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- Electroencephalogram cepstral distances in alzheimer’s disease diagnosisPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João PauloAlzheimer's disease (AD) represents one ofthe greatest public health challenges worldwide nowadays, because it affects millions of people ali o ver the world and it is expected that the disease will increase considerably in the near future. This study is the first application attempt of cepstral analysis on Electroencephalogram (EEG) signals to find new parameters in arder to achieve a better differentiation belween EEGs of AD patients and Control subjects. The results show that the methodology that uses a combined Wavelet (WT) Biorthogonal (Bior) 3.5 and cepstrum analysis was able to describe the EEG dynamics with a higher discriminative power than the other WTs/spectmm methodologies m previous studies. The most important significance figures were found in cepstral distances between cepstrums oftheta and alpha bands (p=0. 00006<0. 05).
- EEG discrimination with artificial neural networksPublication . Rodrigues, Sérgio Daniel de Luís; Teixeira, João Paulo; Rodrigues, Pedro MiguelNeuro degenerative disorders associated with aging as Alzheimer’sdisease(AD) have been increasing significantly in the last decades. AD affects the cerebral cortex and causes specific changes in brain electrical activity. Therefore, the analysis of signals from the electroencephalogram(EEG) may reveal structural and functional deficiencies typically associated with AD. This study aimed to develop an Artificial Neural Network(ANN) to classify EEG signals between cognitively normal control subjects and patients with probable AD . The results showed that the EEG can be a very useful tool to obtain an accurate diagnosis of AD. The best results were performed using the Power Spectral Density(PSD) determined by Short Time Fourier Transform (STFT) with a ANN developed using Levenberg-Marquardt training algorithm, Logarithmic Sigmoid activation function and 9 nodes in the hidden layer(correlation coefficient training:0.99964, test:0.95758 and validation:0.9653 and with a total of:0.99245).
- Artificial neural networks in the discrimination of alzheimer’s diseasePublication . Rodrigues, Pedro Miguel; Teixeira, João PauloAlzheimer’s disease (AD) is the most common cause of dementia, a general term for memory loss and other intellectual abilities. The Electroencephalogram (EEG) has been used as diagnosis tool for dementia over 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 (delta, theta, alpha, beta and gamma) 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.
- Alzheimer’s disease recognition with artificial neural networksPublication . Rodrigues, Pedro Miguel; Teixeira, João PauloAlzheimer’s disease (AD) is the most common cause of dementia, and is well known for affect the memory loss and other intellectual disabilities. The Electroencephalogram (EEG) has been used as diagnosis tool for dementia over 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 (delta, theta, alpha, beta and gamma) 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.
- Lacsogram: a new EEG tool to diagnose Alzheimer's diseasePublication . Rodrigues, Pedro M.; Bispo, Bruno; Garrett, Carolina; Alves, Dílio; Teixeira, João Paulo; Freitas, Diamantino SilvaThis work proposes the application of a new electroencephalogram (EEG) signal processing tool - the lacsogram - to characterize the Alzheimer’s disease (AD) activity and to assist on its diagnosis at different stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). Statistical analyzes are performed to lacstral distances between conventional EEG subbands to find measures capable of discriminating AD in all stages and characterizing the AD activity in each electrode. Cepstral distances are used for comparison. Comparing all AD stages and Controls (C), the most important significances are the lacstral distances between subbands and (p=0.0014<0.05). The topographic maps show significant differences in parietal, temporal and frontal regions as AD progresses. Machine learning models with a leave-one-out cross-validation process are applied to lacstral/cepstral distances to develop an automatic method for diagnosing AD. The following classification accuracies are obtained with an artificial neural network: 95.55% for All vs All, 98.06% for C vs MCI, 95.99% for C vs ADM, 93.85% for MCI vs ADM-ADA. In C vs MCI, C vs ADM and MCI vs ADM-ADA, the proposed method outperforms the stateof- art methods by 5%, 1%, and 2%, respectively. In All vs All, it outperforms the state-of-art EEG and non-EEG methods by 6% and 2%, respectively. These results indicate that the proposed method represents an improvement in diagnosing AD.
- Evaluation of EEG spectralfeatures in Alzheimer disease discriminationPublication . Rodrigues, Pedro Miguel; Bispo, Bruno; Freitas, Diamantino Silva; Teixeira, João Paulo; Carreres, AliciaAlzheimer’s disease (AD) is considered one of the most disabling diseases and it has a high prevalence in developed countries. It is as well the most common cause of dementia and it affects particularly the elderly. The current AD diagnosis accuracy is relatively low. It is therefore necessary to optimize the methods for AD detection. The electroencephalogram (EEG) is an inexpensive and noninvasive technique, that is able to record the electromagnetic fields produced by the brain activity. It has shown in the recent past a growing quality of the contribution to show brain disorders. The aim of this study was to evaluate the individual and combined power of several EEG features in AD discrimination. 95.00% of sensitivity, 100.00% of specificity, 97.06% of accuracy and 0.98 of AUC were the best classification results obtained in this work.
- Electroencephalogram Signal Analysis in Alzheimer's Disease Early DetectionPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João Paulo; Alves, Dílio; Garrett, CarolinaThe 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%).
- EEG discrimination with artificial neural networksPublication . Rodrigues, Sérgio Daniel de Luís; Teixeira, João Paulo; Rodrigues, Pedro MiguelNeuradegeneralive disorders associated with aging as Alzheimer's disease (AO) have been increasing signilicantly in lhe last decades. AO affecls lhe cerebral cartex and causes specific changes in brain electrical activity. Therefore, the analysis 01 signals from lhe electroencephalogram (EEG) may raveal.
- Detection of alzheimer’s disease electroencephalogram temporal eventsPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João PauloAlzheimer’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 electroencephalogram temporal events detection by K-meansPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João PauloAlzheimer Disease (AD) is a chronic progressive and irreversible neurodegenerative brain disorder. Its diagnostic accuracy is relatively low and there is not a biomarker able to detect AD without invasive tests. This study is a new approach to obtained electroencephalogram (EEG) temporal events in order to improve the AD diagnosis. For that, K-means were used and the results suggested that there are sequences of EEG energy variation that appear more frequently in AD patients than in Health subject.