基于Mel频率倒谱系数、动态时间归准的计算机肺音分析系统在健康儿童中的应用

Application of the computer-based respiratory sound analysis system based on Mel-frequency cepstral coefficient and dynamic time warping in healthy children

目的 探索基于Mel频率倒谱系数（MFCC）、动态时间归准（DTW）设计计算机肺音分析系统，对儿童肺音进行识别匹配，验证该肺音分析系统的有效性。 方法 设计计算机肺音分析系统，采用Lung Sounds Reference Guide的标准肺音（包括正常、异常肺音）及采集典型的湿啰音、鼾音、哮鸣音各1例（分别来自厦门大学附属第一医院儿科3例患儿）作为参考肺音；通过自行研制的便携式电子听诊器，对29例健康儿童肺音进行采集作为测试肺音。通过人工识别有效肺音片段及MFCC对测试肺音进行特征提取，而后对经MFCC处理后的肺音数据进行DTW模板匹配，最终得出识别结果。结果 实验获得参考肺音39个，测试肺音58个。实验共进行了58次肺音识别，正确识别52次，错误识别6次，识别正确率为89.7%。结论 基于MFCC、DTW的计算机肺音分析系统对健康儿童肺音（正常肺音）的识别是有效的。

Objective We designed a computer-based respiratory sound analysis system to identify pediatric normal lung sound. To verify the validity of the computer-based respiratory sound analysis system. Method First we downloaded the standard lung sounds from the network database （website： http：//www. easyauscultation, com/lung-sounds-reference-guide ） and recorded 3 samples of abnormal loud sound （rhonchi, wheeze and crackles） from three patients of The Department of Pediatrics, the First Affiliated Hospital of Xiamen University. We regarded such lung sounds as ＂reference lung sounds＂. The ＂ test lung sounds＂ were recorded from 29 children form Kindergarten of Xiamen University. we recorded lung sound by portable electronic stethoscope and valid lung sounds were selected by manual identification. We introduced Mel-frequency cepstral coefficient （MFCC） to extract lung sound features and dynamic time warping （DTW） for signal classification. Result We had 39 standard lung sounds, recorded 58 test lung sounds. This computer-based respiratory sound analysis system was carried out in 58 lung sound recognition, correct identification of 52 times, error identification 6 times. Accuracy was 89.7%. Conclusion Based on MFCC and DTW, our computer-based respiratory sound analysis system can effectively identify healthy lung sounds of children （ accuracy can reach 89.7% ） , fully embodies the reliability of the lung sounds analysis system.