Browsing by Author "Rodrigues, Pedro Miguel"
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- Alzheimer electroencephalogram temporal events detection by K-meansPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João PauloAlzheimer Disease (AD) is a chronic progrcssive and irrevcrsible ncurodegencrative brain disorder. Ils diagnostic accuracy is rclatively low and lhere is nol a biomarkcr able lo detcct AD without invasive tcsts. This study is a ncw approach to obtuined clectroencephalogram (EEG) temporal cvenls in crder lo improve Lhe AD diagnosis. For that, K-means were used and the rcsults suggcsted that thcre are scquences af EEG cnergy variation tbat appear more frequently in AO patients lhan in Health subject.
- 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.
- 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.
- 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.
- 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.
- Alzheimer’s electroencephalogram event scalp and source localizationPublication . Rodrigues, Pedro Miguel; Teixeira, João Paulo; Freitas, Diamantino SilvaAlzheimer’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.
- Alzheimer's electroencephalogram event scalp localizationPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João PauloAlzheimer’s disease (AD) is a neurodegenerative and incurable illness that causes intellectual functions decrease. This study is a new approach to improve the scalp brain anomalies localization associated with Electroencephalogram (EEG) energy variations of EEG threads (subsegments) sequencies sets found in AD patients by unsupervised learning techniques, called AD EEG temporal events. This study showed that AD patients have less brain dynamics than controls, because the AD EEG events propagation time over the scalp is higher and statistically different from control subjects (p < 0.0022)
- Alzheimer's electroencephalogram event scalp localizationPublication . Rodrigues, Pedro Miguel; Freitas, Diamantino Silva; Teixeira, João PauloAlzheimer’s disease (AD) is a neurodegenerative and incurable illness that causes intellectual functions decrease. This study is a new approach to improve the scalp brain anomalies localization associated with Electroencephalogram (EEG) energy variations of EEG threads (subsegments) sequencies sets found in AD patients by unsupervised learning techniques, called AD EEG temporal events. This study showed that AD patients have less brain dynamics than controls, because the AD EEG events propagation time over the scalp is higher and statistically different from control subjects (p<0.0022).
- 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.
- Classification of alzheimer’s electroencephalograms using artificial neural networks and logistic regressionPublication . Rodrigues, Pedro Miguel; Teixeira, João Paulo; Hornero, Roberto; Poza, Jesús; Carreres, AliciaThe Artificial Neural Networks have been used over the years to solve complex problems and their development has strongly grown in recent years. In particular, this work, focused on the development and a comparison between Artificial Neural Networks (ANN) and a traditional statistical technic known as Logistic Regression (LR) in Electroencephalogram (EEG) classification. The Wavelet Transform was seen as the main technique of signal processing, in order to analyze the EEG signals of this study. Some features were extracted by the EEG signals like relative power (RP) in conventional frequency bands and two spectral ratios. The best feature combination was selected by Principal Components Analysis method to increase the accuracy of the ANN and LR to discriminate their entries between Alzheimer Disease and Controls.
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