Evaluation of Heart Disease Diagnosis Approach using ECG Images

被引：0
作者：
Ferreira Junior, Marcos Aurelio A. [1 ]
Gurgel, Mateus Valentim [1 ]
Marinho, Leandro B. [2 ]
Nascimento, Navar Medeiros M. [2 ]
da Silva, Suane Pires P. [1 ]
Alves, Shara Shami A. [2 ]
Bezerra Ramalho, Gerald Luis [1 ]
Reboucas Filho, Pedro Pedrosa [1 ]
机构：
[1] Inst Fed Ceara, Programa Posgrad Ciencia Comp PPGCC, Fortaleza, Ceara, Brazil
[2] Univ Fed Ceara, Grad Program Teleinfonnat Engn PPGETI, Fortaleza, Ceara, Brazil
来源：
2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2019年
关键词：
MultiLayer Perceptron; Neural Networks; ECG; Heart diseases; ARRHYTHMIA DETECTION; BEAT CLASSIFICATION; FEATURE-EXTRACTION; RECOGNITION; INTEGRATION; FEATURES; TREE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页数：7
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