首页> 外文会议>International Conference on Control, Automation and Systems >Cardiovascular abnormality detection method using cardiac sound characteristic waveform with data clustering technique
【24h】

Cardiovascular abnormality detection method using cardiac sound characteristic waveform with data clustering technique

机译:具有数据聚类技术心声特性波形的心血管异常检测方法

获取原文

摘要

Objectives: If life-style related diseases could not be monitored continuously during a long time some heart defects might be difficult to be diagnosed appropriately and detected in an early step. Furthermore, the need for the primary health care physicians to improve the cardiac auscultation skill is still very strong in the primary screening examination, and becomes stronger for the general users to perform the auscultation at home. The objective of this paper presents the novel detection method for heart defects using the cardiac sound characteristic waveform (CSCW) with data clustering technique. Methods: An analytical model based on a mass-spring-damper system is proposed for extracting the CSCW from the sound signals. Feature sets T1, T2, T1T1 and T1T2 induced from the time elapses of the first heart sound and second heart sound with a passive threshold value (THV) are, also, introduced to detect heart abnormalities. Further, data clustering technique will be introduced to determinate an adaptive and reliable THV ranges. The cost function, e.g., Jm, and two cluster centers, e.g., (c11, c12) and (c21, c22), of the feature sets are also used to identify normal and abnormal heart sounds automatically. Results and conclusion: The feature sets were verified useful for identification of normal and abnormal heart sounds. The easy-understanding graphical screening ways of the features was considered, in advance, even for an inexperienced user able to monitor his or her pathology progress. Furthermore, for clustering results, the minimized cost function and cluster centers could be also efficient indicators for identifying the heart defects. Finally, a case study on the normal heart sound and abnormal heart sound was demonstrated to validate the usefulness and efficiency of CSCW with clustering algorithm. Particularly, the normal cases had very small value. For abnormal cases, in case of aortic regurgitation, its J-m was very small and the values of the centers were very high comparing to the normal cases.
机译:目的:如果在很长一段时间内不能连续监测生命风格的相关疾病,可能难以在早期的步骤中适当地诊断出一些心脏缺陷。此外,在初级筛查考试中,对初级医疗医生提高心脏病医生的需求仍然非常强劲,并且对普通用户在家里进行听诊方面变得更强大。本文的目的介绍了使用具有数据聚类技术的心声特征波形(CSCW)的心脏缺陷的新型检测方法。方法:提出了一种基于质量弹簧阻尼系统的分析模型,用于从声音信号中提取CSCW。从第一心脏声音和第二声音心脏具有无源阈值(THV)的时间的经过引起的功能集T1,T2,T1T1和T1T2是,还介绍了用于检测心脏异常。此外,将引入数据聚类技术以确定自适应和可靠的THV范围。成本函数,例如,J M 和两个群集中心,例如(C 11 ,C 12 )和(C 特征集的21 ,C 22 )还用于自动识别正常和异常心脏声音。结果与结论:验证了特征集可用于识别正常和异常心脏声音。提前考虑了易于理解的图形筛选方式,即使是能够监测他或她的病理学进展的缺乏经验的用户也是如此。此外,对于聚类结果,最小化的成本函数和群集中心也可能是用于识别心脏缺陷的有效指标。最后,对正常心声和异常心脏声音进行了一个案例研究,以验证CSCW与聚类算法的有用性和效率。特别是,正常情况有很小的价值。对于异常情况,在主动脉反流的情况下,其J- M 非常小,与正常情况相比,中心的值非常高。

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