Detection of methacholine with time series models of lung sounds

Abstract

Abstract-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.

Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

“…For example, Pasterkamp and coworkers (1996) [22] utilized a 6th order Butterworth low pass filter with cut off frequency of 2400Hz. Broersen and de Waele (2000) [23] used a 4th order Bessel band pass filter with cut off frequencies of 100Hz and 1500Hz. Güler et al (2005) [21] filtered out heart sounds and frictional sounds using a sixth-order Bessel Filter.…”

Monitoring and detection with time series models

“…In detection problems, however, the prototypes models can be completely different from the data characteristics. Therefore, a choice has to be made about the sequence of the data model and the prototypes in computing the model error ME [13]. The estimated model of the data takes the place of the true process in detection, because it really represents the data and the prototypes are compared to that data model.…”