Best Parameters Selection of Arrhythmia Classification Using Convolutional Neural Networks

Rizqi Hadi Prawira, A. Wibowo, Ajif Yunizar Pratama Yusuf
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Abstract

Arrhythmia are disturbances in the heart where the heart beats slower or faster. Some types of Arrhythmia can became a serious problem and life-threatening. Early detection of Arrhythmia is very crucial to patients. Tools that can be used to determine heart condition is Electrocardiogram (ECG). Deep learning methods can be used to classify types of Arrhythmia from ECG images. Convolutional Neural Network is one of deep learning methods that is often used to classify images. CNN-based model such as VGG, ResNet, and MobileNet has gotten success in images classification. Those models are using lots of convolution layer, so those models are easily run into over fitting problem if those are used in small dataset. CNN model in this research needs parameter adjustments to solve over fitting problem. Parameter that were being adjusted were learning rate, dropout rate, and the number of convolution layer. The testing results on CNN model showed that the best learning rate and dropout rate which produced the best model to classify Arrhythmia were 0.0001, and 0.0075 respectively. The number of convolution layers which obtained the best accuracy was 4. Classification using CNN model for Arrhythmia with learning rate, dropout rate, and number of convolution layers were 0.0001, 0.0075, and 4 respectively resulted in the best model with 94.2 % accuracy value.
基于卷积神经网络的心律失常分类最佳参数选择
心律失常是心脏的紊乱,心脏跳动变慢或变快。某些类型的心律失常会成为严重的问题并危及生命。心律失常的早期发现对患者至关重要。可用于确定心脏状况的工具是心电图(ECG)。深度学习方法可用于从心电图图像中分类心律失常的类型。卷积神经网络是深度学习中常用的图像分类方法之一。基于cnn的VGG、ResNet、MobileNet等模型在图像分类方面取得了成功。这些模型使用了大量的卷积层,如果用于小数据集,这些模型很容易遇到过拟合问题。本研究中的CNN模型需要参数调整来解决过拟合问题。调整的参数有学习率、辍学率、卷积层数。在CNN模型上的测试结果表明,产生最佳心律失常分类模型的最佳学习率和辍学率分别为0.0001和0.0075。获得最佳精度的卷积层数为4层。使用CNN模型对心律失常进行分类,其学习率为0.0001,辍学率为0.0075,卷积层数为4,准确率为94.2%。
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