Comput Methods Programs Biomed - Exploring an optimal vector autoregressive model for multi-channel pulmonary sound data.

Tópicos

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Resumo

The purpose of this study is to find a useful mathematical model for multi-channel pulmonary sound data. Vector auto-regressive (VAR) model schema is adopted and the best set of arguments, namely, the order and sample size of the model and the sampling rate of the data, is aimed to be determined. Both conventional prediction error criteria and a set of three new criteria which are derived specifically for pulmonary sound signals are used to evaluate the success of the model. In terms of these criteria, the second order 250-point model is selected to be the most descriptive VAR model for 14-channel pulmonary sound data. The preferred sampling rate is the original data acquisition rate, which is 9600 samples per second. The effect of normalization of the data with respect to the air flow is also examined. Six normalization schemes are implemented on the data prior to the model fit, and the resulting model parameters are examined in terms of the proposed criterion measures. It is concluded that normalization with respect to flow is not necessary prior to the VAR modeling of pulmonary sound data.

Resumo Limpo

purpos studi find use mathemat model multichannel pulmonari sound data vector autoregress var model schema adopt best set argument name order sampl size model sampl rate data aim determin convent predict error criteria set three new criteria deriv specif pulmonari sound signal use evalu success model term criteria second order point model select descript var model channel pulmonari sound data prefer sampl rate origin data acquisit rate sampl per second effect normal data respect air flow also examin six normal scheme implement data prior model fit result model paramet examin term propos criterion measur conclud normal respect flow necessari prior var model pulmonari sound data

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