According to the World Health Organization, an estimated 17 million people die annually due to cardiac disease, which accounts for 30% of the global deaths. Current studies on cardiac diseases indicate that 15% of the people have Left Anterior Hemiblock (LAHB), which ranks third after Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB). To our knowledge, a reliably consistent disease detection and warning algorithm is not currently available for LAHB although various ECG morphologies can be monitored for real-time detection of LAHB. The objective of this research is to develop a real-time detection and warning of LAHB. The presented work describes the design of a weighted feature-based disease classification algorithm, which can be run in a resource constrained mobile environment for effective realtime diagnosis. The testing and evaluation of the algorithm indicates that it is able to detect LAHB with an accuracy of 95.3% and specificity of 100%. © 2016 IEEE.
cited By 0; Conference of 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 ; Conference Date: 24 February 2016 Through 27 February 2016; Conference Code:120090
A. Arunan, Rahul K Pathinarupothi, and Dr. Maneesha V. Ramesh, “A Real-time Detection and Warning of Cardiovascular Disease LAHB for a Wearable Wireless ECG Device”, in 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2016, pp. 98-101.