Loading...
6 results
Search Results
Now showing 1 - 6 of 6
- Early detection of electroencephalogram temporal events in Alzheimer's diseasePublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João Paulo; Alves, Dílio; Garrett, CarolinaAlzheimer’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.
- Alzheimer’s early prediction with electroencephalogramPublication . Rodrigues, Pedro Miguel; Teixeira, João Paulo; Garrett, Carolina; Alves, Dílio; Freitas, Diamantino SilvaAlzheimer's disease (AD) is currently an incurable illness that causes dementia and patienfs condition is progressively worse and it represents one ofthe greatest public health challenges worldwide. The main objective ofthis work was to develop a classificatiwmethodology for EEG signals to improve discrimination amongst patients at varying stages ofthe illness, Mitd Cognitive Impairment (MCI) patients and non-patients either in order to obtain a more reliable methodology to identify AD in early stages.
- Electroencephalogram hybrid method for alzheimer early detectionPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João Paulo; Bispo, Bruno; Alves, Dílio; Garrett, CarolinaAlzheimer’s disease (AD) is a neurocognitive illness that leads to dementia and mainly affects the elderly. As the percentage of old people is strongly increasing worldwide, it is urgent to develop contributions to solve this complex problem. The early diagnosis at AD first stage known as Mild Cognitive Impairment (MCI) needs a better accuracy and there is not a biomarker able to detect AD without invasive tests. In this study, Electroencephalogram (EEG) signals have been used to serve as a way of finding parameters to improve AD diagnosis in first stages. For that, a hybrid method based on a Cepstral analysis of EEG Discrete Wavelet Transform (DWT) multiband decomposition was developed. Several Cepstral Distances (CD) were extracted to verify the lag between cepstra of conventional bands signals. The results showed that this hybrid method is a good tool for describing and distinguishing the AD EEG activity along its different stages because several statistically significant parameters variations were found between controls, MCI, moderate AD and advanced AD (the lowest p-value=0.003<0.05).
- 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.
- 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%).
- Alzheimer's Early Prediction with ElectroencephalogramPublication . Rodrigues, Pedro Miguel; Teixeira, João Paulo; Garrett, Carolina; Alves, Dílio; Freitas, Diamantino SilvaAlzheimer’s disease (AD) is currently an incurable illness that causes dementia and patient’s condition is progressively worse and it represents one of the greatest public health challenges worldwide. The main objective of this work was to develop a classification methodology for EEG signals to improve discrimination amongst patients at varying stages of the illness, Mild Cognitive Impairment (MCI) patients and non-patients either in order to obtain a more reliable methodology to identify AD in early stages. For this purpose, a surrogate decision tree classifier was used with 2 different ways of cross-validation (leave-one-out-crossvalidation and 10-fold-cross validation). The EEG studied features were the values of maxima (NMax) and minima (NMin), the zero-crossing (Zcr) rate, the mean derivative value at a point (Mdif), the Relative Power (RP) in each of the conventional bands and finally the spectral ratios (r). The best classification was obtained with vectors of 10 features as classifier entries in a leaveone- out-cross validation process, reaching 0.934 AUC, a sensitivity of 86.19%, a specificity of 99.35%, an accuracy of 94.88%, with a low out-of-sample classification error of 6.98% which indicates that the classifier generalizes fairly well.