Expanded VAD Guided Subdivision of Cardiopulmonary Sounds

Julio Alejandro Valdez Gonzalez, Pedro Mayorga Ortiz, Christopher Druzgalski, Vesna Zeljkovic, Gilberto Chavez, Monceni Perez

Resumen


Cardiopulmonary auscultation is a diagnostic procedure that has
a challenging task since the components of heart rate and lung
sounds overlap. There were many approaches to quantify the
characteristics of these signals, and one of the newest is the voice
activity detection (VAD) and the Gaussian Mixture Models
(GMM). Considering the lung and heart sounds as acoustic events,
this paper proposes a novel assessment methodology of these
diagnostic indicators. Here, VAD-GMM was applied to detect and
extract the main events in lung sound and heart sounds. VAD-
GMM results were compared with other VAD methodology based
on statistical approach, and it was found that VAD-GMM give
more definite results. Since Mel Frequency Cepstral coefficients
(MFCC) and Quartiles feature vectors, were already successful in
pattern recognition, VAD-GMM was carried out using this kind of
acoustic vectors. Therefore, this method could add in a transition
from qualitative traditional auscultation to quantitative
assessment and assisted computerized diagnosis by identifying
abnormal acoustic indicators. Diagnosis by computerized
detection promises to be a more efficient method than traditional
methods, which are limited by the auditory capability and
experience of a medical professional.


Palabras clave


Cardiopulmonary diagnosis, Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Hilbert transform, Voice Activity Detection (VAD)

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Referencias


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DOI: https://doi.org/10.24050/19099762.n25.2019.1317

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