Detection of Heart Abnormalities Based On ECG Signal Characteristics using Multilayer Perceptron with Firefly Algorithm-Simulated Annealing

Sofiah Ishlakhul Abda, A. Damayanti, E. Winarko
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Abstract

Heart disease is one of the causes of death worldwide. Therefore, detecting heart disease is very important to reduce the increased mortality rate. One of the methods used to detect the abnormalities or disorders of the heart is to use computer assistance to determine the characteristics of an electrocardiogram. Electrocardiogram (ECG) is a test that detects and records the activity of the heart through small metal electrodes attached to the skin of one's chest, arms and legs. This test shows how fast the heart beats and whether the rhythm is stable or not. The purpose of this thesis is to apply a multi-layer perceptron model with firefly algorithm and simulated annealing in detecting cardiac abnormalities based on the ECG signal characteristics. The initial step of this research is image processing. The stages of ECG image processing are grayscale, thresholding, edge detection, segmentation and normalization processes. The results of this image processing are used as input matrices in the perceptron multilayer network training using firefly algorithm and simulated annealing. In the training process, we will get optimal weights and biases for validation tests on test data. The training data in this thesis uses 20 ECG images and in the validation test process uses 10 ECG images. The validation results in the validation test show that the accuracy in detecting heart abnormalities based on the characteristics of ECG signals using multi- layer perceptron with firefly algorithm and simulated annealing is 100%.
基于心电信号特征的多层感知器萤火虫算法-模拟退火检测心脏异常
心脏病是世界范围内导致死亡的原因之一。因此,检测心脏疾病对于降低不断上升的死亡率非常重要。用于检测心脏异常或紊乱的方法之一是使用计算机辅助确定心电图的特征。心电图(ECG)是一种通过附着在胸部、手臂和腿部皮肤上的小金属电极检测并记录心脏活动的测试。这个测试显示心脏跳动的速度和节奏是否稳定。本文的目的是将萤火虫算法与模拟退火相结合的多层感知器模型应用于基于心电信号特征的心脏异常检测。本研究的第一步是图像处理。心电图像处理的主要步骤是灰度化、阈值化、边缘检测、分割和归一化。将图像处理的结果作为输入矩阵,应用萤火虫算法和模拟退火对感知器多层网络进行训练。在训练过程中,我们将得到对测试数据进行验证测试的最优权值和偏差。本文的训练数据使用了20张心电图像,验证测试过程中使用了10张心电图像。验证试验的验证结果表明,基于心电信号特征的多层感知器萤火虫算法和模拟退火算法检测心脏异常的准确率为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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