Detection of methacholine with time series models of lung sounds

A new method for the extraction of features from stationary stochastic processes has been applied to a medical detection problem. It illustrates a practical application of automatic time series modeling. Firstly, the model type and the model order for two time series prototype models are selected. The prototypes represent the lung noises of a single healthy subject, before and after the application of methacholine, using the model error ME as a measure for the difference between time series models, new data can be divided into classes that belong to the prototype models for this person. The prototype models are obtained from a few expiration cycles under known conditions. This is sufficient to detect the presence of methacholine in new data of the same subject if he is able to maintain stationary conditions by following accurately the prescribed breathing pattern. It is not necessary to use the same model type and the same model order for the prototypes and for new data. Automatically and individually selected models for prototypes and data give a good detection of methacholine.