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About

About

Diogo Marcelo Esterlita Nogueira, I was born on March 27, 1990. I am from São João da Pesqueira, Viseu and presently I live in Porto.

I am graduated in Biomedical Engineering by the University of Trás-os-Montes and Alto Douro (concluded in 2011) and I completed my Master's degree in Medical Physics, by the Faculty of Sciences of the University of Porto in 2014.

I started my professional career at INESC TEC in 2012, at the former Optoelectronics and Electronic Systems Unit, which today is called Center of Applied Photonics. During this period, I collaborated in the EYEFRY research project, whose participation ended in 2016.

In 2016 I joined another INESC TEC center, the LIAAD, and currently i'm working in the area of data mining and machine learning.

Interest
Topics
Details

Details

  • Name

    Diogo Marcelo Nogueira
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    15th November 2012
Publications

2023

The selection of an optimal segmentation region in physiological signals

Authors
Oliveira, J; Carvalho, M; Nogueira, D; Coimbra, M;

Publication
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.

2022

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Authors
Oliveira, J; Renna, F; Costa, PD; Nogueira, M; Oliveira, C; Ferreira, C; Jorge, A; Mattos, S; Hatem, T; Tavares, T; Elola, A; Rad, AB; Sameni, R; Clifford, GD; Coimbra, MT;

Publication
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

Abstract

2021

Do we really need a segmentation step in heart sound classification algorithms?

Authors
Oliveira, J; Nogueira, D; Renna, F; Ferreira, C; Jorge, AM; Coimbra, M;

Publication
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)

Abstract

2019

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Authors
Nogueira, DM; Ferreira, CA; Gomes, EF; Jorge, AM;

Publication
JOURNAL OF MEDICAL SYSTEMS

Abstract

2019

Heart Sounds Classification Using Images from Wavelet Transformation

Authors
Nogueira, DM; Zarmehri, MN; Ferreira, CA; Jorge, AM; Antunes, L;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2019, PT I

Abstract