Publication Type:

Conference Paper


2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), p.98-101 (2016)



Algorithm design and analysis, cardiovascular disease, Cardiovascular system, Classification algorithms, Databases, Diseases, Electrocardiography, Feature extraction, LAHB, LBBB, left anterior hemiblock, left bundle branch block, medical signal detection, medical signal processing, patient diagnosis, RBBB, Real-time detection, real-time diagnosis, right bundle branch block, Sensitivity, Support vector machines, warning algorithm, wearable wireless ECG device, weighted feature-based disease classification algorithm, World Health Organization


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

Cite this Research Publication

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.

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