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
6 resultados
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- Traumatic brain injury: An EEG point of view(2017) IANOF, Jéssica Natuline; ANGHINAH, RenatoABSTRACT Traumatic brain injury (TBI) is a silent epidemic. Mild traumatic brain injury (mTBI) causes brain injury that results in electrophysiologic abnormalities visible on electroencephalography (EEG) recordings. The purpose of this brief review was to discuss the importance of EEG findings in traumatic brain injury. Relevant articles published during the 1996-2016 period were retrieved from Medline (PubMed). The keywords were in English and included ""traumatic brain injury"", ""EEG"" and ""quantitative EEG"". We found 460 articles, analyzed 52 and selected 13 articles. EEG after TBI shows slowing of the posterior dominant rhythm and increased diffuse theta slowing, which may revert to normal within hours or may clear more slowly over many weeks. There are no clear EEG or quantitative EEG (qEEG) features unique to mild traumatic brain injury. Although the literature indicates the promise of qEEG in reaching a diagnosis and indicating prognosis of mTBI, further study is needed to corroborate and refine these methods.
- 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.
- Towards automated electroencephalography-based Alzheimer's disease diagnosis using portable low-density devices(2017) CASSANI, Raymundo; FALK, Tiago H.; FRAGA, Francisco J.; CECCHI, Marco; MOORE, Dennis K.; ANGHINAH, RenatoToday, Alzheimer's disease (AD) diagnosis is carried out using subjective mental status examinations assisted in research by scarce and expensive neuroimaging scans and invasive laboratory tests; all of which render the diagnosis time-consuming, geographically confined and costly. Driven by these limitations, quantitative analysis of electroencephalography (EEG) has been proposed as a non-invasive and more convenient technique to study AD. Published works on EEG-based AD diagnosis typically share two main characteristics: EEG is manually selected by experienced clinicians to discard artefacts that affect AD diagnosis, and reliance on EEG devices with 20 or more electrodes. Recent work, however, has suggested promising results by using automated artefact removal (AAR) algorithms combined with medium-density EEG setups. Over the last couple of years, however, low-density, portable EEG devices have emerged, thus opening the doors for low-cost AD diagnosis in low-income countries and remote regions, such as the Canadian Arctic. Unfortunately, the performance of automated diagnostic solutions based on low-density portable devices is still unknown. The work presented here aims to fill this gap. We propose an automated EEG-based AD diagnosis system based on AAR and a low-density (7- channel) EEG setup. EEG data was acquired during resting-awake protocol from control and AD participants. After AAR, common EEG features, spectral power and coherence, are computed along with the recently proposed amplitude-modulation features. The obtained features are used for training and testing of the proposed diagnosis system. We report and discuss the results obtained with such system and compare the obtained performance with results published in the literature using higher-density EEG layouts.
- Alpha band EEG coherence in healthy nonagenarians(2017) JORGE, Mario Silva; SPINDOLA, Livia; KATATA, Joyce Haruyo Biancon; ANGHINAH, RenatoElectroencephalographic (EEG) coherence is a parameter that enables evaluation of cerebral connectivity. It may be related to the functional state of the brain. In the elderly, it may reflect the neuronal loss caused by aging. Objective: To describe characteristics of coherence in nonagenarians. Methods: We evaluated interhemispheric coherence for the alpha band in 42 cognitively normal individuals aged 90 to 101 years. Coherence values in the occipital electrode (0102), in the resting state with closed eyes, were calculated by means of spectral analysis using digital EEG EMSA 32 channels, 12 bits and a frequency of 200 Hz. Results: The mean coherence value for the alpha band at 0102 was 0.65 (SD 0.13). No significant differences were found between men and women. Conclusions: The findings from this study did not show any decrease in interhemispheric coherence for the alpha band in cognitively normal nonagenarians.This may be useful as a standard value for this age group.
conferenceObject Preliminary data of the first post-traumatic brain-injured (TBI) cognitive rehabilitation outpatient centre in Brazil (University of Sao Paulo, School of Medicine)(2017) AREZA-FEGYVERES, Renata; IANOF, Jessica Natuline; GUARIGLIA, Carla; WATANABE, Rafael Gustavo Sato; SCHMIDT, Magali Taino; FREIRE, Fabio Rios; NADRUZ, Patricia; OLIVEIRA, Eduardo; NASCIMENTO, Diana; ARAUJO, Amanda Vitoria; COELHO, Fernanda; ANGHINAH, Renato