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Method and Apparatus for ECG Arrhythmia Classification using a Deep Convolutional Neural Network

机译:使用深度卷积神经网络进行ECG心律失常分类的方法和设备

摘要

A method and apparatus for classifying an electrocardiogram arrhythmia using a deep convolutional neural network are presented. The electrocardiogram arrhythmia classification method using a deep convolutional neural network proposed in the present invention includes a step of converting a 1D ECG signal into a 2D ECG image and reinforcing the converted 2D ECG image using a learning technique, and reducing overfitting. And expanding the training data by performing normalization for, measuring a degree of training of the neural network using an objective function, and performing classification for a plurality of ECG types through a CNN classifier.
机译:提出了一种使用深度卷积神经网络对心律失常进行分类的方法和设备。在本发明中提出的使用深度卷积神经网络的心电图心律不齐分类方法包括以下步骤:将一维ECG信号转换为二维ECG图像,并使用学习技术来增强转换后的二维ECG图像,并减少过度拟合。并且,通过执行归一化,使用目标函数测量神经网络的训练程度,以及通过CNN分类器对多种ECG类型进行分类来扩展训练数据。

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