Abstract

MAYORGA-ORTIZ, P.; VALDEZ-GONZALEZ, J.A.; DRUZGALSKI, C.  and  ZELJKOVIC, V.. Automatic Detection and Classification of Cardiopulmonary Events. Rev. mex. ing. bioméd [online]. 2018, vol.39, n.1, pp.65-80. ISSN 2395-9126.  https://doi.org/10.17488/rmib.39.1.6.

A standard and/or electronic stethoscope based auscultatory signals include not only the internal sounds of the body but also interfering external noise often with similar frequency components. This form of examination is also affected by varying thresholds of clinical practitioner’s hearing and degree of experience in recognition of peculiar auscultatory indicators. Further, the results are often characterized in qualitative descriptive terms subject to individual’s interpretation. To address these concerns, presented studies include concurrent processing of dominant heart (HS) and lung (LS) sounds components and a conditioning stage involving HS presence reduction within LS focused signals. Specifically as determined, the Hilbert transform was a technique of choice in HS characterization. In the case of LS focused signals, the speech activity detection techniques (VAD) and the thresholds calculation of some components of acoustic vectors of Cepstral Coefficients in Mel Frequency (MFCC), were useful in characterization of associated acoustic events. The phases of inspiration and expiration were differentiated by means of the sixth component of MFCC. In order to evaluate the efficiency of this approach, we propose Hidden Markov Models with Mixed Gaussian Models (HMM-GMM). The results utilizing this form of detection are superior when performing classification with HMM-GMM models, which reflect the advantages of presented form of quantifiable detection and classification over traditional clinical approach.

Keywords : Hilbert Transform (HT); Voice Activity Detection (VAD); Mixed Gaussian Models (GMM); Hidden Markov Models (HMM).