new prognostic index to detect the severity of asthmaautomatically using signal processing techniques of capnogram

In this paper, a new prognostic index to detect the severity of asthma by processing capnogram signals is presented. previous studies have shown significant correlation between the capnogram and asthmatic patient. however, most of them used conventional timedomain methods and based on assumption that the capnogram is a stationary signal. in this study, by using linear predictive coding (lpc) coefficients and autoregressive (ar) modelling (burg method), the capnogram signals are processed. then, a number of six features including α1, and α4 from lpc and power spectral density (psd) parameters through ar modelling are extracted. after that, by means of receiver operating characteristic (roc) curve, the effectiveness of the extracted features to differentiate between asthmatic and nonasthmatic conditions is justified. finally, selected features are used in a gaussian radial basis function (grbf) network. the output of this network is an integer prognostic index ranging from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 90.15% and an error rate of 9.85%. in the other word, based on the results, sensitivity and specificity of this algorithm are 93.54% and 98.29%, respectively. this developed algorithm is purposed to provide a fast and lowcost diagnostic system to help healthcare professional involved in respiratory care as it would be possible to monitor severity of asthma automatically and instantaneously.

New Prognostic Index to Detect the Severity of Asthma Automatically Using Signal Processing Techniques of Capnogram

In this paper, a new prognostic index to detect the severity of asthma by processing capnogram signals is presented. Previous studies have shown significant correlation between the capnogram and asthmatic patient. However, most of them used conventional timedomain methods and based on assumption that the capnogram is a stationary signal. In this study, by using linear predictive coding (LPC) coefficients and autoregressive (AR) modelling (Burg method), the capnogram signals are processed. Then, a number of six features including α1, and α4 from LPC and power spectral density (PSD) parameters through AR modelling are extracted. After that, by means of receiver operating characteristic (ROC) curve, the effectiveness of the extracted features to differentiate between asthmatic and nonasthmatic conditions is justified. Finally, selected features are used in a Gaussian radial basis function (GRBF) network. The output of this network is an integer prognostic index ranging from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 90.15% and an error rate of 9.85%. In the other word, based on the results, sensitivity and specificity of this algorithm are 93.54% and 98.29%, respectively. This developed algorithm is purposed to provide a fast and lowcost diagnostic system to help healthcare professional involved in respiratory care as it would be possible to monitor severity of asthma automatically and instantaneously.