Pesu L, Helistö P, Ademovic E, Pesquet J C, Saarinen A, Sovijärvi A R
Laboratory of Biomedical Engineering, Helsinki University of Technology, Finland.
Technol Health Care. 1998 Jun;6(1):65-74.
In this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.
本文采用基于小波包的方法来检测异常呼吸音。将声音信号分割成段，并利用寻找最佳小波包分解的结果形成用于分类的特征向量。使用学习向量量化将这些段分类为包含湿啰音、哮鸣音或正常肺音。该方法通过一小组真实患者数据进行测试，这些数据也由一位专家观察者进行了分析。初步结果很有前景，尽管尚未达到足以用于临床的程度。
