• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >Personalized traits monitoring using a neural network based on oscillometric measurements
【24h】

Personalized traits monitoring using a neural network based on oscillometric measurements

机译：使用基于示波测量的神经网络进行个性化特征监测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Oscillometric method uses a pressure sensor instead of a stethoscope to record the pressure oscillations within the cuff. Oscillations observed from the pressure sensor show personalized traits of systolic pressure, which indicates the maximum amount of work the heart has to perform per stroke in order to move blood through the arteries. For a given person, the oscillation waveforms extracted from the cuff pressure have an oscillation pattern that varies in size over time. Thus, to extract uniform features from a given person, we normalize the variability of the corresponding oscillation patterns for different frequencies, and evaluate systolic blood pressure using a feedforward neural network based on the extracted features. The validity of measuring systolic blood pressure with a neural network was compared with the average values of systolic pressure obtained by two nurses using the auscultatory method. The recognition performance was found to be 98.2% for ±20 mmHg, 93.5% for ±15 mmHg, and 82.3% for ±10 mmHg based on the difference between the systolic pressures measured by the auscultation method and the proposed method with a neural network. The collected database for our experiment includes 85 participants with ages ranging from 10-80 years. In this paper, we present a novel personalized traits monitoring system that can monitor personalized traits of systolic blood pressure. This study can serve as a foundation for the diagnosis and management of isolated systolic hypertension.
机译：示波法使用压力传感器而不是听诊器来记录袖带内的压力波动。从压力传感器观察到的振荡显示出收缩压的个性化特征，这表明心脏每次搏动必须完成的最大工作量，才能使血液流过动脉。对于给定的人，从袖带压力提取的振荡波形具有随时间变化的振荡模式。因此，为了从给定的人中提取一致的特征，我们将不同频率下相应振荡模式的可变性归一化，并基于提取的特征使用前馈神经网络评估收缩压。将使用神经网络测量收缩压的有效性与两位护士使用听诊法测得的收缩压的平均值进行了比较。根据通过听诊法测得的收缩压与所提出的神经网络方法之间的差值，对±20 mmHg的识别性能为98.2％，对±15 mmHg的识别性能为93.5％，对±10 mmHg的识别性能为82.3％。我们为实验收集的数据库包括85位年龄在10-80岁之间的参与者。在本文中，我们提出了一种新颖的个性化特征监测系统，该系统可以监测收缩压的个性化特征。该研究可为孤立性收缩期高血压的诊断和治疗奠定基础。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号