Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1818-1825.doi: 10.13229/j.cnki.jdxbgxb20190504

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Pulse wave signal classification algorithm based on time⁃frequency domain feature aliasing using convolutional neural network

Guo-hua LIU1,2(),Wen-bin ZHOU1,2   

  1. 1.School of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
    2.Key Laboratory of Photoelectric Sensors and Sensor Network Technology, Nankai University, Tianjin 300350, China
  • Received:2019-05-22 Online:2020-09-01 Published:2020-09-16

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Abstract:

A pulse wave classification algorithm of low complexity based on aliasing of time-frequency domain feature was proposed to solve the problems of low recognition rate and complex implementation in current research of pulse wave signal recognition. First, the time domain features of the pulse wave signal are extracted based on the Convolutional Neural Network(CNN), including the single-period feature characterizing the features of the signal segments in the period and the multi-period features characterizing the relationship between the cycles. Then the features in the frequency domain are expressed by the Mel cepstrum coefficients based on the wavelet transform. Finally, based on the aliasing and redundancy elimination of the time-frequency domain features using the neural network full connection layer, the pulse wave signal classification is realized by the Softmax classifier. The method can achieve feature extraction through low computational cost, due to the weight sharing and dimensionality reduction of CNN. In the simulation experiment based on Python platform, the recognition accuracy of the proposed method can reach 93%, which is much higher than the accuracy of traditional recognition algorithms based on time domain features or frequency domain features.

Key words: information processing technology, classification of pulse wave signal, convolutional neural network, aliasing of time-frequency domain features, wavelet transform

CLC Number: 

  • TN911.7

Fig.1

Extraction module of single-cycle feature"

Table 1

Parameter configuration of pulse wave signal single-cycle feature extraction module"

卷积模块1参数卷积模块2参数
卷积层卷积核维度7*1*1卷积层卷积核维度5*1*32
卷积核步长3*1*1卷积核步长2*1*1
卷积核个数32卷积核个数64
激活函数ReLU激活函数ReLU
池化层池化方式最大池化池化层池化方式最大池化
卷积核维度3*1*1卷积核维度2*1*1
卷积核步长2*1*1卷积核步长1*1*1

Fig.2

Extraction module of multi-cycle feature"

Table 2

Parameter configuration of pulse wave signal multi-cycle feature extraction module"

卷积模块3参数卷积模块4参数
卷积层卷积核维度7*3*1卷积层卷积核维度5*3*32
卷积核步长3*1*1卷积核步长2*1*1
卷积核个数32卷积核个数64
激活函数ReLU激活函数ReLU
池化层池化方式最大池化池化层池化方式最大池化
卷积核维度3*1*1卷积核维度2*1*1
卷积核步长2*1*1卷积核步长1*1*1

Fig.3

Frequency domain feature extraction module"

Fig.4

Algorithm structure of pulse wave classification"

Table 3

Parameter configuration of classification network"

网络结构参 数
神经元个数(全连接层1)128
神经元个数(全连接层2)64
激活函数ReLU函数
神经元个数(输出层)K
激活函数(输出层)Softmax函数
交叉验证算法留出法
代价函数交叉熵
网络更新算法动量梯度下降

Fig.5

Waveform comparison before and after pulse wave noise reduction"

Table 4

Test results of training set, test set, and verification set for classification network"

项目识别正确数识别错误数识别准确率
训练集407250.9421
验证集134100.9306
测试集13590.9375

Table 5

effect of different characteristics on classification accuracy of pulse wave signals"

项目识别正确数识别错误数识别准确率
单周期特征121230.8403
多周期特征125190.8681
单周期与多周期混合特征130140.9028
基于小波分析的MFCC75690.5208
时频域混叠特征13590.9375

Table 6

Performance comparison of this method with other classification methods"

方法识别准确率输入维度
本文0.937 53 042
文献[8]0.838 117 000

Fig.6

Influence of number of data sets in training set on recognition accuracy"

Table 7

Effect of different activation functions on performance of classification networks"

项目ReLUSigmoidTanh
训练集识别率0.94210.87960.8750
测试集识别率0.93750.87500.8681
训练时间(100次)/s27.430.729.12
迭代次数8431875996
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