Recognizing Cardiovascular Risk from Photoplethysmogram Signals Using ELM

被引：0

作者：

Shobitha, S. ^{[1
]}

Sandhya, R. ^{[1
]}

Krupa, Niranjana B. ^{[1
]}

Ali, Mohd Alauddin Mohd ^{[2
]}

机构：

[1] PES Univ, Dept Elect & Commun Engg, Bangalore, Karnataka, India

[2] Univ Kebangsaan Malaysia, Dept Elect Elect & Syst Engg, Bangi, Malaysia

来源：

2016 SECOND INTERNATIONAL CONFERENCE ON COGNITIVE COMPUTING AND INFORMATION PROCESSING (CCIP) | 2016年

关键词：

PPG; CVDs; Extreme learning machine; SVM; Back propagation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, photoplethysmogram (PPG) signals, 30 healthy and 30 pathological, are classified as 'healthy' or 'at risk' of cardiovascular diseases (CVDs) using extreme learning machine (ELM), a supervised learning algorithm. Additionally, two other supervised learning algorithms, backpropagation and support vector machine are used for classification to compare their results with that of ELM and hence validate its performance. Based on the results obtained, ELM gives the best accuracy, a sensitivity of 89.33% and a specificity of 90.33%, with minimum training time and minimum number of features as input.

引用

页数：5

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