frequency analysis of capnogram signals to differentiate asthmatic and non-asthmatic conditions using radial basis function neural networks.

in this paper, the method of differentiating asthmatic and non-asthmatic patients using the  frequency analysis of  capnogram  signals is presented.  previously, manual study on capnogram signal has been conducted  by several researchers. all past researches showed significant correlation between capnogram signals and asthmatic patients. however all of them are just manual study conducted through the conventional time domain method. in this study, the power spectral density (psd) of capnogram signals is estimated by using fast fourier transform (fft) and autoregressive (ar) modelling. the  results show the  non-asthmatic  capnograms have one  component  in their  psd estimation, in contrast to asthmatic capnograms that have two components. furthermore, there is a significant difference between the magnitude of the first component  for both asthmatic and non-asthmatic capnograms.  the  effectiveness and  performance  of  manipulating the  characteristics of  the  first frequency  component,  mainly its  magnitude  and  bandwidth,  to  differentiate  between asthmatic and non-asthmatic conditions by means of receiver operating characteristic (roc) curve analysis and radial basis function (rbf) neural network were shown. the output of this network is an integer prognostic index from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 95.65% and an error rate of 4.34%. this developed algorithm is aspired to provide a fast and low-cost diagnostic system to  help  healthcare professional involved in respiratory care as it would be  possible to monitor severity of asthma automatically and instantaneously.