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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.



  • Name

    Diogo Marcelo Nogueira
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    15th November 2012


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

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

Journal of Medical Systems



Classifying Heart Sounds Using Images of MFCC and Temporal Features

Nogueira, DM; Ferreira, CA; Jorge, AM;

Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Porto, Portugal, September 5-8, 2017, Proceedings

Phonocardiogram signals contain very useful information about the condition of the heart. It is a method of registration of heart sounds, which can be visually represented on a chart. By analyzing these signals, early detections and diagnosis of heart diseases can be done. Intelligent and automated analysis of the phonocardiogram is therefore very important, to determine whether the patient’s heart works properly or should be referred to an expert for further evaluation. In this work, we use electrocardiograms and phonocardiograms collected simultaneously, from the Physionet challenge database, and we aim to determine whether a phonocardiogram corresponds to a “normal” or “abnormal” physiological state. The main idea is to translate a 1D phonocardiogram signal into a 2D image that represents temporal and Mel-frequency cepstral coefficients features. To do that, we develop a novel approach that uses both features. First we segment the phonocardiogram signals with an algorithm based on a logistic regression hidden semi-Markov model, which uses the electrocardiogram signals as reference. After that, we extract a group of features from the time and frequency domain (Mel-frequency cepstral coefficients) of the phonocardiogram. Then, we combine these features into a two-dimensional time-frequency heat map representation. Lastly, we run a binary classifier to learn a model that discriminates between normal and abnormal phonocardiogram signals. In the experiments, we study the contribution of temporal and Mel-frequency cepstral coefficients features and evaluate three classification algorithms: Support Vector Machines, Convolutional Neural Network, and Random Forest. The best results are achieved when we map both temporal and Mel-frequency cepstral coefficients features into a 2D image and use the Support Vector Machines with a radial basis function kernel. Indeed, by including both temporal and Mel-frequency cepstral coefficients features, we obtain sligthly better results than the ones reported by the challenge participants, which use large amounts of data and high computational power. © Springer International Publishing AG 2017.