MARCELO ARRUDA FIUZA DE TOLEDO

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Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina

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    IoT Medical Device Architecture to Estimate Non-invasive Arterial Blood Pressure
    (2022) MORENO, Ramon; DIAS, Felipe; ARRUDA, Marcelo; OLIVEIRA, Filipe; BULHOES, Thiago; KRIEGER, Jose; GUTIERREZ, Marco
    High blood pressure (BP) is the leading cause of death worldwide. Besides being a treatable condition, alongside medication and a healthy diet, it requires regular BP measurements to assess whether a patient is properly responding to treatment. There have been many attempts to use the photoplethysmography (PPG) signal to estimate BP continuously, but there has yet to be an effective solution. This work presents our efforts to develop a new method for estimating BP from PPG and infrastructure to collect, process, and store this information. PPG signal is measured from a smartband; our App reads the data from the smartband to a smartphone, processes them using a machine learning method, and estimates BP, which is sent to a server that stores and displays the data
  • article 0 Citação(ões) na Scopus
    Blood Pressure Estimation From Photoplethysmography by Considering Intra- and Inter-Subject Variabilities: Guidelines for a Fair Assessment
    (2023) COSTA, Thiago Bulhoes Da Silva; DIAS, Felipe Meneguitti; CARDENAS, Diego Armando Cardona; TOLEDO, Marcelo Arruda Fiuza De; LIMA, Daniel Mario De; KRIEGER, Jose Eduardo; GUTIERREZ, Marco Antonio
    Cardiovascular diseases are the leading causes of death, and blood pressure (BP) monitoring is essential for prevention, diagnosis, assessment, and treatment. Photoplethysmography (PPG) is a low-cost opto-electronic technique for BP measurement that allows the acquisition of a modulated light signal highly correlated with BP. There are several reports of methods to estimate BP from PPG with impressive results; in this study, we demonstrate that the previous results are excessively optimistic because of their train/test split configuration. To manage this limitation, we considered intra- and inter-subject data arrangements and demonstrated how they affect the results of feature-based BP estimation algorithms (i.e., XGBoost, LightGBM, and CatBoost) and signal-based algorithms (i.e., Residual U-Net, ResNet-18, and ResNet-LSTM). Inter-subject configuration performance is inferior to intra-subject configuration performance, regardless of the model. We also showed that, using only demographic attributes (i.e., age, sex, weight, and subject index number), a regression model achieved results comparable to those obtained in an intra-subject scenario.Although limited to a public clinical database, our findings suggest that algorithms that use an intra-subject setting without a calibration strategy may be learning to identify patients and not predict BP.
  • article 0 Citação(ões) na Scopus
    Study of CNN Capacity Applied to Left Ventricle Segmentation in Cardiac MRI
    (2021) TOLEDO, M. A. F.; LIMA, D. M.; KRIEGER, J. E.; GUTIERREZ, M. A.
    CNN (Convolutional Neural Network) models have been successfully used for segmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance Imaging), providing clinical measurements. In practice, two questions arise with deployment of CNNs: (1) when is it better to use a shallow model instead of a deeper one? (2) how the size of a dataset might change the network performance? We propose a framework to answer them, by experimenting with deep and shallow versions of three U-Net families, trained from scratch in six subsets varying from 100 to 10,000 images, different network sizes, learning rates and regularization values. 1620 models were evaluated using five-fold cross-validation by loss and DICE. The results indicate that: sample size affects performance more than architecture or hyper-parameters; in small samples the performance is more sensitive to hyper-parameters than architecture; the performance difference between shallow and deeper networks is not the same across families. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.