基于云的多变量信号心脏异常检测

IF 1.1 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
Sachin M. Karmuse, Arun L. Kakhandki, Mallikarjun Anandhalli
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引用次数: 0

摘要

摘要本文讨论了利用人脸跟踪、前额区域提取和盲源分离等方法进行心率监测的方法。盲源分离应用于三个RGB颜色通道。独立成分分析(ICA)是此类收购的有力工具。主要有四种ICA算法,这些算法已经在文献中进行了描述。本文研究了两种主要的常用ICA算法的贡献。对这些方法从前额区域的标准RGB信号中获得独立信号的能力进行了比较。这些方法是特征矩阵的联合近似对角化(JADE)和不动点ICA(FastICA)。相同的RGB数据集已应用于这些常见的ICA算法,并与血容量脉冲(BVP)传感器读数的结果进行了比较。两种方法都提供了同样一致的结果。然而,与JADE相比,FastICA在心率测量方面显示出更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud based multivariate signal based heart abnormality detection
Abstract This paper discusses the rate of heartbeat monitoring with the help of face tracking, extraction of forehead region and separation of blind source. Separation of blind source is applied for three RGB color channel. Independent Component Analysis (ICA) is a powerful tool for such acquisitions. There are mainly four ICA algorithms and these algorithms have been described in the literature. In this paper contribution of two main common ICA algorithms has been studied. These methods are compared to each other in terms of their ability to obtain independent signal from standard RGB signal of forehead region. These methods are Joint Approximate Diagonalization of Eigen matrices (JADE) and Fixed-point ICA (FastICA). Same RGB data set have been applied to these common ICA algorithms and compared with the results of blood volume pulse (BVP) sensor readings. Both methods provide equally consistent results. However, FastICA has shown better results for heart rate measurement compared to JADE.
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来源期刊
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES INFORMATION SCIENCE & LIBRARY SCIENCE-
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21.40%
发文量
88
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