During Phase I, Flint Hills Scientific developed an algorithm for real time quantitative seizure detection which performs with sensitivity and specificity equal to expert visual analysis. Of even greater value is the capability of this algorithm to predict seizure onset by 13.6 seconds (mean), in its generic mode. Preliminary studies indicate that with automated individualized adaptation, prediction time can be increased to 180 seconds or longer. To the best of our knowledge no other system in existence has achieved this level of success. These results lay the ground for the fulfillment of """"""""seizure prediction, early recognition and blockage of seizures,"""""""" the number one AES research priority. The main goal of Phase II will be to advance, further refine, and validate this technology for implementation into a portable or implantable device with diagnostic, warning, and therapeutic capabilities. We are confident that this technology, by decreasing or eliminating unpredictability, will minimize the potentially devastating effect of seizures on quality of life while decreasing morbidity, the cost of health care, and the reliance on the welfare system. These unique advantages will ensure widespread acceptance of this technology by those directly and indirectly affected by epilepsy and by the health care system.

Proposed Commercial Applications

1. Software package for real time seizure prediction, detection, localization, imaging, and quantitative analysis. 2. Software package for automated, selective noise reduction 3. Portable device for the automated early warning of impending seizures. 4. Portable or implantable devices for automated early therapeutic intervention.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
2R44NS034630-02
Application #
2422022
Study Section
Special Emphasis Panel (ZDA1-MXC-A (15))
Project Start
1995-09-30
Project End
1999-08-31
Budget Start
1997-09-30
Budget End
1998-08-31
Support Year
2
Fiscal Year
1997
Total Cost
Indirect Cost
Name
Flint Hills Scientific, LLC
Department
Type
DUNS #
City
Lawrence
State
KS
Country
United States
Zip Code
66049
Haas, Shane M; Frei, Mark G; Osorio, Ivan (2007) Strategies for adapting automated seizure detection algorithms. Med Eng Phys 29:895-909
Bhavaraju, Naresh C; Frei, Mark G; Osorio, Ivan (2006) Analog seizure detection and performance evaluation. IEEE Trans Biomed Eng 53:238-45
Meng, Lingmin; Frei, Mark G; Osorio, Ivan et al. (2004) Gaussian mixture models of ECoG signal features for improved detection of epileptic seizures. Med Eng Phys 26:379-93
Peters, T E; Bhavaraju, N C; Frei, M G et al. (2001) Network system for automated seizure detection and contingent delivery of therapy. J Clin Neurophysiol 18:545-9
Sunderam, S; Osorio, I; Watkins 3rd, J F et al. (2001) Vagal and sciatic nerve stimulation have complex, time-dependent effects on chemically-induced seizures: a controlled study. Brain Res 918:60-6
Osorio, I; Frei, M G; Manly, B F et al. (2001) An introduction to contingent (closed-loop) brain electrical stimulation for seizure blockage, to ultra-short-term clinical trials, and to multidimensional statistical analysis of therapeutic efficacy. J Clin Neurophysiol 18:533-44
Frei, M G; Davidchack, R L; Osorio, I (1999) Least squares acceleration filtering for the estimation of signal derivatives and sharpness at extrema. IEEE Trans Biomed Eng 46:971-7