Hashemi Amjad, Arabalibeik Hossein, Agin Khosrow
Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences, Tehran, Iran.
Stud Health Technol Inform. 2012;173:161-5.
Wheezes are abnormal, continuous sounds heard over large airways and chest. They are divided to two groups based on relative intensity of airway obstruction. They are usually heard in asthma, pneumonia, emphysema and chronic obstructive pulmonary diseases (COPD). We present a classification method to discriminate between polyphonic and monophonic wheeze sounds using multilayer perceptron (MLP) neural network and mel-frequency cepstral coefficients (MFCC). Wheeze signals are divided to segments with 50% overlap. MFCC features are then extracted. Groups with different numbers of MFCC powerful features are compared by receiver operating characteristic (ROC) curves. The test results show an accuracy of 92.8%.
哮鸣音是在大气道和胸部听到的异常连续性声音。根据气道阻塞的相对强度，它们可分为两组。通常在哮喘、肺炎、肺气肿和慢性阻塞性肺疾病（COPD）中听到。我们提出一种使用多层感知器（MLP）神经网络和梅尔频率倒谱系数（MFCC）来区分多音调和单音调哮鸣音的分类方法。哮鸣音信号被分割成重叠率为50%的片段。然后提取MFCC特征。通过接收者操作特征（ROC）曲线比较具有不同数量MFCC强特征的组。测试结果显示准确率为92.8%。
