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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >ECG Heartbeat Classification Based on Multi-Scale Wavelet Convolutional Neural Networks
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ECG Heartbeat Classification Based on Multi-Scale Wavelet Convolutional Neural Networks

机译：基于多尺度小波卷积神经网络的ECG心跳分类

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This paper proposes a novel Deep Learning technique for ECG beats classification. Unlike the traditional Deep Learning models, a new Multi-Scale Wavelet Convolutional Neural Networks (MS-WCNN) is proposed to recognize automatically various cardiac arrhythmias. The proposed MS-WCNN model incorporates the one dimensional CNN and the Stationary Wavelet Transform (SWT) to extract discriminative features from the ECG signal and its wavelet sub-bands simultaneously. The extracted features are then merged using a concatenation strategy. This improves greatly the features learning process of our model at different scales, providing better diagnosis performances. The MITBIH Arrhythmia database has been used to evaluate the performance of the developed model, considering five heartbeats classes: Non-ectopic beat, Supra ventricular ectopic beat, Ventricular ectopic beat, Fusion beat and Unknown beat. The obtained results show that the MS-WCNN method achieves higher or comparable performances with respect to the existing ECG classification algorithms, with an overall diagnosis accuracy of 99, 11%.
机译：本文提出了一种新的ECG击败分类的深入学习技术。与传统的深度学习模型不同，建议新的多尺度小波卷积神经网络（MS-WCNN）识别自动识别各种心律失常。所提出的MS-WCNN模型包括一维CNN和静止小波变换（SWT），以同时提取来自ECG信号的判别特征及其小波子带。然后使用串联策略合并提取的特征。这极大地改善了我们模型在不同尺度上的特征学习过程，提供了更好的诊断性能。 MITBIH心律失常数据库已被用于评估开发模型的性能，考虑五个心跳类：非异位搏动，上腔间卵搏，心室异位搏动，融合搏动和未知节拍。所得结果表明，MS-WCNN方法相对于现有的ECG分类算法实现了更高或比较的性能，整体诊断精度为99,11％。

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