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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).
- 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.
- Editorial: advances in machine learning approaches and technologies for supporting nervous system disease diagnosisPublication . Rodrigues, Pedro Miguel; Bispo, Bruno; Freitas, Diamantino Silva; Marques, João Alexandre Lobo; Teixeira, João PauloThe nervous system is essential for physical and mental health but is complex and delicate. As it can unfortunately be affected by several progressive diseases, an early diagnosis is often critical for effective treatment (Xu et al., 2022). The diagnosis of nervous system diseases traditionally relies on a combination of clinical examination, imaging and signals tests, and laboratory tests (Siuly and Zhang, 2016). However, these methods can be time-consuming, expensive, and not always accurate (Milligan, 2019). In an era marked by unprecedented technological advances in machine learning (ML), a computational tool that allows the identification of patterns in data that would be difficult or even impossible for humans, its application to assist in medical diagnosis emerges as a beacon of hope in the complex panorama of nervous system diseases. The Research Topic Advances in machine learning approaches and technologies for supporting nervous system disease diagnosis aims to shed light on the transformative role that ML-based approaches and technologies are playing in reshaping the way an ensemble of nervous system disorders are understood, diagnosed, and treated.
- 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.
- 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.
- 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).
- 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.
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