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%).

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%).

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 Disease Recognition with Artificial Neural Networks

2013 ◽  
pp. 102-118 ◽  
Author(s):  
Pedro Miguel Rodrigues ◽  
João Paulo Teixeira
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Eeg Signals ◽  
Intellectual Abilities ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Short Time ◽  
And Control ◽  
Electroencephalogram Eeg

Alzheimer’s Disease (AD) is the most common cause of dementia, and is well known for its affect on memory loss and other intellectual abilities. The Electroencephalogram (EEG) has been used as a diagnosis tool for dementia for several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and control subjects. For this purpose, two different methodologies and variations were used. The Short Time Fourier Transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands and their associated Spectral Ratios (r1, r2, r3, and r4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8%, and 91.5% of Accuracy.

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.

Molecular Genetics of Early- and Late-Onset Alzheimer’s Disease

2020 ◽  
Vol 20 ◽  
Author(s):  
Md. Sahab Uddin ◽  
Sharifa Hasana ◽  
Md. Farhad Hossain ◽  
Md. Siddiqul Islam ◽  
Tapan Behl ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Massively Parallel Sequencing ◽  
Late Onset ◽  
Current Knowledge ◽  
The Elderly ◽  
Apoe Ε4 ◽  
Sequencing Technologies ◽  
Genetic Components ◽  
Ε4 Allele

: Alzheimer’s disease (AD) is the most common form of dementia in the elderly and this complex disorder is associated with environmental as well as genetic components. Early-onset AD (EOAD) and late-onset AD (LOAD, more common) are major identified types of AD. The genetics of EOAD is extensively understood with three genes variants such as APP, PSEN1, and PSEN2 leading to disease. On the other hand, some common alleles including APOE are effectively associated with LOAD identified but the genetics of LOAD is not clear to date. It has been accounted that about 5% to 10% of EOAD patients can be explained through mutations in the three familiar genes of EOAD. The APOE ε4 allele augmented the severity of EOAD risk in carriers, and APOE ε4 allele was considered as a hallmark of EOAD. A great number of EOAD patients, who are not genetically explained, indicate that it is not possible to identify disease- triggering genes yet. Although several genes have been identified through using the technology of next-generation sequencing in EOAD families including SORL1, TYROBP, and NOTCH3. A number of TYROBP variants were identified through exome sequencing in EOAD patients and these TYROBP variants may increase the pathogenesis of EOAD. The existence of ε4 allele is responsible for increasing the severity of EOAD. However, several ε4 allele carriers live into their 90s that propose the presence of other LOAD genetic as well as environmental risk factors that are not identified yet. It is urgent to find out missing genetics of EOAD and LOAD etiology to discover new potential genetics facets which will assist to understand the pathological mechanism of AD. These investigations should contribute to developing a new therapeutic candidate for alleviating, reversing and preventing AD. This article based on current knowledge represents the overview of the susceptible genes of EOAD, and LOAD. Next, we represent the probable molecular mechanism which might elucidate the genetic etiology of AD and highlight the role of massively parallel sequencing technologies for novel gene discoveries.

Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech

2018 ◽  
Vol 15 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Shohei Kato ◽  
Akira Homma ◽  
Takuto Sakuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Characteristic Curve ◽  
Linear Regression Analysis ◽  
Speech Sound ◽  
The Elderly ◽  
Speech Sounds ◽  
Mel Frequency Cepstral Coefficients

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.

scholarly journals Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06226
Author(s):  
Diedre Carmo ◽  
Bruna Silva ◽  
Clarissa Yasuda ◽  
Letícia Rittner ◽  
Roberto Lotufo
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
Hippocampus Segmentation

scholarly journals Autoregulation of Cerebral Blood Flow to Changes in Arterial Pressure in Mild Alzheimer's Disease

2010 ◽  
Vol 30 (11) ◽  
pp. 1883-1889 ◽  
Author(s):  
Allyson R Zazulia ◽  
Tom O Videen ◽  
John C Morris ◽  
William J Powers
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Arterial Pressure ◽  
The Elderly ◽  
Local Defect ◽  
Clinical Dementia Rating ◽  
Positron Emission ◽  
Before And After

Studies in transgenic mice overexpressing amyloid precursor protein (APP) demonstrate impaired autoregulation of cerebral blood flow (CBF) to changes in arterial pressure and suggest that cerebrovascular dysfunction may be critically important in the development of pathological Alzheimer's disease (AD). Given the relevance of such a finding for guiding hypertension treatment in the elderly, we assessed autoregulation in individuals with AD. Twenty persons aged 75±6 years with very mild or mild symptomatic AD (Clinical Dementia Rating 0.5 or 1.0) underwent 15O-positron emission tomography (PET) CBF measurements before and after mean arterial pressure (MAP) was lowered from 107±13 to 92±9 mm Hg with intravenous nicardipine; 11C-PIB-PET imaging and magnetic resonance imaging (MRI) were also obtained. There were no significant differences in mean CBF before and after MAP reduction in the bilateral hemispheres (−0.9±5.2 mL per 100 g per minute, P=0.4, 95% confidence interval (CI)=−3.4 to 1.5), cortical borderzones (−1.9±5.0 mL per 100 g per minute, P=0.10, 95% CI=−4.3 to 0.4), regions of T2W-MRI-defined leukoaraiosis (−0.3±4.4 mL per 100 g per minute, P=0.85, 95% CI=−3.3 to 3.9), or regions of peak 11C-PIB uptake (−2.5±7.7 mL per 100 g per minute, P=0.30, 95% CI=−7.7 to 2.7). The absence of significant change in CBF with a 10 to 15 mm Hg reduction in MAP within the normal autoregulatory range demonstrates that there is neither a generalized nor local defect of autoregulation in AD.

scholarly journals Isovitexin modulates autophagy in Alzheimer’s disease via miR-107 signalling

2020 ◽  
Vol 11 (1) ◽  
pp. 391-401
Author(s):  
Jiang Cheng ◽  
Guowei Wang ◽  
Na Zhang ◽  
Fang Li ◽  
Lina Shi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
The Elderly ◽  
Tnf Α ◽  
Autophagic Process ◽  
Molecular Signals ◽  
Ad Mouse Model ◽  
Mtor Signalling

AbstractBackground:Alzheimer’s disease (AD) is an ultimately fatal, degenerative brain disease in the elderly people. In the current work, we assessed the defensive capability of isovitexin (IVX) through an intracerebroventricular injection of streptozotocin (STZ)-induced AD mouse model.Methods:Mice were separated into four cohorts: sham-operated control mice; STZ-intoxicated Alzheimer’s mice; IVX cohort, IVX + STZ; and Ant-107 cohort, antagomiR-107 + IVX/STZ as in the IVX cohort.Results:The outcomes indicated that IVX administration ameliorated spatial memory loss and blunted a cascade of neuro-noxious episodes – including increased amyloid-beta (Aβ) and degraded myelin basic protein burden, neuroinflammation (represented by elevated caspase-1, TNF-α and IL-6 levels) and autophagic dysfunction (represented by altered LC3-II, Atg7 and beclin-1 expressions) – via the inhibition of PI3K/Akt/mTOR signalling axis. We considered the question of whether the epigenetic role of microRNA-107 (miR-107) has any impact on these events, by using antagomiR-107.Conclusion:This probing underscored that miR-107 could be a pivotal regulatory button in the activation of molecular signals linked with the beneficial autophagic process and anti-inflammatory activities in relation to IVX treatment. Hence, this report exemplifies that IVX could guard against Aβ toxicity and serve as an effectual treatment for patients afflicted with AD.

An Informant Questionnaire for Detecting Alzheimer's Disease: Are Some Items Better Than Others?

2011 ◽  
Vol 17 (4) ◽  
pp. 674-681 ◽  
Author(s):  
Sietske A.M. Sikkes ◽  
Dirk L. Knol ◽  
Mark T. van den Berg ◽  
Elly S.M. de Lange-de Klerk ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Equation ◽  
Structural Model ◽  
The Elderly ◽  
Equation Modeling ◽  
Or Education ◽  
Healthy Elderly ◽  
Patient Groups ◽  
Better Than

AbstractA decline in everyday cognitive functioning is important for diagnosing dementia. Informant questionnaires, such as the informant questionnaire on cognitive decline in the elderly (IQCODE), are used to measure this. Previously, conflicting results on the IQCODEs ability to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI), and cognitively healthy elderly were found. We aim to investigate whether specific groups of items are more useful than others in discriminating between these patient groups. Informants of 180 AD, 59 MCI, and 89 patients with subjective memory complaints (SMC) completed the IQCODE. To investigate the grouping of questionnaire items, we used a two-dimensional graded response model (GRM).The association between IQCODE, age, gender, education, and diagnosis was modeled using structural equation modeling. The GRM with two groups of items fitted better than the unidimensional model. However, the high correlation between the dimensions (r=.90) suggested unidimensionality. The structural model showed that the IQCODE was able to differentiate between all patient groups. The IQCODE can be considered as unidimensional and as a useful addition to diagnostic screening in a memory clinic setting, as it was able to distinguish between AD, MCI, and SMC and was not influenced by gender or education. (JINS, 2011, 17, 674–681)

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