Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (2): 598-603.DOI: 10.11772/j.issn.1001-9081.2020060881

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles     Next Articles

Respiratory sound recognition of chronic obstructive pulmonary disease patients based on HHT-MFCC and short-term energy

CHANG Zheng, LUO Ping, YANG Bo, ZHANG Xiaoxiao   

  1. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2020-06-24 Revised:2020-09-29 Online:2021-02-10 Published:2020-12-18


常峥, 罗萍, 杨波, 张晓晓   

  1. 重庆邮电大学 自动化学院, 重庆 400065
  • 通讯作者: 常峥
  • 作者简介:常峥(1996-),男,河南新乡人,硕士研究生,主要研究方向:嵌入式系统、音频信号处理;罗萍(1971-),女,重庆人,副教授,硕士研究生,主要研究方向:嵌入式系统、自动化技术;杨波(1994-),男,湖北咸宁人,硕士研究生,主要研究方向:模糊控制;张晓晓(1994-),男,河南三门峡人,硕士研究生,主要研究方向:嵌入式系统。

Abstract: In order to optimize the Mel-Frequency Cepstral Coefficient (MFCC) feature extraction algorithm, improve the recognition accuracy of respiratory sound signals, and achieve the purpose of identifying Chronic Obstructive Pulmonary Disease (COPD), a feature extraction algorithm with the fusion of MFCC based on Hilbert-Huang Transform (HHT) and short-term Energy, named HHT-MFCC+Energy, was proposed. Firstly, the preprocessed respiratory sound signal was used to calculate the Hilbert marginal spectrum and marginal spectrum energy through HHT. Secondly, the spectral energy was passed through the Mel filter to obtain the eigenvector, and then the logarithm and discrete cosine transform of the eigenvector were performed to obtain the HHT-MFCC coefficients. Finally, the short-term energy of signal was fused with the HHT-MFCC eigenvector to form a new feature, and the signal was identified by Support Vector Machine (SVM). Three feature extraction algorithms including MFCC, HHT-MFCC and HHT-MFCC+Energy were combined with SVM to recognize the respiratory sound signal. Experimental results show that the proposed feature fusion algorithm has better respiratory sound recognition effect for both COPD patients and healthy people compared with the other two algorithms:the average recognition rate of the proposed algorithm can reach 97.8% on average when extracting 24-dimensional features and selecting 100 training samples, which is 6.9 percentage points and 1.4 percentage points higher than those of MFCC and HHT-MFCC respectively.

Key words: respiratory sound signal, Chronic Obstructive Pulmonary Disease (COPD), Hilbert-Huang Transform (HHT), short-term energy, feature fusion

摘要: 为了优化梅尔频率倒谱系数(MFCC)特征提取算法,提高对呼吸声信号识别的准确率,实现识别慢性阻塞性肺病(COPD)的目的,提出了基于希尔伯特黄变换(HHT)的MFCC与短时能量(Energy)融合的特征提取算法HHT-MFCC+Energy。首先,经预处理的呼吸声信号通过HHT计算出Hilbert边际谱和边际谱能量;其次,谱能量通过Mel滤波器得到特征向量,再对特征向量取对数和进行离散余弦变换得到HHT-MFCC系数;最后,将信号的短时能量与HHT-MFCC特征向量融合形成新特征,并通过支持向量机(SVM)进行信号识别。将MFCC、HHT-MFCC和HHT-MFCC+Energy三种特征提取算法结合SVM进行呼吸声信号识别,实验结果表明,所提出的特征融合算法在COPD患者和健康人呼吸声识别效果上都优于其他两种算法:当提取24维特征、选取100个训练样本时,识别率平均值能达到97.8%,分别比MFCC和HHT-MFCC高出6.9个百分点和1.4个百分点。

关键词: 呼吸声信号, 慢性阻塞性肺病, 希尔伯特黄变换, 短时能量, 特征融合

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