Abstract
The risk of developing life-threatening ventricular arrhythmias in patients with structural heart disease is higher with increased occurrence of premature ventricular complex (PVC). Therefore, reliable detection of these arrhythmias is a challenge for a cardiovascular diagnosis system. While early diagnosis is critical, the task of its automatic detection and classification becomes crucial. Therefore, the underlying models should be efficient, albeit ensuring robustness. Although neural networks (NN) have proven successful in this setting, we show that kernel-based learning algorithms achieve superior performance. In particular, recently developed sparse Bayesian methods, such as, Relevance Vector Machines (RVM), present a parsimonious solution when compared with Support Vector Machines (SVM), yet revealing competitive accuracy. This can lead to significant reduction in the computational complexity of the decision function, thereby making RVM more suitable for real-time applications.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Pattern Recognition and Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
