ABSTRACT
Objective:To explore a fast and accurate method to diagnose children's pneumonia according to respiratory signals, so as to avoid the cancer induction caused by traditional X-ray examination.Methods:A Mach Zehnder optical fiber sensor was used to build a respiratory signals(RSPs) detection system, and the RSPs of the monitored children were extracted according to the vibration signal generated by the children's lung rales. Preprocessing methods such as the discrete cosine transform(DCT) were used to compress and denoise the RSPs. Multi-feature extraction of RSPs was conducted through signal processing methods such as the Hilbert transform and autoregressive (AR) model spectrum estimation. A support vector machine (SVM) classification model was constructed to classify the collected RSPs.Results:The accuracy rate of the proposed RSP classification of children with or without pneumonia was 94.41%, which was higher than the previous methods.Conclusions:The children's pneumonia diagnosis system based on an optical fiber sensor has a higher detection accuracy, and is expected to be widely used in clinical practice.