Rapid wheezing detection algorithm for real-time asthma diagnosis and personal health care

Abstract

Pulmonary disease, such as asthma or chronic obstructive pulmonary disease (COPD), has been a major health concern throughtout the world. It would thus be necessary to develop an effective monitorning device and a real-time diagnosis algorithm for targeted populations, especially for children. Wheezing sound induced by asthma is a critical index for clinicians to make diagnosis. The traditional way to detect wheezing sound is to utilize the digital image process (DIP) method for tracking the specific wheezing pattern appeared in the short-time Fourier transform (STFT) spectral graph of respiratory sound. However, this method requires intensive computation and thus is difficult to implement for real-time diagnosis and low power consumption for personal health care system. In this study, we developed a new wheezing detection algorithm which is based on the estimation of correlation-coefficient of respiratory sound spectrum, called respiratory spectrum correlation-coefficient method (RSCM) in place of DIP step. Because of low memory quest of RSCM process, it can be installed easily in the microcontroller or PDA. We have implanted RSCM to a personal asthma daily care system based on both laptop and PDA. User can measure the respiratory sound by designed microphone input and real-time diagnose the occurrence of asthma. In the initial test, thirteen cases (six for wheezing and seven for normal) of respiratory sound were collected from the public domain websites. The result shows that the sensitivity and specificity for wheezing detection are 83% and 86%, respectively. This result assures the possibility to meet the demands of personal health care.