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

SBIR 第一阶段：信息理论学习及其在胎儿心电图上的应用

• 批准号: 0128452
• 负责人: Neil Euliano
• 金额: $ 9.69万
• 依托单位: Convergent Engineering, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2002-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0128452&HistoricalAwards=false
• 关键词: SBIR; Phase; Information; Theoretic; Learning

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.

这个小企业创新研究（SBIR）第一阶段项目的重点是开发和评估一类新的算法盲源分离（BSS）和独立分量分析（ICA）的基础上最近提出的信息理论学习（ITL）标准。 该算法产生几个实用的标准，以适应通用映射器，无论是在无监督或监督的范式。 ITL标准可以显着改善系统训练的均方误差。 NeuroDimension将开发新的算法来选择用于分离的片段，解决噪声混合物的BSS，并将ITL标准扩展到卷积混合物。 该公司还提出通过胎儿心率监测问题来验证这些方法，这需要分离母体和胎儿ECG，这是一个盲源分离问题。 最小交叉熵的ITL准则可以利用ECG是统计独立的这一事实。期望的是，新的信息理论学习将提取更清晰的ECG，因为它利用了关于信号统计的所有信息，而不仅仅是二阶统计（如MSE）。最后将ITL准则与传统的干扰消除算法在佛罗里达大学医学院获得的真实的数据进行比较。 该项目有可能开发一种新的临床仪器，即胎儿心脏监测仪，这是一个有市场的产品。 该公司利用一种新的信息信号处理方法，可以以实用的实时方式识别难以捉摸的胎儿心脏信号。