电子学报 ›› 2023, Vol. 51 ›› Issue (3): 701-711.DOI: 10.12263/DZXB.20211181
吕杭1, 蒋明峰1, 李杨1, 张鞠成2(), 王志康2(
)
收稿日期:
2021-08-30
修回日期:
2022-07-09
出版日期:
2023-03-25
通讯作者:
作者简介:
基金资助:
LÜ Hang1, JIANG Ming-feng1, LI Yang1, ZHANG Ju-cheng2(), WANG Zhi-kang2(
)
Received:
2021-08-30
Revised:
2022-07-09
Online:
2023-03-25
Published:
2023-04-20
Corresponding author:
Supported by:
摘要:
心律失常是常见的心血管疾病之一,目前很多方法通过计算机辅助系统对心电图进行分析以识别心律失常,但由于大多数心律失常数据样本较少,计算机辅助系统识别心律失常效果不佳.本文提出了一种基于混合时频域分析特征提取的卷积神经网络方法,该方法提取心电图的RR间期时域特征、希尔伯特-黄变换提取的频域特征和连续小波变换提取的时频域联合特征,经过特征融合后输入卷积神经网络训练分类模型,并采用Focal Loss作为网路的损失函数,实现对心律失常的分类.本文使用MIT-BIH(Massachusetts Institute of Technology-Boston's Beth Israel Hospital)心律失常数据库验证本文提出方法对4类心电数据分类的结果,实验结果表明,与现有的分类算法相比,本文所提出的混合时频域特征方法能有效提升心律失常分类的准确性.
中图分类号:
吕杭, 蒋明峰, 李杨, 等. 基于混合时频域特征的卷积神经网络心律失常分类方法的研究[J]. 电子学报, 2023, 51(3): 701-711.
Hang Lü, Ming-feng JIANG, Yang LI, et al. Research on Arrhythmia Classification by Using Convolutional Neural Network with Mixed Time-Frequency Domain Features[J]. Acta Electronica Sinica, 2023, 51(3): 701-711.
N | S | V | F | Q |
---|---|---|---|---|
正常搏动(N) | 房性早搏(A) | 室性早搏(V) | 心室和正常搏动的融合(F) | 起博心跳 |
左束支传导阻滞搏(L) | 异常房性早搏(a) | 室性逸搏(E) | 起搏和正常搏动的融合(f) | |
右束支传导阻滞搏(R) | 交界性早搏(J) | 未分类搏动(Q) | ||
心房逸搏(e) | 室上性早搏或异位性搏动(S) | |||
交界性逸搏(j) |
表1 MIT/BIH数据集根据AAMI标准划分的5种心拍
N | S | V | F | Q |
---|---|---|---|---|
正常搏动(N) | 房性早搏(A) | 室性早搏(V) | 心室和正常搏动的融合(F) | 起博心跳 |
左束支传导阻滞搏(L) | 异常房性早搏(a) | 室性逸搏(E) | 起搏和正常搏动的融合(f) | |
右束支传导阻滞搏(R) | 交界性早搏(J) | 未分类搏动(Q) | ||
心房逸搏(e) | 室上性早搏或异位性搏动(S) | |||
交界性逸搏(j) |
数据集/标签 | N | S | V | F | 总 |
---|---|---|---|---|---|
DS1 | 45 824 | 3 788 | 943 | 414 | 50 969 |
DS2 | 44 218 | 3 219 | 1 836 | 388 | 49 661 |
表2 训练集(DS1)和测试集(DS2)ECG样本
数据集/标签 | N | S | V | F | 总 |
---|---|---|---|---|---|
DS1 | 45 824 | 3 788 | 943 | 414 | 50 969 |
DS2 | 44 218 | 3 219 | 1 836 | 388 | 49 661 |
网络层名 | 核尺寸 | 滤波器 | 填充 | 步长 | 输出形状 | 参数 |
---|---|---|---|---|---|---|
输入层1 | - | - | - | - | 100×250×1 | - |
卷积层 | 7×7 | 16 | vaild | 1 | 94×244×16 | 800 |
批归一化 | - | - | - | - | 94×244×16 | 64 |
最大池化 | 5×5 | - | - | - | 18×48×16 | - |
卷积层 | 3×3 | 32 | vaild | 1 | 16×46×32 | 4 640 |
批归一化 | - | - | - | - | 16×46×32 | 128 |
最大池化 | 3×3 | - | - | - | 5×15×32 | - |
卷积层 | 3×3 | 64 | vaild | 1 | 3×13×64 | 18 496 |
批归一化 | - | - | - | - | 3×13×64 | 256 |
最大池化 | 3×3 | - | - | - | 1×4×64 | - |
全连接层 | - | - | - | - | 1×4×16 | 1 040 |
展开层 | - | - | - | - | 64 | - |
输入层2 | - | - | - | - | 4 | - |
输入层3 | - | - | - | - | 8×248×1 | - |
卷积层 | 7×7 | 16 | same | 1 | 8×248×16 | 800 |
批归一化 | - | - | - | - | 8×248×16 | 64 |
最大池化 | 2×5 | - | - | - | 4×49×16 | - |
卷积层 | 3×3 | 32 | same | 1 | 4×49×32 | 4 640 |
批归一化 | - | - | - | - | 4×49×32 | 128 |
最大池化 | 2×3 | - | - | - | 2×16×32 | - |
卷积层 | 3×3 | 64 | same | 1 | 2×16×64 | 18 496 |
批归一化 | - | - | - | - | 2×16×64 | 256 |
最大池化 | 2×3 | - | - | - | 1×5×64 | - |
全连接层 | - | - | - | - | 1×5×16 | 1 040 |
展开层 | - | - | - | - | 80 | - |
融合层 | - | - | - | - | 148 | - |
全连接层 | - | - | - | - | 32 | 4 768 |
softmax层 | - | - | - | - | 4 | 132 |
表3 CNN分类模型的参数
网络层名 | 核尺寸 | 滤波器 | 填充 | 步长 | 输出形状 | 参数 |
---|---|---|---|---|---|---|
输入层1 | - | - | - | - | 100×250×1 | - |
卷积层 | 7×7 | 16 | vaild | 1 | 94×244×16 | 800 |
批归一化 | - | - | - | - | 94×244×16 | 64 |
最大池化 | 5×5 | - | - | - | 18×48×16 | - |
卷积层 | 3×3 | 32 | vaild | 1 | 16×46×32 | 4 640 |
批归一化 | - | - | - | - | 16×46×32 | 128 |
最大池化 | 3×3 | - | - | - | 5×15×32 | - |
卷积层 | 3×3 | 64 | vaild | 1 | 3×13×64 | 18 496 |
批归一化 | - | - | - | - | 3×13×64 | 256 |
最大池化 | 3×3 | - | - | - | 1×4×64 | - |
全连接层 | - | - | - | - | 1×4×16 | 1 040 |
展开层 | - | - | - | - | 64 | - |
输入层2 | - | - | - | - | 4 | - |
输入层3 | - | - | - | - | 8×248×1 | - |
卷积层 | 7×7 | 16 | same | 1 | 8×248×16 | 800 |
批归一化 | - | - | - | - | 8×248×16 | 64 |
最大池化 | 2×5 | - | - | - | 4×49×16 | - |
卷积层 | 3×3 | 32 | same | 1 | 4×49×32 | 4 640 |
批归一化 | - | - | - | - | 4×49×32 | 128 |
最大池化 | 2×3 | - | - | - | 2×16×32 | - |
卷积层 | 3×3 | 64 | same | 1 | 2×16×64 | 18 496 |
批归一化 | - | - | - | - | 2×16×64 | 256 |
最大池化 | 2×3 | - | - | - | 1×5×64 | - |
全连接层 | - | - | - | - | 1×5×16 | 1 040 |
展开层 | - | - | - | - | 80 | - |
融合层 | - | - | - | - | 148 | - |
全连接层 | - | - | - | - | 32 | 4 768 |
softmax层 | - | - | - | - | 4 | 132 |
方法 | 类别 | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|
RR CWT | N | 0.981 5 | 0.945 7 | 0.963 3 | 0.931 1 |
S | 0.589 8 | 0.749 5 | 0.660 1 | ||
V | 0.690 8 | 0.946 6 | 0.798 7 | ||
F | 0.000 0 | 0.000 0 | 0.000 | ||
RR CWT Focal Loss | N | 0.980 7 | 0.959 9 | 0.970 2 | 0.944 7 |
S | 0.827 7 | 0.800 7 | 0.814 0 | ||
V | 0.773 0 | 0.932 0 | 0.845 1 | ||
F | 0.000 0 | 0.000 0 | 0.000 0 | ||
RR HHT | N | 0.973 2 | 0.960 8 | 0.966 9 | 0.921 6 |
S | 0.539 9 | 0.324 6 | 0.405 4 | ||
V | 0.742 5 | 0.833 2 | 0.785 2 | ||
F | 0.000 0 | 0.000 0 | 0.000 0 | ||
RR HHT Focal Loss | N | 0.960 2 | 0.959 8 | 0.975 5 | 0.933 0 |
S | 0.601 0 | 0.377 5 | 0.499 4 | ||
V | 0.775 4 | 0.819 1 | 0.789 7 | ||
F | 0.000 0 | 0.000 0 | 0.000 0 | ||
RR CWT HHT | N | 0.977 6 | 0.926 8 | 0.951 5 | 0.936 5 |
S | 0.498 3 | 0.648 7 | 0.563 7 | ||
V | 0.843 5 | 0.939 1 | 0.888 7 | ||
F | 0.002 3 | 0.010 3 | 0.003 7 | ||
RR CWT HHT Focal Loss | N | 0.984 3 | 0.947 3 | 0.965 5 | 0.946 7 |
S | 0.547 3 | 0.802 8 | 0.650 9 | ||
V | 0.928 9 | 0.978 3 | 0.952 9 | ||
F | 0.003 9 | 0.010 3 | 0.005 7 |
表4 不同方法下的评价指标
方法 | 类别 | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|
RR CWT | N | 0.981 5 | 0.945 7 | 0.963 3 | 0.931 1 |
S | 0.589 8 | 0.749 5 | 0.660 1 | ||
V | 0.690 8 | 0.946 6 | 0.798 7 | ||
F | 0.000 0 | 0.000 0 | 0.000 | ||
RR CWT Focal Loss | N | 0.980 7 | 0.959 9 | 0.970 2 | 0.944 7 |
S | 0.827 7 | 0.800 7 | 0.814 0 | ||
V | 0.773 0 | 0.932 0 | 0.845 1 | ||
F | 0.000 0 | 0.000 0 | 0.000 0 | ||
RR HHT | N | 0.973 2 | 0.960 8 | 0.966 9 | 0.921 6 |
S | 0.539 9 | 0.324 6 | 0.405 4 | ||
V | 0.742 5 | 0.833 2 | 0.785 2 | ||
F | 0.000 0 | 0.000 0 | 0.000 0 | ||
RR HHT Focal Loss | N | 0.960 2 | 0.959 8 | 0.975 5 | 0.933 0 |
S | 0.601 0 | 0.377 5 | 0.499 4 | ||
V | 0.775 4 | 0.819 1 | 0.789 7 | ||
F | 0.000 0 | 0.000 0 | 0.000 0 | ||
RR CWT HHT | N | 0.977 6 | 0.926 8 | 0.951 5 | 0.936 5 |
S | 0.498 3 | 0.648 7 | 0.563 7 | ||
V | 0.843 5 | 0.939 1 | 0.888 7 | ||
F | 0.002 3 | 0.010 3 | 0.003 7 | ||
RR CWT HHT Focal Loss | N | 0.984 3 | 0.947 3 | 0.965 5 | 0.946 7 |
S | 0.547 3 | 0.802 8 | 0.650 9 | ||
V | 0.928 9 | 0.978 3 | 0.952 9 | ||
F | 0.003 9 | 0.010 3 | 0.005 7 |
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