Evaluation of Heart Disease Diagnosis Approach using ECG Images

Among illnesses, heart diseases are accounted for as one of the most responsible for deaths. Precise and fast diagnoses increase the patient's chances to receive treatment time. A non-invasive and low-cost way to diagnose it is by using Electrocardiogram (ECG). In this paper, we propose a way t...

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Bibliographic Details
Main Authors: Ferreira, Marcos Aurelio A., Gurgel, Mateus Valentim, Marinho, Leandro B., Nascimento, Navar Medeiros M., da Silva, Suane Pires P., Alves, Shara Shami A., Ramalho, Geraldo Luis Bezerra, Filho, Pedro Pedrosa Reboucas
Format: Conference Proceeding
Language:English
Subjects:
ECG
Electrocardiography
Feature extraction
Heart diseases
Heart rate variability
MultiLayer Perceptron
Neural networks
Support vector machines
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Summary:Among illnesses, heart diseases are accounted for as one of the most responsible for deaths. Precise and fast diagnoses increase the patient's chances to receive treatment time. A non-invasive and low-cost way to diagnose it is by using Electrocardiogram (ECG). In this paper, we propose a way to diagnosis two types of heart arrhythmia, by using the ECG record as an image. To access the performance of our system, five feature extraction methods well-known in literature are used along with five different classifiers are tested. We were able to identify heart disorders with over 96.00% of accuracy, using a vanilla neural-network, Multilayer Perceptron (MLP), and Local Binary Patterns (LBP) from ECG images. This investigation has shown promising results from a medical point-of-view.
ISSN:2161-4407
DOI:10.1109/IJCNN.2019.8851807