smaller changes in brain network and microstate patterns post-infection, implying a milder cognitive impact. Sequential linear analyses showed that SARS-CoV-2 infection is associated with a marked rise in low-complexity, synchronized neural activity within low-frequency EEG bands. This is evidenced by a significant increase in Hjorth Activity within the theta band and Hjorth Mobility in the delta band. Sequential nonlinear analysis indicated a significant reduction in the Hurst exponent across all age groups, pointing to increased chaos and complexity within the cognitive system following infection. Furthermore, linear regression analysis based on questionnaires established a significant positive relationship between the magnitude of changes in these neural indicators and the persistence of long-term symptoms post-infection. Discussion The findings underscore the enduring neurological impacts of SARS-CoV-2 infection, marked by cognitive decline and increased EEG disarray. Although children and adolescents experienced milder effects, cognitive decline and heightened low-frequency electrical activity were evident. These observations might contribute to understanding potential anxiety, insomnia, and neurodevelopmental implications.

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Section: Linear Analysis In Time Sequencementioning

confidence: 99%

EEG Signatures of Cognitive Decline after Mild SARS- CoV-2 Infection: An Age-Dependent Study

Sun,

Chen

et al. 2024

Preprint

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“…The local ethical committee approved this process of EEG recording of Hospital Clinic Universitario de Valladolid (CUV) [17]. This EEG data of AD and NC already used in the [17], [18]. All EEG signals recorded using 16 electrodes are used in the proposed study.…”

Section: Eeg Datasetmentioning

confidence: 99%

Alzheimer’s disease detection from optimal EEG channels and Tunable Q-Wavelet Transform

Puri¹,

Nalbalwar²,

Nandgaonkar³

et al. 2022

IJEECS

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Alzheimer’s disease (AD) is a non-curable neuro-degenerative disorder that has no cure to date. However, it can be delayed through daily activity assessment using a robust Electroencephalogram (EEG) based system at an early stage. A selection tech- nique using a Shannon entropy to signal energy ratio is proposed to select optimal EEG channels for AD detection. A threshold for channel selection is calculated using the best detection accuracy during backward elimination. The selected EEG channels are decomposed using Tunable Q-wavelet transform (TQWT) into nine different sub- bands (SBs). Four features: Katz’s fractal dimension, Tsallis entropy, Relyi’s entropy, and kurtosis are extracted for each SB. These features are used to train and test sup- port vector machine, k-nearest neighbor, Ensemble bagged tree (EBT), decision tree, and neural network for detecting AD patients from normal subjects. 16-channel EEG signals from 12 AD and 11 normal subjects recorded using the 10-20 electrode place- ment method are used for evaluation. Ten optimized channels are selected, resulting in 32.5% compression. The experimental results of the proposed method showed promis- ing classification accuracy of 96.20% with the seventh SB features and EBT classifier. The significance of these features was inspected by using the Kruskal-Wallis test.

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“…Moreover, the imaging and signal analysis system has been introduced in the digital field to analyze the AD severity range with the help of artificial intelligence (AI) [ 14 , 15 ]. Hence, several neural models and classifiers were implemented to find the AD severity rate in the early stages without the requirement of high resource usage [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer’s Disease Using EEG Signal

Swarnalatha

2023

Computational Intelligence and Neuroscience

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Recent researchers have been drawn to the analysis of electroencephalogram (EEG) signals in order to confirm the disease and severity range by viewing the EEG signal which has complicated the dataset. The conventional models such as machine learning, classifiers, and other mathematical models achieved the lowest classification score. The current study proposes to implement a novel deep feature with the best solution for EEG signal analysis and severity specification. A greedy sandpiper-based recurrent neural system (SbRNS) model for predicting Alzheimer’s disease (AD) severity has been proposed. The filtered data are used as input for the feature analysis and the severity range is divided into three classes: low, medium, and high. The designed approach was then implemented in the matrix laboratory (MATLAB) system, and the effectiveness score was calculated using key metrics such as precision, recall, specificity, accuracy, and misclassification score. The validation results show that the proposed scheme achieved the best classification outcome.

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EEG-Based Diagnosis of Alzheimer's Disease Using Kolmogorov Complexity

Cited by 26 publications

References 11 publications

EEG Signatures of Cognitive Decline after Mild SARS- CoV-2 Infection: An Age-Dependent Study

EEG Signatures of Cognitive Decline after Mild SARS- CoV-2 Infection: An Age-Dependent Study

Alzheimer’s disease detection from optimal EEG channels and Tunable Q-Wavelet Transform

A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer’s Disease Using EEG Signal

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