Detection of Alzheimer’s Disease Electroencephalogram Temporal Events

2013 ◽  
Vol 2 (3) ◽  
pp. 44-61 ◽  
Author(s):  
Pedro Miguel Rodrigues ◽  
Diamantino Rui Freitas ◽  
João Paulo Teixeira
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
The Elderly ◽  
Clinical Settings ◽  
Brain Disorder ◽  
New Approach ◽  
Self Organized ◽  
Available Technology ◽  
Temporal Events ◽  
Electroencephalogram Eeg

Alzheimer’s Disease (AD) is a chronic progressive and irreversible neurodegenerative brain disorder. The aging population has been increasing significantly in recent decades. Therefore, AD will continue to increase because the disease affects mainly the elderly. Its diagnostic accuracy is relatively low, and there is not a biomarker able to detect AD without invasive tests. The electroencephalogram (EEG) test is a widely available technology in clinical settings. It may help diagnosis of brain disorders, once it can be used in patients who have cognitive impairment involving a general decrease in overall brain function or in patients with a located deficit. This study is a new approach to detect EEG temporal events in order to improve the AD diagnosis. For that, K-means and Self-Organized Maps were used, and the results suggested that there are sequences of EEG energy variation that appear more frequently in AD patients than in healthy subjects.

Alzheimer's Electroencephalogram Event Scalp and Source Localization

2016 ◽  
pp. 33-49
Author(s):  
Pedro Miguel Rodrigues ◽  
João Paulo Teixeira ◽  
Diamantino R. S. Freitas
Keyword(s):  
Source Localization ◽  
The Elderly ◽  
Clinical Settings ◽  
New Approach ◽  
Brain Anomalies ◽  
Available Technology ◽  
Temporal Events ◽  
Near Future ◽  
Electroencephalogram Eeg ◽  
Irreversible Impairment

Alzheimer's disease is the most common cause of dementia which causes a progressive and irreversible impairment of several cognitive functions. The aging population has been increasing significantly in recent decades and this disease affects mainly the elderly. Its diagnostic accuracy is relatively low and there is not a biomarker able to detect AD without invasive tests. Despite the progress in better understanding the disease there remains no prospect of cure at least in the near future. The electroencephalogram (EEG) test is a widely available technology in clinical settings. It may help diagnosis of brain disorders, once it can be used in patients who have cognitive impairment involving a general decrease in overall brain function or in patients with a located deficit. This study is a new approach to improve the scalp localization and the detection of brain anomalies (EEG temporal events) sources associated with AD by using the EEG.

Early Detection of Electroencephalogram Temporal Events in Alzheimer's Disease

2017 ◽  
pp. 112-131
Author(s):  
Pedro Miguel Rodrigues ◽  
Diamantino Freitas ◽  
João Paulo Teixeira ◽  
Dílio Alves ◽  
Carolina Garrett
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Study Groups ◽  
Propagation Time ◽  
Cognitive Changes ◽  
Self Organized ◽  
High Incidence ◽  
Temporal Events ◽  
Electroencephalogram Eeg ◽  
The Brain

Alzheimer's Disease (AD) is considered one of the most debilitating illness in modern societies and the leading cause of dementia. This study is a new approach to detect early AD Electroencephalogram (EEG) temporal events in order to improve early AD diagnosis. For that, Self-Organized Maps (SOM) were used, and it was found that there are sequences of EEG energy variation, characteristic of AD, that appear with high incidence in Mild Cognitive Impairment (MCI) patients. Those AD events are related to the first cognitive changes in patients that interfered with the normal EEG signal pattern. Moreover, there are significant differences concerning the propagation time of those events between the study groups(p=0.0082<0.05), meaning that, as AD progresses the brain dynamics are progressively affected, what is expected because AD causes brain atrophy.

Early Detection of Electroencephalogram Temporal Events in Alzheimer's Disease

2021 ◽  
pp. 245-266
Author(s):  
Pedro Miguel Rodrigues ◽  
Diamantino Freitas ◽  
João Paulo Teixeira ◽  
Dílio Alves ◽  
Carolina Garrett
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Study Groups ◽  
Propagation Time ◽  
Cognitive Changes ◽  
Self Organized ◽  
High Incidence ◽  
Temporal Events ◽  
Electroencephalogram Eeg ◽  
The Brain

Alzheimer's Disease (AD) is considered one of the most debilitating illness in modern societies and the leading cause of dementia. This study is a new approach to detect early AD Electroencephalogram (EEG) temporal events in order to improve early AD diagnosis. For that, Self-Organized Maps (SOM) were used, and it was found that there are sequences of EEG energy variation, characteristic of AD, that appear with high incidence in Mild Cognitive Impairment (MCI) patients. Those AD events are related to the first cognitive changes in patients that interfered with the normal EEG signal pattern. Moreover, there are significant differences concerning the propagation time of those events between the study groups(p=0.0082<0.05), meaning that, as AD progresses the brain dynamics are progressively affected, what is expected because AD causes brain atrophy.

Preventing Alzheimer's Wandering

2019 ◽  
pp. 77-89
Author(s):  
Sara Paiva ◽  
Rui Peleja ◽  
Jorge Cunha ◽  
Carlos Abreu
Keyword(s):  
Quality Of Life ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quality Of Care ◽  
Cognitive Impairments ◽  
The Elderly ◽  
Strong Impact ◽  
Brain Disorder ◽  
Age Related

With increased life expectancy, the incidence of age-related cognitive impairments, faced by the elderly and older generations, is growing. Among the population with cognitive impairments, those that suffer from Alzheimer's disease are the most common. The Alzheimer's disease is a chronic degenerative brain disorder that is characterised by a failure of memory and, in some instances, by disorders in language, perception and planning. As a consequence of the progressive damages imposed by the illness, patients will increasingly seek and need assistance. This paper presents a tool to aid the development and managing of caregiving communities, comprising immediate family members, relatives, neighbours and healthcare professionals, to assist patients with Alzheimer's disease. Such communities could have a strong impact on the quality of care provided to the patients. At the same time, it is hoped that involving communities will significantly improve the quality of life of Alzheimer's patients and their families while reducing the costs related to the care provided.

scholarly journals Electrophysiological Biomarkers of Epileptogenicity in Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Tingting Yu ◽  
Xiao Liu ◽  
Jianping Wu ◽  
Qun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Frequency ◽  
Cortical Network ◽  
Accurate Identification ◽  
New Approach ◽  
Epileptiform Discharges ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg

Cortical network hyperexcitability is an inextricable feature of Alzheimer’s disease (AD) that also might accelerate its progression. Seizures are reported in 10–22% of patients with AD, and subclinical epileptiform abnormalities have been identified in 21–42% of patients with AD without seizures. Accurate identification of hyperexcitability and appropriate intervention to slow the compromise of cognitive functions of AD might open up a new approach to treatment. Based on the results of several studies, epileptiform discharges, especially those with specific features (including high frequency, robust morphology, right temporal location, and occurrence during awake or rapid eye movement states), frequent small sharp spikes (SSSs), temporal intermittent rhythmic delta activities (TIRDAs), and paroxysmal slow wave events (PSWEs) recorded in long-term scalp electroencephalogram (EEG) provide sufficient sensitivity and specificity in detecting cortical network hyperexcitability and epileptogenicity of AD. In addition, magnetoencephalogram (MEG), foramen ovale (FO) electrodes, and computational approaches help to find subclinical seizures that are invisible on scalp EEGs. We performed a comprehensive analysis of the aforementioned electrophysiological biomarkers of AD-related seizures.

Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection

2021 ◽  
pp. 224-244
Author(s):  
Pedro Miguel Rodrigues ◽  
Diamantino Rui Freitas ◽  
João Paulo Teixeira ◽  
Dílio Alves ◽  
Carolina Garrett
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Artificial Neural Networks ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
The Elderly ◽  
Good Tool ◽  
Electroencephalogram Eeg ◽  
State Of Art

The World's health systems are now facing a global problem known as Alzheimer's disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are promising concerning AD early diagnosis and they show that EEG can be a good tool for AD detection (Controls (C) vs AD: accuracy 95%; C vs Mild-cognitive Impairment (MCI): accuracy 77%; MCI vs AD: accuracy 83%; All vs All: accuracy 90%).

Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection

2018 ◽  
Vol 7 (1) ◽  
pp. 40-59
Author(s):  
Pedro Miguel Rodrigues ◽  
Diamantino Rui Freitas ◽  
João Paulo Teixeira ◽  
Dílio Alves ◽  
Carolina Garrett
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Signal Analysis ◽  
The Elderly ◽  
Eeg Signals ◽  
Discrimination Accuracy ◽  
Good Tool ◽  
Electroencephalogram Eeg ◽  
State Of Art

The World's health systems are now facing a global problem known as Alzheimer's disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are promising concerning AD early diagnosis and they show that EEG can be a good tool for AD detection (Controls (C) vs AD: accuracy 95%; C vs Mild-cognitive Impairment (MCI): accuracy 77%; MCI vs AD: accuracy 83%; All vs All: accuracy 90%).

Preventing Alzheimer’s Wandering

2013 ◽  
Vol 8 (4) ◽  
pp. 32-43 ◽  
Author(s):  
Sara Paiva ◽  
Rui Peleja ◽  
Jorge Cunha ◽  
Carlos Abreu
Keyword(s):  
Quality Of Life ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthcare Professionals ◽  
Cognitive Impairments ◽  
The Elderly ◽  
Strong Impact ◽  
Brain Disorder ◽  
Age Related

With increased life expectancy, the incidence of age-related cognitive impairments, faced by the elderly and older generations, is growing. Among the population with cognitive impairments, those that suffer from Alzheimer’s disease are the most common. The Alzheimer’s disease is a chronic degenerative brain disorder that is characterised by a failure of memory and, in some instances, by disorders in language, perception and planning. As a consequence of the progressive damages imposed by the illness, patients will increasingly seek and need assistance. This paper presents a tool to aid the development and managing of caregiving communities, comprising immediate family members, relatives, neighbours and healthcare professionals, to assist patients with Alzheimer’s disease. Such communities could have a strong impact on the quality of care provided to the patients. At the same time, it is hoped that involving communities will significantly improve the quality of life of Alzheimer's patients and their families while reducing the costs related to the care provided.

scholarly journals Prospects of Marine Sterols against Pathobiology of Alzheimer’s Disease: Pharmacological Insights and Technological Advances

Marine Drugs ◽  
2021 ◽  
Vol 19 (3) ◽  
pp. 167
Author(s):  
Md. Ataur Rahman ◽  
Raju Dash ◽  
Abdullah Al Mamun Sohag ◽  
Mahboob Alam ◽  
Hyewhon Rhim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Antioxidant Properties ◽  
Antioxidant Defense System ◽  
The Elderly ◽  
Cellular Systems ◽  
Cholesterol Homeostasis ◽  
Brain Disorder ◽  
Technological Advances ◽  
Anti Cancer

Alzheimer’s disease (AD) is a degenerative brain disorder characterized by a progressive decline in memory and cognition, mostly affecting the elderly. Numerous functional bioactives have been reported in marine organisms, and anti-Alzheimer’s agents derived from marine resources have gained attention as a promising approach to treat AD pathogenesis. Marine sterols have been investigated for several health benefits, including anti-cancer, anti-obesity, anti-diabetes, anti-aging, and anti-Alzheimer’s activities, owing to their anti-inflammatory and antioxidant properties. Marine sterols interact with various proteins and enzymes participating via diverse cellular systems such as apoptosis, the antioxidant defense system, immune response, and cholesterol homeostasis. Here, we briefly overview the potential of marine sterols against the pathology of AD and provide an insight into their pharmacological mechanisms. We also highlight technological advances that may lead to the potential application of marine sterols in the prevention and therapy of AD.

Effect Of Nhexane Extract Of Caryota No Seed On Markers Of Neurodegeneration And Fecundity In Drosophila Melanogaster

2020 ◽  
Vol 6 (5) ◽  
pp. 1-7
Author(s):  
Chinonye A Maduagwuna ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drosophila Melanogaster ◽  
Neurodegenerative Diseases ◽  
Cognitive Functions ◽  
The Elderly ◽  
Common Cause ◽  
Chronic Neuroinflammation

Study background: Chronic neuroinflammation is a common emerging hallmark of several neurodegenerative diseases. Alzheimer’s Disease (AD) is the most common cause of dementia among the elderly and is characterized by loss of memory and other cognitive functions.

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