- Title
- A genetic algorithm-neural network wrapper approach for bundle branch block detection
- Creator
- Allami, Ragheed; Stranieri, Andrew; Balasubramanian, Venki
- Date
- 2016
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161606
- Identifier
- vital:12511
- Identifier
- ISBN: 2325-887X
- Abstract
- An Electrocardiogram (ECG) records the electrical impulses of the heart and indicates rhythm anomalies for diagnostic purposes [1], [2]. A typical ECG tracing of the cardiac cycle consists of a P wave, QRS complex, and T wave [3]. Good performance of an ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as the T and P waves [4]. A Bundle Branch Block (BBB) is a delay or obstruction along electrical impulse pathways of the heart manifesting in a prolonged QRS interval usually greater than 120ms. The automated detection and classification of a BBB is important for prompt, accurate diagnosis and treatment to reduce morbidity and mortality.
- Publisher
- IEEE
- Relation
- Computing in Cardiology Conference (CinC), 2016; Vancouver, BC ;11-14 Sept. 2016, published in Computing in Cardiology p. 461-464
- Rights
- Copyright IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Electrocardiography; Feature extraction; Genetic algorithms; Optimization; Artificial neural networks; Sensitivity
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