Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models. Issue 7 (1st May 2015)

• Record Type: Journal Article
• Authors: Seera, Manjeevan; Lim, Chee Peng; Liew, Wei Shiung; Lim, Einly; Loo, Chu Kiong
• Is Part Of: Expert systems with applications. Volume 42:Issue 7(2015)
• Page Start: 3643
• Page End: 3652
• Publication Date: 2015-05-01
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.12.023
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st095">Highlights</title> <sec> <p id="sp0005"> <list id="l0005"> <list-item id="o0005"> <label></label> <p id="p0265">Medical data classification problems with two real data sets are investigated.</p> </list-item> <list-item id="o0010"> <label></label> <p id="p0270">A literature review on biomedical signal processing techniques is provided.</p> </list-item> <list-item id="o0015"> <label></label> <p id="p0275">The data sets are corrupted with noise to assess the robustness of different models.</p> </list-item> <list-item id="o0020"> <label></label> <p id="p0280">The logistic regression model produces the best results in noise-free environments.</p> </list-item> <list-item id="o0025"> <label></label> <p id="p0285">Ensemble-based learning model yields the best results in noisy environments.</p> </list-item> </list> </p> </sec> </abstract>