Paper Info
Title
A real-time detection and warning of cardiovascular disease LAHB for a wearable wireless ECG device.
Abstract
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%.
Year
DOI
Venue
2016
10.1109/BHI.2016.7455844
BHI
Keywords
Field
DocType
cardiovascular system,diseases,electrocardiography,medical signal detection,medical signal processing,patient diagnosis,LAHB,LBBB,RBBB,World Health Organization,cardiovascular disease,electrocardiography,left anterior hemiblock,left bundle branch block,real-time detection,real-time diagnosis,right bundle branch block,warning algorithm,wearable wireless ECG device,weighted feature-based disease classification algorithm
Disease,Left bundle branch block,Algorithm design,Wireless,Wearable computer,Simulation,Computer science,Internal medicine,Cardiology,Right bundle branch block,Electrocardiography,Statistical classification
Conference
Citations 
PageRank 
References 
3
0.45
2
Authors
3
Name
Order
Citations
PageRank
Anjali Arunan130.45
Rahul Krishnan Pathinarupothi2105.55
Ramesh Maneesha36322.44