A new deep learning model for assisted diagnosis on electrocardiogram

Eric Ke Wang; liu Xi; Ruipei Sun; Fan Wang; Leyun Pan; Caixia Cheng; Antonia Dimitrakopoulou-Srauss; Nie Zhe; Yueping Li; Eric Ke Wang; liu Xi; Ruipei Sun; Fan Wang; Leyun Pan; Caixia Cheng; Antonia Dimitrakopoulou-Srauss; Nie Zhe; Yueping Li

doi:10.3934/mbe.2019124

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 4: 2481-2491. doi: 10.3934/mbe.2019124

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Research article Special Issues

A new deep learning model for assisted diagnosis on electrocardiogram

1.
Harbin Institute of Technology, Shenzhen, 518055, China
2.
German Cancer Research Center, 69120 Heidelberg, Germany
3.
School of Computer Engineering, Shenzhen Polytechnic, Shenzhen, China

Received: 18 November 2018 Accepted: 05 March 2019 Published: 22 March 2019

In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep learning model called CBRNN to assist diagnosis on electrocardiogram for clinical medical service. It combines two sub networks which are convolutional neural network (CNN) and bi-directional recurrent neural network (BRNN). In the model, CNN with one-dimension convolution is employed to extract features for each lead of ECG, and BRNN is used to fuse features of different leads to represent deeper features. In the training step, we use more than 40 thousand training data and more than 19 thousand validation data to obtain the optimal parameters of the model. Besides, by validating our model on more than CCDD 120, 000 real data, it achieves an 87.69% accuracy rate, higher than popular deep learning models such as CNN and ResNet. Our model has better accuracy than state-of-the-art models and it is also slightly higher than the average accuracy of human judgement. It can be served for the first round screening of ECG examination clinical diagnosis.
- clinical medicine,
- electrocardiogram,
- multi-lead,
- convolutional neural network,
- bi-directional recurrent neural network
Citation: Eric Ke Wang, liu Xi, Ruipei Sun, Fan Wang, Leyun Pan, Caixia Cheng, Antonia Dimitrakopoulou-Srauss, Nie Zhe, Yueping Li. A new deep learning model for assisted diagnosis on electrocardiogram[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2481-2491. doi: 10.3934/mbe.2019124

Related Papers:

Abstract

In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep learning model called CBRNN to assist diagnosis on electrocardiogram for clinical medical service. It combines two sub networks which are convolutional neural network (CNN) and bi-directional recurrent neural network (BRNN). In the model, CNN with one-dimension convolution is employed to extract features for each lead of ECG, and BRNN is used to fuse features of different leads to represent deeper features. In the training step, we use more than 40 thousand training data and more than 19 thousand validation data to obtain the optimal parameters of the model. Besides, by validating our model on more than CCDD 120, 000 real data, it achieves an 87.69% accuracy rate, higher than popular deep learning models such as CNN and ResNet. Our model has better accuracy than state-of-the-art models and it is also slightly higher than the average accuracy of human judgement. It can be served for the first round screening of ECG examination clinical diagnosis.

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