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A High Performance Approach to Improving Auscultation Capability in Noisy Environments

Completed TRL 4 (started at 2, targeting 4)

Description

Some commercial auscultation devices pick up all sounds without filtering out the background noise. Although the electronic stethoscopes made by 3M and Thinklab have some simple built-in filters, those filters cannot differentiate important internal body sounds from interfering background noise. Other devices used Doppler effects, which cannot be easily interpreted by physicians. We propose a high performance approach to enhancing auscultation capability. The idea is to utilize a second microphone to pick up the background noise and then use the microphone signals to improve the quality of stethoscope signals. There are several key advantages of our approach. First, the second mic is attached to a recorder, which is independent of the stethoscope. That is, the current stethoscope can still be used without any modifications. Second, the background noise level in the stethoscope will be reduced significantly, as we will apply advanced and fast algorithms to eliminate the background noise. Two algorithms will be evaluated. One is based on adaptive filtering, which is a time-domain approach. The other one is based on spectral subtraction with multi-band scaling. These algorithms have been applied by our team to speech enhancement applications in very noisy environments such as battlefield and also astronaut's helmet.

Benefits

The proposed system can also be used in doctors offices, military and civilian hospitals, etc. We plan to integrate our software into some commercial products made by 3M and Thinklab. We expect this technology and its spin-off products to be at least 10 million dollars over the next decade.

We strongly believe our algorithms will yield excellent performance in this NASA application (remote monitoring of astronaut based on auscultation). In Phase 1, we will perform extensive simulations and experiments to prove the feasibility of our algorithms. In Phase 2, real-time prototypes will be developed and evaluated.

Details

Technology areaHuman Health, Life Support, and Habitation Systems > Human Health and Performance > Medical Diagnosis and Prognosis
ProgramSmall Business Innovation Research/Small Business Tech Transfer (SBIR/STTR)
Lead organizationSignal Processing, Inc., Rockville, MD
Start date2011-02-18
End date2011-09-29

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