公开(公告)号：US20170312171A1

公开(公告)日：2017-11-02

申请号：US15526218

申请日：2015-11-20

Applicant: University of Washington

Inventor： Heemun Kwok ,  Jason Coult ,  Lawrence D. Sherman

IPC: A61H31/00 ,  A61B5/0402 ,  A61N1/39 ,  A61B5/0205 ,  A61B5/00 ,  A61B5/04 ,  A61B5/053

CPC classification number: A61H31/005 ,  A61B5/0205 ,  A61B5/04012 ,  A61B5/0402 ,  A61B5/0452 ,  A61B5/053 ,  A61B5/7264 ,  A61H31/007 ,  A61H2201/5061 ,  A61H2201/5084 ,  A61H2230/045 ,  A61N1/3925 ,  A61N1/3987 ,  A61N1/3993

Abstract: Examples described herein include defibrillators or other medical equipment that may employ hidden Markov models to classify cardiac rhythms in ECG signals. Hidden Markov models may additionally or instead be used to determine presence of a chest compression from the thoracic impedance signal. Classification of cardiac rhythms may be used to determine when to deliver a shock to a patient. Other applications are also described.

公开(公告)号：US20160296762A1

公开(公告)日：2016-10-13

申请号：US15026952

申请日：2014-10-03

Applicant: UNIVERSITY OF WASHINGTON THROUGH ITS CENTER FOR COMMERCIALIZATION

Inventor： Alampallam R. Ramachandran ,  Lawrence Sherman ,  Jason Coult ,  Peter Kudenchuk ,  Allison Chin ,  Christopher Neils

IPC: A61N1/39 ,  A61N1/04 ,  A61B5/046 ,  A61B5/0402 ,  A61B5/00

CPC classification number: A61N1/3987 ,  A61B5/0402 ,  A61B5/0404 ,  A61B5/0452 ,  A61B5/046 ,  A61B5/4836 ,  A61B5/7264 ,  A61H31/007 ,  A61H2230/04 ,  A61N1/046 ,  A61N1/3925

Abstract: Examples of systems, apparatuses, and methods for classification of electrocardiogram signals during cardiopulmonary resuscitation are described. An example system may include a defibrillator comprising an electrocardiogram analyzer. The electrocardiogram analyzer may be configured to apply a prediction modeling technique to an electrocardiogram signal to generate a predicted signal. The electrocardiogram signal may be captured from a patient undergoing cardiopulmonary resuscitation. The electrocardiogram analyzer may be further configured to subtract the predicted signal from the electrocardiogram signal to generate an error signal and to classify a rhythm of the electrocardiogram signal as one of a shockable rhythm or non-shockable based on the error signal. Decision parameters derived from the signals may be used in conjunction with a machine learning technique to classify the electrocardiogram signal.

Abstract translation: 描述了在心肺复苏期间用于分类心电图信号的系统，装置和方法的示例。 示例性系统可以包括包括心电图分析仪的除颤器。 心电图分析器可以被配置为将预测建模技术应用于心电图信号以产生预测信号。 可以从经历心肺复苏的患者获取心电图信号。 心电图分析器还可以被配置为从心电图信号中减去预测信号以产生误差信号，并将心电图信号的节律分类为基于误差信号的可震荡节律或不可震荡之一。 从信号导出的判定参数可以与机器学习技术结合使用以对心电图信号进行分类。