A Real-Time, Portable Non-Invasive Monitoring System of Muscle Oxygen and pH in Trauma Patients

Award Information
Agency: Department of Defense
Branch: Army
Contract: W81XWH-08-C-0111
Agency Tracking Number: A08A-037-0046
Amount: $100,000.00
Phase: Phase I
Program: STTR
Awards Year: 2008
Solicitation Year: 2008
Solicitation Topic Code: A08-T037
Solicitation Number: 2008.A
Small Business Information
1507 Plateau Lane, Rapid City, SD, 57703
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Bernt Askildsen
 (605) 484-0055
Business Contact
 Janna Streleck
Title: Business Manager
Phone: (605) 484-0680
Email: jannastreleck@gmail.com
Research Institution
 Brian Hemmelman
 501 East Saint Joseph Street
Rapid City, SD, 57703
 (605) 394-1668
 Nonprofit college or university
War fighters face increased risk in the post 9-11 era of expanding terrorist activity and urban military conflicts. The problem is compounded by complex modern urban areas and modern rules of engagement often preclude the use of wide area offensive tactics. Consequently dismounted forces are called to enter challenging hostile terrain in search of select enemy targets. Many of these respondents are injured or killed each year due to improvised explosive devices detonated from remote locations. War fighters injured by these explosions often have significant loss of blood that leads to shock, which results in inadequate organ perfusion and tissue oxygenation. Hemorrhage is therefore a major cause of soldier death in the modern day battlefield. Resuscitation from shock aims to correct the mismatch between available oxygen and the demands of critical organs. Accurate knowledge of partial oxygen pressure, the oxygen saturation, and the pH of the peripheral muscle tissue support a successful resuscitation procedure. Therefore, this project will develop technology for a miniature device able to measure these parameters. The proposed system will utilize ultra bright directive light emitters with narrow photon energy spread in the 660nm to 1050nm spectrum. Each sensing objective will be optimized on a 3-layer phantom using an artificial neural network classifier scheme already proven to extract very reliable signals from highly cluttered ultra wide band raw data. The artificial neural network training strategy will take into account issues such as inhomogeneous medium, variation of light intensities, lack of monochromatic light sources, and the relation between light absorption, scatter and reflections. Under ideal conditions, the accuracy is expected exceed 95%.

* Information listed above is at the time of submission. *

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