SBIR Phase II: Information Theoretic Learning and Application to Fetal ECG

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.