Authors:
Julien Allali
1
;
Pascal Ferraro
2
;
Costas S. Iliopoulos
3
and
Spiros Michalakopoulos
4
Affiliations:
1
Laboratoire Bordelais de Recherche en Informatique;University Simon Fraser, Canada
;
2
Laboratoire Bordelais de Recherche en Informatique;University of Calgary, Canada
;
3
King's College London;Digital Ecosystems & Business Intelligence Institute, Australia
;
4
King's College London, United Kingdom
Keyword(s):
Arrhythmia, Combinatorics, Electrocardiogram (ECG), Heart rate variability, Pattern matching.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Three problems that arise from electrocardiogram (ECG) interpretation and analysis are presented, followed by algorithmic solutions based on a combinatorial model. First, the beat classification problem is discussed and possible solutions are investigated. Secondly, given the R R -intervals, which can be determined using this combinatorial model, or any Q R S detection algorithm, the heart rate is determined in a statistical manner from which sinus bradycardia and sinus tachycardia are inferred. Finally, a new combinatorial method formeasuring heart rate variability (HRV) is presented and an algorithm for detecting atrial fibrillation is described. The developed algorithms were implemented and tests were carried out on records from the MIT-BIH arrhythmia database. The results of the tests are presented and discussed.