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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (10): 1912-1923    DOI: 10.3785/j.issn.1008-973X.2022.10.003
    
Classification method for electrocardiograph signals based on parallel architecture model and spatial-temporal attention mechanism
Xiang-dong PENG(),Cong-cheng PAN(),Ze-jun KE,Hua-qiang ZHU,Xiao ZHOU
School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China
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Abstract  

A parallel architecture electrocardiograph (ECG) classification model based on deep learning was proposed in order to effectively extract the spatiotemporal characteristics of ECG signals and improve the classification accuracy. A spatiotemporal attention mechanism based on gate channel attention block (GCA block) and gate time step attention (GTSA block) module was adopted in order to achieve multi-channel feature fusion. The bidirectional long-short time memory network and the convolutional neural network were used as the base feature extractor. The before-after dependence of the ECG signal sequence data and the local correlation features at different scales were captured respectively, and the automatic classification of five different types of ECG signals was realized. Results verified on the MIT-BIH dataset showed that the accuracy, specificity, sensitivity, accuracy and Macro-F1 of the total classification of five different ECG signals by the method were 99.50%, 99.61%, 96.20%, 98.02% and 97.08%, respectively. The model can not only effectively shorten the depth of the network model and prevent the model from overfitting, but also more accurately extract the spatiotemporal characteristics of the ECG signal and obtain better classification performance compared with other ECG classification models.

Key words： electrocardiograph classification      data imbalanced      deep learning      parallel architecture      spatiotemporal attention mechanism     
Received: 06 March 2022      Published: 25 October 2022
CLC:  TP 183  
Fund:  江西省自然科学基金资助项目（20192BAB207003）；江西省教育厅科学技术研究资助项目（GJJ180263）
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Xiang-dong PENG
Cong-cheng PAN
Ze-jun KE
Hua-qiang ZHU
Xiao ZHOU
Cite this article:

Xiang-dong PENG,Cong-cheng PAN,Ze-jun KE,Hua-qiang ZHU,Xiao ZHOU. Classification method for electrocardiograph signals based on parallel architecture model and spatial-temporal attention mechanism. Journal of ZheJiang University (Engineering Science), 2022, 56(10): 1912-1923.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.10.003     OR     https://www.zjujournals.com/eng/Y2022/V56/I10/1912


基于并行架构和时空注意力机制的心电分类方法

为了有效提取心电信号 (ECG) 的时空特征和提高分类准确性，提出基于深度学习的并行架构心电分类模型. 该模型采用基于GCA Block和GTSA Block模块实现多路特征融合的时空注意力机制. 使用双向长短时记忆网络和卷积神经网络作为基特征提取器，分别捕捉心电信号序列数据的前后依赖关系和不同尺度上的局部相关特征，实现对5种不同类型的心电信号的自动分类. 在MIT-BIH数据集上验证的结果表明，该方法对5种不同心电信号的总体分类准确率、特异性、敏感度、精确度和Macro-F1分别为99.50%、99.61%、96.20%、98.02%和97.08%. 相较于其他心电分类模型，该模型不仅能够有效地缩短网络模型深度，防止模型过拟合，而且能够更准确地提取心电信号的时空特征，获得更好的分类性能.


关键词： 心电分类,  数据不平衡,  深度学习,  并行架构,  时空注意力机制 
Fig.1 ECG signal comparison with noise reduction using wavelet method
Fig.2 Five types of heart beat morphology
Fig.3 Proportion of ECG dataset split
心律类型 Nb
训练集 测试集 总计
N 72379 18140 90519
S 2206 551 2757
V 5762 1378 7140
F 629 171 800
Q 6424 1610 8034
Tab.1 Comparison of number of heartbeats in training and test sets before data augment
心律类型 Nb
数据增强前 数据增强后
N 72379 72379
S 2206 3152
V 5762 7365
F 629 2617
Q 6424 8593
Tab.2 Comparison of number of heartbeats in training set after data augment
Fig.4 Structure of parallel architecture model
类型 网络层 激活函数 卷积核数 神经元数 步长
输入 输入层 — — 340×1 —
输入 1D Conv + BN Relu 32 7×1 1
大尺度 最大池化层 — — 3×1 2
大尺度 GCA Block×4 — — — —
大尺度 1D Conv + BN Relu 32 5×1 1
小尺度 最大池化层 — — 2×1 2
小尺度 GCA Block×3 — — — —
小尺度 Bi-LSTM+BN Tanh — 170×1 —
Bi-LSTM GTSA Block — — — —
Bi-LSTM GAP — — — —
特征融合 Concate — — — —
特征融合 全连接层 Relu — 256×1 —
特征融合 Dropout — — — —
输出 全连接层 Relu — 128×1 —
输出 Addition — — — —
输出 全连接层 Softmax — 5×1 —
Tab.3 Parameters of parallel architecture model
Fig.5 Overall structure of Bi-LSTM
Fig.6 Structure of GCA Block
Fig.7 Structure of GTSA Block
Fig.8 Accuracy and loss curves of heartbeat classification during training and test validation
Fig.9 Confusion matrix of heartbeat classification
心律类型 Spe/% Sen/% Pre/% F1/%
N 98.25 99.84 99.64 99.74
S 99.90 94.37 95.94 95.15
V 99.94 98.19 99.12 98.65
F 99.97 88.89 95.60 92.12
Q 99.99 99.81 99.81 99.75
Tab.4 Classification results over different evaluation metrics
Fig.10 Effect of data enhancement on effectiveness of ECG signal classification
方法 类别 方法 OA/% Spe/% Sen/% Macro-F1/%
文献[19]方法 5 CNN-LSTM 98.10 98.70 97.50 —
文献[30]方法 5 FCMDBN 96.54 — 98.32 —
文献[31]方法 8 4-layer LSTM 99.26 99.14 99.26 —
文献[32]方法 5 CAE + LSTM 99.23 — — —
文献[20]方法 5 STFT + 2-DCNN 99.0 — — —
文献[18]方法 5 CNN + BLSTM 95.90 — 95.90 95.92
文献[21]方法 4 CNN + BLSTM 99.56 99.47 95.90 96.40
文献[27]方法 5 HCRNet 98.70 — 99.28 99.38
本文方法 5 PSTA- Net 99.50 99.61 96.20 97.08
Tab.5 Comparison of classification performance by proposed network and other methods
Fig.11 Heat-map of five types of ECG signals
心律类型 Spe/% Sen/% Pre/% F1/%
N 98.15 98.31 99.43 98.87
S 97.41 92.05 93.14 92.59
V 99.37 97.11 98.13 97.62
F 98.42 85.07 94.39 89.49
Q 99.71 98.27 99.43 98.85
Tab.6 Beat classification results of small-scale methods
心律类型 Spe/% Sen/% Pre/% F1/%
N 99.21 99.12 99.39 99.25
S 98.54 93.29 93.42 93.36
V 99.48 96.75 98.73 97.73
F 98.53 85.36 94.71 89.79
Q 99.69 99.07 99.28 99.17
Tab.7 Beat classification results of large-scale methods
心律类型 Spe/% Sen/% Pre/% F1/%
N 99.34 99.33 99.66 99.49
S 98.29 94.02 94.98 94.50
V 99.53 97.01 99.03 98.01
F 99.66 85.73 95.17 90.20
Q 99.81 99.19 99.31 99.25
Tab.8 Beat classification results of multi-scale methods
心律类型 Sen/% F1/% Spe/%
N ±0.24 ±0.27 ±0.13
S ±0.41 ±0.52 ±0.24
V ±0.39 ±0.57 ±0.19
F ±0.87 ±0.93 ±0.95
Q ±0.23 ±0.21 ±0.15
Tab.9 Model classification performance fluctuation range based on 5-fold cross-validation
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