• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical engineering & physics. >Non-intrusive real-time breathing pattern detection and classification for automatic abdominal functional electrical stimulation
【24h】

Non-intrusive real-time breathing pattern detection and classification for automatic abdominal functional electrical stimulation

机译：自动腹部功能性电刺激的非侵入式实时呼吸模式检测和分类

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

Abdominal Functional Electrical Stimulation (AFES) has been shown to improve the respiratory function of people with tetraplegia. The effectiveness of AFES can be enhanced by using different stimulation parameters for quiet breathing and coughing. The signal from a spirometer, coupled with a facemask, has previously been used to differentiate between these breath types. In this study, the suitability of less intrusive sensors was investigated with able-bodied volunteers. Signals from two respiratory effort belts, positioned around the chest and the abdomen, were used with a Support Vector Machine (SVM) algorithm, trained on a participant by participant basis, to classify, in real-time, respiratory activity as either quiet breathing or coughing. This was compared with the classification accuracy achieved using a spirometer signal and an SVM. The signal from the belt positioned around the chest provided an acceptable classification performance compared to the signal from a spirometer (mean cough (c) and quiet breath (q) sensitivity (Se) of Se-c = 92.9% and Se-q = 96.1% vs. Se-c = 90.7% and Se-q = 98.9%). The abdominal belt and a combination of both belt signals resulted in lower classification accuracy. We suggest that this novel SVM classification algorithm, combined with a respiratory effort belt, could be incorporated into an automatic AFES device, designed to improve the respiratory function of the tetraplegic population. (C) 2014 IPEM. Published by Elsevier Ltd. All rights reserved.
机译：腹部功能性电刺激（AFES）已被证明可以改善四肢瘫痪患者的呼吸功能。通过使用不同的刺激参数进行安静的呼吸和咳嗽，可以增强AFES的有效性。肺活量计与面罩相结合的信号先前已用于区分这些呼吸类型。在这项研究中，对身体较弱的志愿者研究了侵入性较小的传感器。来自位于胸部和腹部周围的两条呼吸努力带的信号与支持向量机（SVM）算法一起使用，并在参与者的基础上对其进行了训练，以将呼吸活动实时分类为安静呼吸或咳嗽。将其与使用肺活量计信号和SVM获得的分类精度进行了比较。与来自肺活量计的信号相比，来自胸部周围皮带的信号提供了可接受的分类性能（平均咳嗽（c）和安静呼吸（q）的敏感度（Se）为Se-c = 92.9％，Se-q = 96.1相对于Se-c的百分比为90.7％，Se-q为98.9％）。腹带和两种带信号的组合导致分类精度降低。我们建议，可以将这种新颖的SVM分类算法与呼吸努力带相结合，并入自动AFES设备中，该设备旨在改善四肢瘫痪人群的呼吸功能。 （C）2014年IPEM。由Elsevier Ltd.出版。保留所有权利。

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