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Multiclass Classification of APG Signals using ELM for CVD Risk Identification: A Real-Time Application

机译：用于CVD风险识别ELM的ELM的APG信号的多级分类：实时应用

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In this paper, we present a non-invasive method of classifying a subject's health as "Healthy" or "At Risk" of cardiovascular disease (CVD). The novelty of the work lies in recognizing the rare case of a young subject with cardiovascular disease as well as old subjects who are healthy, and the real-time implementation of CVD risk analysis. Thirty healthy and thirty pathological signals are pre-processed using Empirical Mode Decomposition (EMD), later, the analysis of the acceleration plethysmogram (APG) signals are carried out. Seven features of the wave contour are extracted along with actual age of the subject, four classes are identified using an extreme learning machine (ELM) classifier, and we made four groups which are, Healthy Young, Unhealthy Young, Healthy Old, and Unhealthy Old. Implementation of the proposed system is done on a Raspberry Pi 2 using the Python programming language. The training of the classifier and prediction of CVD risk group, using the extracted features, takes on average 17.83 milliseconds. The overall accuracy of the system is 86%.
机译：在本文中，我们提出了一种将受试者的健康分类为心血管疾病（CVD）的“健康”或“危险”的非侵入性方法。这项工作的新颖性在于认识到具有心血管疾病的罕见案例，以及健康的旧科目，以及CVD风险分析的实时实施。使用经验模式分解（EMD）预处理三十个健康和30个病理信号，以后进行了加速体系谱（APG）信号的分析。波轮廓的七个特征随着对象的实际年龄提取，使用极端学习机（榆树）分类器来识别四个类，并且我们制作了四个群体，健康年轻，不健康的年轻，健康的老，不健康的旧。使用Python编程语言在覆盆子PI 2上完成所提出的系统。使用提取功能的CVD风险组的分类器和预测的培训平均为17.83毫秒。系统的整体准确性为86％。

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