Key result
Modulation filters improve heart-lung sound separation signal-to-noise ratio ~54% over wavelet decomposition.
Why the study?
Heart and lung sounds overlap in time and frequency domains, potentially causing important affections to be discarded during auscultation.
Absolute Event Rate: 10.21% vs 6.61%
Modulation filters provided a higher signal-to-noise ratio than wavelet decomposition for separating heart and lung sounds, which may optimize bandwidth for telemedicine applications.
May enhance telemedicine auscultation; leaves open clinical validation in patient cohorts.
Auscultation is a medical procedure that provides a general idea of heart and lung behavior as the physician listens to the breath sound. Due to the fact that the sounds from these organs overlap in time and frequency domains, important affections in one of them could be discarded. For instance, the objective of this work is to implement two different methods of cardiac and pulmonary sound separation. First, we apply modulation filters to the timefrequency representation of the original signal, recorded on the chest. Second, we apply an iterative algorithm of wavelet decomposition and reconstruction filters. Results show that they both separate signals appropriately. Taking lung signals as noise, we determine that signal to noise (SNR) ratio is 10.21 for the first method and for 6.61 the second. Applications in telemedicine are encourager since the bandwidth of the signal transmission could be reduced by sending it separately.
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Salazar et al. (2012) studied this question. Modulation filters vs. Wavelet decomposition and reconstruction filters was evaluated on Signal to noise ratio (SNR) taking lung signals as noise. Modulation filters achieved a signal-to-noise ratio of 10.21 compared to 6.61 for wavelet decomposition in separating heart and lung sounds.
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