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

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2 Author(s)
L. J. Hadjileontiadis ; Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Greece ; S. M. Panas

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

Published in:

Higher-Order Statistics, 1997., Proceedings of the IEEE Signal Processing Workshop on

Date of Conference:

21-23 Jul 1997