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MACHINE LEARNING QUALITY ASSESSMENT OF PHYSIOLOGICAL SIGNALS

机译:机器学习质量评估生理信号

摘要

A method comprising receiving, as input, a plurality of PPG waveform signal segments; extracting, from each of the segments, a feature set representing the PPG waveform signal; at a training stage, training a machine learning model on a training set comprising: (i) the feature sets, and (ii) labels indicating a quality parameters associated with the PPG waveform signal in each of the PPG waveform signal segments; and at an inference stage, applying the trained machine learning model to at least one feature set extracted from at least one target PPG waveform signal segment, to determine a quality parameter of the at least one target PPG waveform signal.
机译:一种方法,包括接收,作为输入,多个PPG波形信号段; 从每个段中提取表示PPG波形信号的特征集; 在培训阶段,在训练集上培训机器学习模型,包括:(i)特征集,(ii)标签,指示与每个PPG波形信号段中的PPG波形信号相关的质量参数; 并且在推断阶段,将培训的机器学习模型应用于从至少一个目标PPG波形信号段提取的至少一个特征集,以确定至少一个目标PPG波形信号的质量参数。

著录项

  • 公开/公告号US2022015713A1

    专利类型

  • 公开/公告日2022-01-20

    原文格式PDF

  • 申请/专利权人 SENSORITY LTD.;

    申请/专利号US202117376450

  • 申请日2021-07-15

  • 分类号A61B5;A61B5/024;A61B5/352;G06N20;

  • 国家 US

  • 入库时间 2022-08-24 23:25:01

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