基于经验模态分解分析和隐马尔可夫模型识别的咳嗽音检测  

Cough Sound Detection Bases on EMD Analysis and HMM Recognition

作　　者：胡维平[1,3] 赖克方[2] 杜明辉[1] 陈如冲[2] 钟思军[1] 陈荣昌 钟南山[2] 

机构地区：[1]华南理工大学电子与信息学院,广州510640 [2]呼吸疾病国家重点实验室(广州医学院),广州呼吸疾病研究所,广州510120 [3]广西师范大学物理与电子工程学院,桂林541001

出　　处：《生物医学工程学杂志》2009年第2期277-281,共5页Journal of Biomedical Engineering

基　　金：广东省自然科学基金资助项目(05006593);广州市科技攻关重点资助项目(2004E3-E0541)

摘　　要：咳嗽是众多呼吸道疾病中常见的重要病症之一,其强度和发生频率提供了极其重要的临床信息。为利用这些信息,必须把咳嗽音从其他声音例如语音、清喉音、清鼻音等中分辨出来。我们提出一种基于经验模态分解(Empirical Mode Decomposition,EMD)分析的咳嗽音检测方法。该方法通过应用EMD的自适应滤波器组特性,提取信号的频域能量分布以统计分析咳嗽音及语音等特征,进而找到优化特征提取的方法,并利用隐马尔可夫模型(Hidden Markov model,HMM)进行咳嗽音的检测。临床数据的实验表明,该优化方法能有效提高咳嗽音检测的正确率。Cough is one of the most common symptoms of many respiratory diseases. the characteristics of intensity and frequency of cough sound offer important clinical messages. When using these messages, we have need to differentiate the cough sound from the other sounds such as speech voice, throat clearing sound and nose clearing sound. In this paper, based on Empirical Mode Decomposition （EMD） and Hidden Markov Model （HMM）, we pro-posed a novel method to analyze and detect cough sound. Employing the property of adaptive dyadic filter banks of EMD, we gained the mean energy distribution in the frequency domain of the signals in order to analyze the statistical characteristics of cough sound and of other sounds not accompanied by cough, and then we found the optimal characteristics for the recognition using HMM. The experiments on clinical date showed that this optimal characteristic method effectively improved the detective rate of cough sound.

关 键 词：咳嗽音检测 经验模态分解 隐马尔可夫模型 

分 类 号：R56[医药卫生—呼吸系统] R318[医药卫生—内科学]