SBIR-STTR Award

This Small Business Innovation Research (SBIR)Phase I project focuses on the development and evaluation of a new class of algorithms for blind source separation (BSS) and independent component analysis (ICA) based on a recently proposed information theoretic learning (ITL) criterion. The algorithms yield several practical criteria to adapt universal mappers, either under unsupervised or supervised paradigms. The ITL criterion can dramatically improve upon systems trained with mean square error. NeuroDimension will develop new algorithms to choose the segments for separation, address BSS of noisy mixtures, and extend the ITL criterion to convolutive mixtures. The firm further proposes to validate these methods via the fetal heart rate monitoring problem, which requires the separation of the maternal and fetal ECGs, a blind source separation problem. The ITL criterion of minimum cross entropy can exploit the fact that the ECGs are statistically independent. The expectation is that the new information theoretic learning will extract a much cleaner ECG because it is exploiting all the information about the signal statistics, not only the second order statistics (as MSE does). Finally the ITL criterion will be compared with the conventional interference cancellation algorithms in real data obtained from the University of Florida College of Medicine. The project has the potential to develop a new piece of clinical instrumentation, a fetal heart monitor, for which there is a demonstrated market. The firm utilizes a new approach to information signal process that may be able to identify the elusive fetal heart signal in a practical, real-time manner

This Small Business Innovation Phase II Project will develop information theoretic methods to separate fetal electrocardiogram (FECG) signals from the noisy electrical environment of the maternal abdomen based on statistical properties of the mixtures (blind source separation). The separation is done using a recently introduced algorithm (Mermaid) that is computationally and data efficient. Phase I research showed that Mermaid is a marked improvement over prior methods of FECG separation. The project will develop the technology for a comprehensive fetal and maternal monitor including fetal heart rate, FECG, and maternal Electrohysterogram (EHG, which measures contraction information) in a very compact device. The project includes clinical studies designed to provide the information necessary to create and validate NeuroDimension's system and also to illustrate its effectiveness. Potential markets include hospital-based fetal monitoring, home/physician's office fetal monitoring and stress tests, and use as a research tool. The monitor not only will be less expensive than current monitors, but also will provide additional information that can dramatically improve patient care and reduce costs by avoiding unnecessary procedures.