This paper used machine learning approaches for detecting ECG abnormality utilizing a Support Vector Machine (SVM) and Cost-Sensitive Decision-Tree (CS-DT) classifier. The Empirical Mode Decomposition approach was utilized to examine the properties of R-peaks and QRS complexes in ECG signs. Various...
P4596Cost-effectiveness analysis of a handheld ECG machine used for a population-wide screening programme: the Belgian Heart Rhythm Week Screening programmedoi:10.1093/eurheartj/ehx504.P4596Proietti M.Farcomeni A.Goethals P.Scavee C.Vijgen J....
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Support vector machineIntelligent sensorsEmbedded systemThe electrocardiogram (ECG) found many clinical applications. Recently, it was proposed as a promising technology also for biometric applications, i.e., to recognize a subject within a group of known people. For such an application, the accuracy...
This paper used machine learning approaches for detecting ECG abnormality utilizing a Support Vector Machine (SVM) and Cost-Sensitive Decision-Tree (CS-DT) classifier. The Empirical Mode Decomposition approach was utilized to examine the properties of R-peaks and QRS complexes in ECG signs. Various...
Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA 2 Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry—University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of...