Dr. Michael A. Alvarez Navarro Profile

Dr. Michael A. Alvarez Navarro

at University of Puerto Rico Rio Piedras

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Publications (4)

Proceedings Article | 19 March 2024 Presentation

A computational framework to analyze time and location-dependent NIRS readouts for detecting cytochrome c oxidase

Anastasia Goulopoulos, Michael Alvarez, Heidy Sierra, Walfre Franco

Proceedings Volume PC12822, PC128220A (2024) https://doi.org/10.1117/12.3003150

KEYWORDS: Near infrared spectroscopy, Mitochondria, Signal detection, Oxygen, 3D modeling, Tissues, Spectroscopy, Oxygenation, Tissue optics, Photoplethysmography

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Proceedings Article | 28 September 2023 Presentation + Paper

Using Monte Carlo simulated PPGs signals to train a deep learning model to predict hemoglobin levels

Michael Alvarez, Emmanuel Arzuaga, Heidy Sierra

Proceedings Volume 12655, 1265504 (2023) https://doi.org/10.1117/12.2677499

KEYWORDS: Blood oxygen saturation, Monte Carlo methods, Simulations, Education and training, Deep learning, Tissues, Signal generators, Skin, Blood

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Measuring Hemoglobin (Hb) levels is required for the assessment of different health conditions, such as anemia, a condition where there are insufficient healthy red blood cells to carry enough oxygen to the body’s tissues. Measuring Hb levels requires the extraction of a blood sample, which is then sent to a laboratory for analysis. This is an invasive procedure that may add challenges to the continuous monitoring of Hb levels. Noninvasive techniques, including imaging and photoplethysmography (PPG) signals combined with machine learning techniques, are being investigated for continuous measurements of Hb. However, the availability of real data to train the algorithms is limited to establishing a generalization and implementation of such techniques in healthcare settings. In this work, we present a computational model based on Monte Carlo simulations that can generate multispectral PPG signals that cover a broad range of Hb levels. These signals are then used to train a Deep Learning (DL) model to estimate hemoglobin levels. Through this approach, valuable insights about the relationships between PPG signals, oxygen saturation, and Hb levels are learned by the DL model. The signals were generated by propagating a source in a volume that contains the skin tissue properties and the target physiological parameters. The source consisted of plane waves using the 660 nm and 890 nm wavelengths. A range of 6 g/dL to 18 dL Hb values was used to generate 468 PPGs to train a Convolutional Neural Network (CNN). The initial results show high accuracy in detecting low levels of Hb. To the best of our knowledge, the complexity of biological interactions involved in measuring hemoglobin levels has yet to be fully modeled. The presented model offers an alternative approach to studying the effects of changes in Hb levels on the PPGs signal morphology and its interaction with other physiological parameters that are present in the optical path of the measured signals.

Proceedings Article | 19 May 2020 Presentation + Paper

Studying the effect of a decision fusion model on enhanced hyperspectral images

Carlos Theran, Michael Alvarez, Heidy Sierra, Emmanuel Arzuaga

Proceedings Volume 11392, 1139206 (2020) https://doi.org/10.1117/12.2560107

KEYWORDS: Data fusion, Image fusion, Hyperspectral imaging, Spatial resolution, Image classification, Data modeling, Fuzzy logic, Remote sensing, Neural networks, Buildings

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Proceedings Article | 19 May 2020 Presentation + Paper

Analyzing the effects of pixel-scale data fusion in hyperspectral image classification performance

Michael Alvarez, Carlos Theran, Emmanuel Arzuaga, Heidy Sierra

Proceedings Volume 11392, 1139205 (2020) https://doi.org/10.1117/12.2560106

KEYWORDS: Spatial resolution, Image fusion, Data fusion, Hyperspectral imaging, Image classification, Image enhancement, Resolution enhancement technologies, Multispectral imaging, Spectral resolution

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