RENATO ANGHINAH

(Fonte: Lattes)
Índice h a partir de 2011
19
Lattes
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Instituto Central, Hospital das Clínicas, Faculdade de Medicina
LIM/45 - Laboratório de Fisiopatologia Neurocirúrgica, Hospital das Clínicas, Faculdade de Medicina

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Agora exibindo 1 - 10 de 12
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    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, Renato
    Existing 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.
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    Recurrent visits to the Emergency Department (ED) due to Headache: economic burden and epidemiological profile
    (2019) SOUZA, Marcio Nattan P.; CALDERARO, Marcelo; OLIVEIRA, Ana Paula D. S.; KUBOTA, Gabriel T.; ZAMBON, Lucas S.; ANGHINAH, Renato; JORDAO, Mauricio R.
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    CORRELATION BETWEEN CHANGES IN GREY AND WHITE MATTER RADIODENSITY WITH PROGNOSIS AFTER CRANIOPLASTY
    (2014) OLIVEIRA, Arthur Maynart Pereira; AMORIM, Robson Luis Oliveira de; PAIVA, Wellingson Silva; ANDRADE, Almir Ferreira de; PASCHOAL JUNIOR, Fernando Mendes; BOR-SENG-SHU, Edson; COELHO, Fernanda; GATTAS, Gabriel Scarabotolo; ANGHINAH, Renato; TEIXEIRA, Manoel Jacobsen
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    TOWARDS AN EEG-BASED BIOMARKER FOR ALZHEIMER'S DISEASE: IMPROVING AMPLITUDE MODULATION ANALYSIS FEATURES
    (2013) FRAGA, Francisco J.; FALK, Tiago H.; TRAMBAIOLLI, Lucas R.; OLIVEIRA, Eliezyer F.; PINAYA, Walter H. L.; KANDA, Paulo A. M.; ANGHINAH, Renato
    In this paper, an EEG-based biomarker for automated Alzheimer's disease (AD) diagnosis is described, based on extending a recently-proposed ""percentage modulation energy"" (PME) metric. More specifically, to improve the signal-to-noise ratio of the EEG signal, PME features were averaged over different durations prior to classification. Additionally, two variants of the PME features were developed: the ""percentage raw energy"" (PRE) and the ""percentage envelope energy"" (PEE). Experimental results on a dataset of 88 participants (35 controls, 31 with mild-AD and 22 with moderate AD) show that over 98% accuracy can be achieved with a support vector classifier when discriminating between healthy and mild AD patients, thus significantly outperforming the original PME biomarker. Moreover, the proposed system can achieve over 94% accuracy when discriminating between mild and moderate AD, thus opening doors for very early diagnosis.
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    MORPHOLOGICAL CHANGES ON CORTICAL SURFACE AND THEIR CORRELATION OF WITH NEUROLOGICAL OUTCOME IN PATIENTS WITH BONE DEFECTS AFTER DECOMPRESSIVE CRANIECTOMY
    (2014) OLIVEIRA, Arthur Maynart Pereira; AMORIM, Robson Luis Oliveira de; PAIVA, Wellingson Silva; ANDRADE, Almir Ferreira de; PASCHOAL JUNIOR, Fernando Mendes; BOR-SENG-SHU, Edson; COELHO, Fernanda; GATTAS, Gabriel Scarabotolo; ANGHINAH, Renato; TEIXEIRA, Manoel Jacobsen
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    WHAT CAN WE REALLY EXPECT OF CEREBRAL BLOOD FLOW AFTER CRANIOPLASTY?
    (2014) OLIVEIRA, Arthur Maynart Pereira; AMORIM, Robson Luis Oliveira de; PAIVA, Wellingson Silva; ANDRADE, Almir Ferreira de; PASCHOAL JUNIOR, Fernando Mendes; BOR-SENG-SHU, Edson; COELHO, Fernanda; GATTAS, Gabriel Scarabotolo; ANGHINAH, Renato; TEIXEIRA, Manoel Jacobsen
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    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.
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    Chronic traumatic encephalopathy - a study in Brazilian retired soccer players
    (2019) LANOF, Jessica Natuline; AREZA-FEGYVERES, Renata; GUARIGLIA, Carla; FREIRE, Fabio; NADRUZ, Patr Prime Icia; CERASI, Alessandra; LEITE, Claudia; PASTORELLO, Bruno; CERRI, Giovanni Guido; ANGHINAH, Renato
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    Effects of cranioplasty in cerebral blood flow
    (2015) PAIVA, Wellingson Silva; OLIVEIRA, Arthur; AMORIM, Robson; BOR-SENG-SHU, Edson; ANGHINAH, Renato; ANDRADE, Almir; TEIXEIRA, Manoel
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    Use of a Questionnaire to Measure Concussion Knowledge in Brazilian adults
    (2019) ARAUJO, Amanda; AREZA-FEGYVERES, Renata; GUARIGLIA, Carla; LANOF, Jessica Natuline; BARATHO, Regina; DEMARIO, Jose; WATANABE, Rafael; ANGHINAH, Renato