Browsing by Author "Bispo, Bruno"
Now showing 1 - 6 of 6
Results Per Page
Sort Options
- 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.
- 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).
- 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.
- Features Selection Algorithms for Classification of Voice SignalsPublication . Silva, Letícia; Bispo, Bruno; Teixeira, João PauloIn data mining problems, the high dimensionality of the input features can affect the performance of the process. In this way, the features selection methods appear as a solution to the problems encountered when analyzing databases with large dimensions. This article presents the implementation of the Pearson's linear correlation, ReliefF, Welch's t-test and multilinear regression based algorithms with forwards selection and backward elimination direction for the selection of acoustic features for the task of voice pathologies identification. The best set of selected features improved the accuracy and F1-score from 83% to 92% (9 points of percentage), using the ReliefF algorithm.
- 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.
- Outliers treatment to improve the recognition of voice pathologiesPublication . Silva, Letícia; Hermsdorf, Juliana; Guedes, Victor; Teixeira, Felipe; Fernandes, Joana Filipa Teixeira; Bispo, Bruno; Teixeira, João PauloIn some of the processes used in data analysis, such as the recognition of pathologies and pathological subjects, the presence of anomalous instances in the dataset is an unfavorable situation that can lead to misleading results. This article presents a function that implements the identification of anomalies in dataset using the boxplot and standard deviation methods. Also was used the filling technique to treat these anomalies, in which the anomalous point value were substituted by a limit value determined by the boxplot or standard deviation methods. To improve the outliers methods some normalization processes based on the z-score, logarithmic and squared root methodologies were experimented. These outliers treatment were applied to the dataset used in the recognition of vocal pathologies (dysphonia, chronic laryngitis and vocal cords paralysis vs control), performed by a MLP and LSTM neural networks. After the experiments, both the standard deviation and the boxplot methods with z-score normalization showed very useful for pre-processing the dataset for voice pathologies recognition. The accuracy was improved between 3 and 13 points in percentage.