The Research Foundation for The State University of New York
发明人:
Ohad Barsimantov,Kenneth McLeod,J. David Schaffer
申请号：
US15182087
公开号：
US20160361041A1
申请日:
2016.06.14
申请国别(地区):
US
年份:
2016
代理人:
摘要：
Cardiac Output (CO) has traditionally been difficult, dangerous, and expensive to obtain. Surrogate measures such as pulse rate and blood pressure have therefore been used to permit an estimate of CO. MEMS technology, evolutionary computation, and time-frequency signal analysis techniques provide a technology to non-invasively estimate CO, based on precordial (chest wall) motions. The technology detects a ventricular contraction time point, and stroke volume, from chest wall motion measurements. As CO is the product of heart rate and stroke volume, these algorithms permit continuous, beat to beat CO assessment. Nontraditional Wavelet analysis can be used to extract features from chest acceleration. A learning tool is preferable to define the packets which best correlate to contraction time and stroke volume.
