基于改进深度残差收缩网络的心电信号分类算法

龚玉晓; 高淑萍

doi:10.21656/1000-0887.440074

基于改进深度残差收缩网络的心电信号分类算法

doi: 10.21656/1000-0887.440074

龚玉晓,
高淑萍^,

西安电子科技大学数学与统计学院，西安 710126

基金项目:

国家自然科学基金项目 91338115

高等学校学科创新引智计划(111计划) B08038

详细信息

作者简介:
龚玉晓(1997—)，女，硕士生(E-mail: 986809674@qq.com)

通讯作者:
高淑萍(1963—)，女，教授，博士，硕士生导师(通讯作者. E-mail: gaosp@mail.xidian.edu.cn)

中图分类号: O29;TP183;TN911.7
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- 文章访问数: 459
- HTML全文浏览量: 150
- PDF下载量: 67
- 被引次数: 0
出版历程
- 收稿日期: 2023-03-22
- 修回日期: 2023-06-19
- 刊出日期: 2023-08-01

An Electrocardiogram Signal Classification Algorithm Based on Improved Deep Residual Shrinkage Networks

GONG Yuxiao,
GAO Shuping^,

School of Mathematics and Statistics, Xidian University, Xi'an 710126, P.R.China

摘要

摘要: 心电信号分类是医疗保健领域的重要研究内容. 针对大多数方法不能很好地降低样本数量少的类别漏诊率，以及降低预处理操作的复杂性问题，提出了一种基于改进深度残差收缩网络(IDRSN)的心电信号分类算法(即DRSL算法). 首先，使用合成少数类过采样技术(SMOTE)扩充数量少的类别样本，从而解决了类不平衡问题；其次，利用改进深度残差收缩网络提取空间特征，其残差模块可以避免网络层加深造成的过拟合，压缩激励和软阈值化子网络可以提取重要局部特征并自动去除噪声；然后，通过长短期记忆网络(LSTM)提取时间特征；最后，利用全连接网络输出分类结果. 在MIT-BIH心律失常数据集上的实验结果表明，该算法的分类性能优于IDRSN、DRSN、GAN+2DCNN、CNN+LSTM_ATTENTION、SE-CNN-LSTM分类算法.
- 心电信号 /
- 合成少数类过采样技术 /
- 深度残差收缩网络 /
- 压缩激励 /
- 长短期记忆网络
Abstract: The electrocardiogram (ECG) signal classification is a significant research topic in the healthcare field. Most existing methods could not effectively reduce the missed diagnosis rate of classification with small-size samples and tackle the complexity of preprocessing operations. An electrocardiogram signal classification algorithm based on the improved deep residual shrinkage networks was proposed, namely the DRSL algorithm. Firstly, the small-size classification samples were augmented with the synthetic minority over-sampling technique to solve the classification imbalance problem. Secondly, the spatial features were extracted by mean of the improved deep residual shrinkage networks, where the residual module can avoid overfitting caused by deepening of network layers, and the squeeze-and-excitation operation with soft threshold subnetwork can extract important local features and remove noises automatically. Then, the time features were extracted with the long short-term memory networks. Finally, the classification results were output with the fully connected neural networks. The experimental results on the MIT-BIH arrhythmia database show that, the proposed algorithm is superior to IDRSN, DRSN, GAN+2DCNN, CNN+LSTM_ATTENTION, SE-CNN-LSTM in terms of classification performances.
- ECG signal /
- synthetic minority over-sampling technique /
- deep residual shrinkage network /
- squeeze-and-excitation /
- long short-term memory network

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图 1 DRSN结构图

Figure 1. The structure of DRSN

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图 2 LSTM单元内部结构图

Figure 2. The internal structure of an LSTM unit

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图 3 IDRSN结构图

Figure 3. The structure of IDRSN

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图 4 DRSL算法流程图

Figure 4. The flow chart of the DRSL algorithm

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图 5 Loss值与迭代次数的关系

Figure 5. Relationships between the training loss and the number of iteration times

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图 6 准确率与迭代次数的关系

Figure 6. Relationships between the accuracy and the number of iteration times

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图 7 混淆矩阵

Figure 7. Confusion matrix

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表 1 AAMI标准以及4种心拍类别的样本数量

Table 1. The AAMI standards and the number of heartbeats in the 4 classes

heartbeat category of the AAMI	heartbeat category of the MIT-BIH database	expert annotation code	number
normal heartbeat (N)	normal beat (N) left bundle branch block beat (L) right bundle branch block beat (R) nodal (junctional) escape beat (j) atrial escape beat (e)	1, 2, 3, 11, 34	90 004
supraventricular ectopic heartbeat (S)	aberrated atrial premature beat (a) nodal (junctional) premature beat (J) atrial premature beat (A) supraventricular premature beat (S)	4, 7, 8, 9	2 778
ventricular ectopic heartbeat (V)	premature ventricular contraction (V) ventricular escape beat (E)	5, 10	7 004
fusion heartbeat (F)	fusion of ventricular and normal beat (F)	6	802
total			100 588

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表 2 类别平衡前后的数据集

Table 2. Datasets before and after class balancing

	N	S	V	F
training set	14 000	1 944	4 903	562
balanced training set	14 000	14 000	14 000	14 000
validation set	2 000	278	700	80
test set	4 000	556	1 401	160

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表 3 DRSL网络的超参数设置

Table 3. Hyperparameter settings for the DRSL network

layer type	network parameter value	training parameter value
CNN max pooling IDRSN(a)+IDRSN(b) IDRSN(a)+IDRSN(b) IDRSN(a)+IDRSN(b) IDRSN(a)+IDRSN(b) IDRSN(a)+IDRSN(b) LSTM global average pooling dense dense	F_filter=8, D_cs=3, padding=“same”, kernel_regularizer=L2(0.000 1) pooling_size=(2, 2), strides=(2, 2), a=“tanh” C_out=8 C_out=16 C_out=32 C_out=64 C_out=128 unit is 32, a=“tanh” a=“LeakyReLU” unit is 32 unit is 4, a=“softmax”, kernel_regularizer=L2(0.000 1)	epochs is 20 batch_size is 64 optimizer=“Adam” initial_learning_rate=0.002 each additional 10 epochs, the learning rate decreased to 0.1 times

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表 4 DRSL算法在测试集上的分类结果

Table 4. The classification results of the DRSL algorithm on the test set

	δ_Acc/%	δ_Sen/%	δ_Spec/%	δ_PPV/%
N	98.53	98.80	98.02	98.95
S	99.17	95.50	99.53	95.33
V	98.92	97.64	99.30	97.64
F	99.30	88.13	99.60	85.45
overall classification performance	98.98	95.02	99.11	94.34

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表 5 DRSL算法与其消融实验的总体分类性能比较

Table 5. The overall classification performance comparison between the DRSL algorithm and its ablation experiments

classification algorithm	overall classification performance
classification algorithm	average δ_Acc/%	average δ_Sen/%	average δ_Spec/%	average δ_PPV/%
DRSL	98.98	95.02	99.11	94.34
IDRSN	98.94	94.60	99.00	93.93
DRSN	98.79	93.88	98.85	94.71

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表 6 DRSL算法与其他算法的总体分类性能比较

Table 6. The overall classification performance comparison between the DRSL algorithm and other algorithms

classification algorithm	denoising and sample balancing methods	overall classification performance
classification algorithm	denoising and sample balancing methods	average δ_Acc/%	average δ_Sen/%	average δ_Spec/%	average δ_PPV/%
DRSL	null+SMOTE	98.98	95.02	99.11	94.34
GAN+2DCNN^[9]	null+GAN	98.41	93.68	98.85	92.65
CNN+LSTM_ATTENTION^[11]	wavelet threshold+SMOTE	98.38	95.80	98.58	92.35
SE-CNN-LSTM^[12]	wavelet threshold+SMOTE	97.69	89.56	97.86	89.27

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表 7 DRSL算法与其他算法的S类分类性能比较

Table 7. The classification performance comparison in class S between the DRSL algorithm and other algorithms

classification algorithm	class S classification performance
classification algorithm	δ_Acc/%	δ_Sen/%	δ_Spec/%	δ_PPV/%
DRSL	99.17	95.50	99.53	95.33
IDRSN	99.04	92.99	99.64	96.28
DRSN	98.61	92.81	99.19	91.98
GAN+2DCNN^[9]	98.05	95.50	98.30	84.69
CNN+LSTM_ATTENTION^[11]	98.28	94.24	98.69	87.77
SE-CNN-LSTM^[12]	97.47	87.05	98.51	85.36

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