Adaptive Noise Suppression of Pediatric Lung Auscultations With Real Applications to Noisy Clinical Settings in Developing Countries

Abstract

Goal: Chest auscultation constitutes a portable low-cost tool widely used for respiratory disease detection. Though it offers a powerful means of pulmonary examination, it remains riddled with a number of issues that limit its diagnostic capability. Particularly, patient agitation (especially in children), background chatter, and other environmental noises often contaminate the auscultation, hence affecting the clarity of the lung sound itself. This paper proposes an automated multiband denoising scheme for improving the quality of auscultation signals against heavy background contaminations.

Methods: The algorithm works on a simple two-microphone setup, dynamically adapts to the background noise and suppresses contaminations while successfully preserving the lung sound content. The proposed scheme is refined to offset maximal noise suppression against maintaining the integrity of the lung signal, particularly its unknown adventitious components that provide the most informative diagnostic value during lung pathology.

Results: The algorithm is applied to digital recordings obtained in the field in a busy clinic in West Africa and evaluated using objective signal fidelity measures and perceptual listening tests performed by a panel of licensed physicians. A strong preference of the enhanced sounds is revealed.

Significance: The strengths and benefits of the proposed method lie in the simple automated setup and its adaptive nature, both fundamental conditions for everyday clinical applicability. It can be simply extended to a real-time implementation, and integrated with lung sound acquisition protocols.

Fig. 1

Spectrogram representation of four lung sound excerpts. Top panel: internal microphone; middle panel: external microphone recording; bottom panel: signal as outputted by spectral subtraction algorithm B. The quasi-periodic energy patterns, more pronounced in (a) and (b), correspond to the breathing and heart cycles and are well preserved in the enhanced signal. (a) Electronic interference contaminations and (b) soft background cry have successfully been removed. Panels (c) and (d) show cases heavily contaminated by room noise and loud background crying which have substantially been suppressed using the proposed algorithm. Notice how concurring adventitious events were kept intact in (c) at 1.5–3 s and in (d) at 0.6–0.8 s . The period at the beginning of (d) corresponded to an interval of no contact with the child’s body and was silenced after the postprocessing algorithm.

Fig. 2

(a) Average results with error bars on the evaluation of objective, quality, and intelligibility measures for original noisy signal (left bar) and the enhanced signal (right bar), compared with noise as the ground truth. Enhanced signals were found to be more “distant” representations of the noise signals. Stars indicate statistically significant differences. (b) Average responses of the listening text where bars indicate the preference percentage per choice. Left: overall results, comparing average preference of the original sounds versus preference of any of the enhanced versions. Panel [A to Any] includes choices {A, B, C, D, Any}; Right: the breakdown among all choices. Choice Any of A,B,C,D has been abbreviated to Any.

Fig. 3

Spectrogram illustrations comparing the proposed method with (a) speechSP and (b) FX-LMS applied on the same sound excerpt. SpeechSP suppresses important lung sounds like crackle patterns (black circles) and wheeze pattern (blue circle). FX-LMS convergence is challenged by both the parametric setup and the complex, abrupt noise environment resulting in non-optimal lung sound recovery. Colormap is the same as Fig. 1.