基于单导肺音信号的呼吸气相识别

Respiratory phase detection based on one lung sound signal

为进行不用测量气流的自动肺音识别,提出了一种基于单导肺音信号的呼吸气相检测算法。在肺音平均功率谱上定位出吸气顶点和呼吸气相切换点。通过吸气顶点,来判断气相模式,为肺音特征分析提供时域定位标准。经37例实验数据验证,结果表明:全自动模式下正常肺音分相准确率为85.7%,半自动模式下的准确率达92.3%。该算法可有效地识别呼吸气相,可简化肺音研究。

A respiratory phase detection method was developed for automatic lung sound recognition without measuring the airflow. The transition points between the respiration phases and the inspiration peaks were located using the average power spectrum of lung sound. The respiratory phase pattern was evaluated from the respiration peaks to provide the time domain localization reference for lung sound feature analyzation. The method was verified using 37 recorded lung sounds. The results show that phase detection accuracy is 85.7% in the fully-automated mode and 92.3% in the semi-automated mode for normal lung sounds. Thus, the algorithm can accurately detect the respiratory phase and simplify lung research.