RENATO ANGHINAH
Projetos de Pesquisa
Unidades Organizacionais
Instituto Central, Hospital das Clínicas, Faculdade de Medicina
LIM/45 - Laboratório de Fisiopatologia Neurocirúrgica, Hospital das Clínicas, Faculdade de Medicina
LIM/45 - Laboratório de Fisiopatologia Neurocirúrgica, Hospital das Clínicas, Faculdade de Medicina
11 resultados
Resultados de Busca
Agora exibindo 1 - 10 de 11
conferenceObject Towards Automated EEG-Based Alzheimer's Disease Diagnosis Using Relevance Vector Machines(2014) CASSANI, Raymundo; FALK, Tiago H.; FRAGA, Francisco J.; KANDA, Paulo A.; ANGHINAH, RenatoExisting electroencephalography (EEG) based Alzheimer's disease (AD) diagnostic systems typically rely on experts to visually inspect and segment the collected signals into artefact-free epochs and on support vector machine (SVM) based classifiers. The manual selection process, however, introduces biases and errors into the diagnostic procedure, renders it ""semi-automated,"" and makes the procedure costly and labour-intensive. In this paper, we overcome these limitations by proposing the use of an automated artefact removal (AAR) algorithm to remove artefacts from the EEG signal without the need for human intervention. We investigate the effects of the so-called wavelet-enhanced independent component analysis (wICA) AAR on three classes of EEG features, namely spectral power, coherence, and amplitude modulation, and ultimately, on diagnostic accuracy, specificity and sensitivity. Furthermore, we propose to replace the binary SVM classifier with a soft-decision relevance vector machine (RVM) classifier. Experimental results show the proposed RVM-based system outperforming the SVM trained on features extracted from both manually-selected and wICA-processed epochs. Moreover, the class membership information output by the RVM is shown to provide clinicians with a richer pool of information to assist with AD assessment.- Characterizing Alzheimer's Disease Severity via Resting-Awake EEG Amplitude Modulation Analysis(2013) FRAGA, Francisco J.; FALK, Tiago H.; KANDA, Paulo A. M.; ANGHINAH, RenatoChanges in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer's disease (AD). Existing tools available to perform such analysis (e.g., detrended fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer's disease, but also to monitor its progression.
article 19 Citação(ões) na Scopus Alzheimer's disease qEEG Spectral analysis versus coherence. Which is the best measurement?(2011) ANGHINAH, Renato; KANDA, Paulo Afonso Medeiros; LOPES, Helder Frederico; BASILE, Luis Fernando Hindi; MACHADO, Sergio; RIBEIRO, Pedro.; VELASQUES, Bruna; SAMESHIMA, Koichi; TAKAHASHI, Daniel Yasumasa; PINTO, Lecio Figueira; CARAMELLI, Paulo; NITRINI, RicardoThere is evidence in electroencephalography that alpha, theta and delta band oscillations reflect cognitive and memory performances and that quantitative techniques can improve the electroencephalogram (EEG) sensitivity. This paper presents the results of comparative analysis of qEEG variables as reliable markers for Alzheimer's disease (AD). We compared the sensitivity and specificity between spectral analysis (spectA) and coherence (Coh) within the same group of AD patients. SpectA and Coh were calculated from EEGs of 40 patients with mild to moderate AD and 40 healthy elderly controls. The peak of spectA was smaller in the AD group than in controls. AD group showed predominance of slow spectA in theta and delta bands and a significant reduction of inter-hemispheric Coh for occipital alpha 2 and beta 1 and for frontal delta sub-band. ROC curve supported that alpha band spectA was more sensitive than coherence to differentiate controls from AD.- Chronic Traumatic Encephalopathy Presenting as Alzheimer's Disease in a Retired Soccer Player(2016) GRINBERG, Lea T.; ANGHINAH, Renato; NASCIMENTO, Camila Fernandes; AMARO JR., Edson; LEITE, Renata P.; MARTIN, Maria da Graca M.; NASLAVSKY, Michel S.; TAKADA, Leonel T.; JACOB FILHO, Wilson; PASQUALUCCI, Carlos A.; NITRINI, RicardoThe relationship between soccer and chronic traumatic encephalopathy (CTE) is not well established. We report clinicopathological correlations in an 83-year-old retired center-back soccer player, with no history of concussion, manifesting typical Alzheimer-type dementia. Examination revealed mixed pathology including widespread CTE, moderate Alzheimer's disease, hippocampal sclerosis, and TDP-43 proteinopathy. This case adds to a few CTE cases described in soccer players. Furthermore, it corroborates that CTE may present clinically as typical Alzheimer-type dementia. Further studies investigating the extent to which soccer is a risk for CTE are needed.
- Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis(2017) IANOF, Jéssica Natuline; FRAGA, Francisco José; FERREIRA, Leonardo Alves; RAMOS, Renato Teodoro; DEMARIO, José Luiz Carlos; BARATHO, Regina; BASILE, Luís Fernando Hindi; NITRINI, Ricardo; ANGHINAH, RenatoABSTRACT Alzheimer's disease (AD) is a dementia that affects a large contingent of the elderly population characterized by the presence of neurofibrillary tangles and senile plaques. Traumatic brain injury (TBI) is a non-degenerative injury caused by an external mechanical force. One of the main causes of TBI is diffuse axonal injury (DAI), promoted by acceleration-deceleration mechanisms. Objective: To understand the electroencephalographic differences in functional mechanisms between AD and DAI groups. Methods: The study included 20 subjects with AD, 19 with DAI and 17 healthy adults submitted to high resolution EEG with 128 channels. Cortical sources of EEG rhythms were estimated by exact low-resolution electromagnetic tomography (eLORETA) analysis. Results: The eLORETA analysis showed that, in comparison to the control (CTL) group, the AD group had increased theta activity in the parietal and frontal lobes and decreased alpha 2 activity in the parietal, frontal, limbic and occipital lobes. In comparison to the CTL group, the DAI group had increased theta activity in the limbic, occipital sublobar and temporal areas. Conclusion: The results suggest that individuals with AD and DAI have impairment of electrical activity in areas important for memory and learning.
- Feature selection before EEG classification supports the diagnosis of Alzheimer's disease(2017) TRAMBAIOLLI, L. R.; SPOLAOR, N.; LORENA, A. C.; ANGHINAH, R.; SATO, J. R.Objective: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redundant features.& para;& para;Objective: This study aimed to evaluate the relevance of FS approaches applied to Alzheimer's Disease (AD) EEG-based diagnosis and compare the selected features with previous clinical findings.& para;& para;Methods: Eight different FS algorithms were applied to EEG spectral measures from 22 AD patients and 12 healthy age-matched controls. The FS contribution was evaluated by considering the leave-one-subject-out accuracy of Support Vector Machine classifiers built in the datasets described by the selected features.& para;& para;Results: The Filtered Subset Evaluator technique achieved the best performance improvement both on a per-patient basis (91.18% of accuracy) and on a per-epoch basis (85.29 +/- 21.62%), after removing 88.76 +/- 1.12% of the original features. All algorithms found out that alpha and beta bands are relevant features, which is in agreement with previous findings from the literature.& para;& para;Conclusion: Biologically plausible EEG datasets could achieve improved accuracies with pre-processing FS steps.& para;& para;Significance: The results suggest that the FS and classification techniques are an attractive complementary tool in order to reveal potential biomarkers aiding the AD clinical diagnosis. (C) 2017 Published by Elsevier Ireland Ltd on behalf of International Federation of Clinical Neurophysiology.
- Clinician's Road Map to Wavelet EEG as an Alzheimer's disease Biomarker(2014) KANDA, Paulo Afonso Medeiros; TRAMBAIOLLI, Lucas R.; LORENA, Ana C.; FRAGA, Francisco J.; BASILE, Luis Fernando I.; NITRINI, Ricardo; ANGHINAH, RenatoAlzheimer's disease (AD) is considered the main cause of dementia in Western countries. Consequently, there is a need for an accurate, universal, specific and cost-effective biomarker for early AD diagnosis, to follow disease progression and therapy response. This article describes a new diagnostic approach to quantitative electroencephalogram (QEEG) diagnosis of mild and moderate AD. The data set used in this study was composed of EEG signals recorded from 2 groups: (S1) 74 normal subjects, 33 females and 41 males (mean age 67 years, standard deviation = 8) and (S2) 88 probable AD patients (NINCDS-ADRDA criteria), 55 females and 33 males (mean age 74.7 years, standard deviation = 7.8) with mild to moderate symptoms (DSM-IV-TR). Attention is given to sample size and the use of state of the art open source tools (LetsWave and WEKA) to process the EEG data. This innovative technique consists in associating Morlet wavelet filter with a support vector machine technique. A total of 111 EEG features (attributes) were obtained for 162 probands. The results were accuracy of 92.72% and area under the curve of 0.92 (percentage split test). Most important, comparing a single patient versus the total data set resulted in accuracy of 84.56% (leave-one-patient-out test). Particular emphasis was on clinical diagnosis and feasibility of implementation of this low-cost procedure, because programming knowledge is not required. Consequently, this new method can be useful to support AD diagnosis in resource-limited settings.
- EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease(2012) FALK, Tiago H.; FRAGA, Francisco J.; TRAMBAIOLLI, Lucas; ANGHINAH, RenatoRecent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.
- Oral Infections and Orofacial Pain in Alzheimer's Disease: A Case-Control Study(2014) ROLIM, Thais de Souza; FABRI, Gisele Maria Campos; NITRINI, Ricardo; ANGHINAH, Renato; TEIXEIRA, Manoel Jacobsen; SIQUEIRA, Jose Tadeu T. de; CESTARI, Jose Augusto Ferrari; SIQUEIRA, Silvia Regina Dowgan T. deBackground: Dental infections are frequent and have recently been implicated as a possible risk factor for Alzheimer's disease (AD). Despite a lack of studies investigating orofacial pain in this patient group, dental conditions are known to be a potential cause of pain and to affect quality of life and disease progression. Objectives: To evaluate oral status, mandibular function and orofacial pain in patients with mild AD versus healthy subjects matched for age and gender. Methods: Twenty-nine patients and 30 control subjects were evaluated. The protocol comprised a clinical questionnaire and dental exam, research diagnostic criteria for temporomandibular disorders, the McGill Pain Questionnaire, the decayed, missing, and filled teeth index, and included a full periodontal evaluation. AD signs and symptoms as well as associated factors were evaluated by a trained neurologist. Results: A higher prevalence of orofacial pain (20.7%, p < 0.001), articular abnormalities in temporomandibular joints (p < 0.05), and periodontal infections (p = 0.002) was observed in the study group compared to the control group. Conclusion: Orofacial pain and periodontal infections were more frequent in patients with mild AD than in healthy subjects. Orofacial pain screening and dental and oral exams should be routinely performed in AD patients in order to identify pathological conditions that need treatment thus improving quality of life compromised due to dementia.
conferenceObject Improvement in the automatic classification of Alzheimer's disease using EEG after feature selection(2019) TAVARES, Guilherme; SAN-MARTIN, Rodrigo; IANOF, Jessica N.; ANGHINAH, Renato; FRAGA, Francisco J.Improvement in early Alzheimer's disease (AD) diagnosis using EEG, as a consequence of advances in Machine Learning (ML) techniques, may be a valuable asset to physicians. However, in order to disseminate the use of this technology through distinct areas of the globe, from developed to developing countries, from urban to rural regions and from dense to underpopulated regions, the system must be simple, reliable and economically viable. Towards this goal, we evaluated automatic AD-EEG diagnosis accuracy changes before and after feature selection. Nineteen AD patients and 17 healthy subjects (HS) had their resting-state 32-channel EEG recorded for 25 minutes. Power spectrum density (PSD) in bands delta (1.5 - 6 Hz), theta (6.5 - 8 Hz), alpha1 (8.5 10 Hz), alpha2 (10.5 - 12 Hz), beta1 (12.5 - 18 Hz), beta2 (18.5 - 21 Hz) and beta3 (21.5 - 30 Hz) were extracted from EEG signals. After that, participants were automatically classified as AD or HS with eight different machine learning algorithms under Regression, Tree, Support Vector Machine (SVM) and Ensemble categories. Lastly, feature selection (FS) yielded a robust reduction to the number of features and channels needed and also improved classification performance. After FS, the Regression, SVM and Ensemble categories displayed average accuracy of 95.6% (92.86 - 97.14), F1 score of 97.74% (96.3 - 98.55), channel numbers 25.88 (10.4 - 31) and number of features 68.52 (13.18 - 93.4). Our results suggest that reducing the number of features and channels may not only optimize the computational and equipment cost, as well as EEG test preparation time and complexity (due to the reduced number of channels), but also increase the discriminatory power of classifiers.