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Detalhes

Detalhes

003
Publicações

2020

Teaching Cardiopulmonary Auscultation in Workshops using a Virtual Patient Simulation Technology - A Pilot Study

Autores
Pereira, D; Gomes, P; Faria, S; Correia, RC; Coimbra, MT;

Publicação
2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Abstract
Auscultation is currently both a powerful screening tool, providing a cheap and quick initial assessment of a patient's clinical condition, and a hard skill to master. The teaching of auscultation in Universities is today reduced to an unsuitable number of hours. Virtual patient simulators can potentially mitigate this problem, by providing an interesting high-quality alternative to teaching with real patients or patient simulators. In this paper we evaluate the pedagogical impact of using a virtual patient simulation technology in a short workshop format for medical students, training them to detect cardiac pathologies. Results showed a significant improvement (+16%) in the differentiation between normal and pathological cases, although longer duration formats seem to be needed to accurately identify specific pathologies.

2020

Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes

Autores
Martins, M; Gomes, P; Oliveira, C; Coimbra, M; da Silva, HP;

Publicação
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Abstract
Combining Phonocardiography (PCG) and Electrocardiography (ECG) data has been recognized within the state-of-the-art as of added value for enhanced cardiovascular assessment. However, multiple aspects of ECG data acquisition in a stethoscope form factor remain unstudied, and existing devices typically enforce a substantial change into routine clinical auscultation procedures, with predictably low technology acceptance. As such, in this paper, we present a novel approach to ECG data acquisition throughout the five main cardiac auscultation points, and that intends to be incorporated in a commonly used electronic stethoscope. Therefore, it enables analysis and acquisition of both PCG and ECG signals in a single pass. We describe the development, experimental evaluation, and comparison of the ECG signals obtained using our proposed approach and a gold standard medical device, through metrics that allow the evaluation of morphological similarities. Results point to a high correlation between the two evaluated setups, thus supporting the idea of meaningfully collecting ECG data along medical auscultation points with the proposed form factor. Moreover, this work has led us to conclude that for the studied population, signals acquired on focuses F1, F2, and F3 are usually highly correlated with leads V1 and V2 of the standard ECG medical recording procedure.

2020

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Autores
Pires, IM; Marques, G; Garcia, NM; Florez Revuelta, F; Canavarro Teixeira, M; Zdravevski, E; Spinsante, S; Coimbra, M;

Publicação
ELECTRONICS

Abstract
The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs' identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

2020

Gaussian Mixture Model Based Probabilistic Modeling of Images for Medical Image Segmentation

Autores
Riaz, F; Rehman, S; Ajmal, M; Hafiz, R; Hassan, A; Aljohani, NR; Nawaz, R; Young, R; Coimbra, M;

Publicação
IEEE Access

Abstract
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. © 2013 IEEE.

2020

Computer Vision Challenges for Chronic Wounds Assessment

Autores
Teixeira, PA; Sousa, PA; Coimbra, MT;

Publicação
42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2020, Montreal, QC, Canada, July 20-24, 2020

Abstract

Teses
supervisionadas

2020

Changing Perspectives: Interlead Conversion in Electrocardiographic Signals

Autor
Carolina Martins Barbosa Rodrigues Afonso

Instituição
UP-FCUP

2020

Diagnosis of Rheumatic Heart Diseases based in Phonocardiograms and Echocardiograms

Autor
Diogo Marcelo Esterlita Nogueira

Instituição
UP-FCUP

2020

Criação de algoritmos de Deep Learning para identificação de tecidos nas histeroscopias

Autor
Ana Sofia Ferreira Martins

Instituição
UP-FCUP

2019

Real-time analysis of vital sign signals for online health monitoring in Unsupervised environments

Autor
Can Ye

Instituição
UP-FCUP

2019

Medical images analysis using Deep Learning - A skin cancer screening system

Autor
José Querubim Rocha Reisinho

Instituição
UP-FCUP