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Publications

Publications by Miguel Coimbra

2013

Knowledge on Heart Condition of Children based on Demographic and Physiological Features

Authors
Ferreira, P; Vinhoza, TTV; Castro, A; Mourato, F; Tavares, T; Mattos, S; Dutra, I; Coimbra, M;

Publication
2013 IEEE 26TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Abstract
We evaluated a population of 7199 children between 2 and 19 years old to study the relations between the observed demographic and physiological features in the occurrence of a pathological/non-pathological heart condition. The data was collected at the Real Hospital Portugues, Pernambuco, Brazil, We performed a feature importance study, with the aim of categorizing the most relevant variables, indicative of abnormalities. Results show that second heart sound, weight, heart rate, height and secondary reason for consultation are important features, but not nearly as decisive as the presence of heart murmurs. Quantitatively speaking. systolic murmurs and a hyperphonetic second heart sound increase the odds of having a pathology by a factor of 320 and 6, respectively.

2016

Why should you model time when you use Markov models for heart sound analysis

Authors
Oliveira, J; Mantadelis, T; Coimbra, M;

Publication
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, FL, USA, August 16-20, 2016

Abstract

2017

On modifying the temporal modeling of HSMMs for pediatric heart sound segmentation

Authors
Oliveira, J; Mantadelis, T; Renna, F; Gomes, P; Coimbra, M;

Publication
2017 IEEE International Workshop on Signal Processing Systems, SiPS 2017, Lorient, France, October 3-5, 2017

Abstract

2013

Heart Sound Segmentation of Pediatric Auscultations Using Wavelet Analysis

Authors
Castro, A; Vinhoza, TTV; Mattos, SS; Coimbra, MT;

Publication
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Abstract
Auscultation is widely applied in clinical activity, nonetheless sound interpretation is dependent on clinician training and experience. Heart sound features such as spatial loudness, relative amplitude, murmurs, and localization of each component may be indicative of pathology. In this study we propose a segmentation algorithm to extract heart sound components (S1 and S2) based on it's time and frequency characteristics. This algorithm takes advantage of the knowledge of the heart cycle times (systolic and diastolic periods) and of the spectral characteristics of each component, through wavelet analysis. Data collected in a clinical environment, and annotated by a clinician was used to assess algorithm's performance. Heart sound components were correctly identified in 99.5% of the annotated events. S1 and S2 detection rates were 90.9% and 93.3% respectively. The median difference between annotated and detected events was of 33.9 ms.

2013

Combining a Tablet and an Electronic Stethoscope to Create a New Interaction Paradigm for Teaching Cardiac Auscultation

Authors
Pereira, D; Gomes, P; Mota, E; Costa, E; Cruz Correia, R; Coimbra, M;

Publication
Communications in Computer and Information Science

Abstract
Learning cardiac auscultation is a fundamental but hard task for a medicine student, involving a combination of gesture mechanics and cardiac sounds interpretation. We aim to create a low cost simulator combining a tablet and an electronic stethoscope, thus defining a new interaction paradigm that al-lows a student to train auscultation when and where they want. In this paper we evaluate the usability of a first approach to this new paradigm using a high-fidelity prototype and its heuristic evaluation. © Springer-Verlag Berlin Heidelberg 2013.

2015

Most probable explanation for MetaProbLog and its application in heart sound segmentation

Authors
Mantadelis, T; Oliveira, J; Coimbra, M;

Publication
CEUR Workshop Proceedings

Abstract
This paper, presents ongoing work that extends MetaProbLog with Most Probable Explanation (MPE) inference method. The MPE inference method is widely used in Hidden Markov Models in order to derive the most likely states of a model. Recently, we started developing an application that uses MetaProbLog to models phonocardiograms. We target to use this application in order to diagnose heart diseases by using phonocardiogram classification. Motivated by the importance of phonocardiogram classification, we started the implementation of the MPE inference method and an improvement of representation for annotated disjunctions.

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