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

这个小企业创新二期项目将开发信息理论方法，根据混合物的统计特性（盲源分离）将胎儿心电图（FECG）信号从母体腹部的嘈杂电环境中分离出来。分离是使用最近引入的算法（Mermaid）完成的，该算法具有计算和数据效率。一期研究表明，Mermaid比以前的FECG分离方法有明显的改进。该项目将开发一种全面的胎儿和产妇监护技术，包括胎儿心率、FECG和产妇子宫电图（EHG，测量收缩信息），在一个非常紧凑的设备中。该项目包括临床研究，旨在提供创建和验证NeuroDimension系统所需的信息，并说明其有效性。潜在市场包括医院胎儿监测、家庭/医生办公室胎儿监测和压力测试，以及用作研究工具。这种监测仪不仅比目前的监测仪便宜，而且还能提供额外的信息，从而大大改善病人的护理，并通过避免不必要的程序降低成本。

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.