Autoregressive modeling of lung sounds using higher-order statistics: Estimation of source and transmission

Leontios J. Hadjileontiadis, Stavros M. Panas

Research output: Contribution to conferencePaperpeer-review

10 Scopus citations

Abstract

The use of higher-order statistics in an autoregressive modeling of lung sounds is presented, resulting in a characterization of their source and transmission. The lung sound source in the airway is estimated using the prediction error of an all-pole filter based on higher-order statistics (AR-HOS), while the acoustic transmission through the lung parenchyma and chest wall is modeled by the transfer function of the same AR-HOS filter. The parametric bispectrum, using the estimated ai coefficients of the AR-HOS model, is also calculated to elucidate the frequency characteristics of the modeled system. The implementation of this approach on pre-classified lung sound segments in known disease conditions, selected from teaching tapes, was examined. Experiments have shown that a reliable and consistent with current knowledge estimation of lung sound characteristics can be achieved using this method, even in the presence of additive Gaussian noise.

Original languageBritish English
Pages4-8
Number of pages5
StatePublished - 1997
EventProceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS - Banff, Can
Duration: 21 Jul 199723 Jul 1997

Conference

ConferenceProceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS
CityBanff, Can
Period21/07/9723/07/97

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