Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms

• Published in: Artificial intelligence in medicine Vol. 28; no. 3; pp. 231 - 263
• Main Authors: Carrault, G., Cordier, M.-O., Quiniou, R., Wang, F.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2003; Elsevier
• Subjects: Algorithms; Arrhythmia; Arrhythmias, Cardiac - diagnosis; Artificial Intelligence; Artificial neural network; Bioengineering; Cardiac arrhythmia; Cardiac arrhythmia classification; Cardiology and cardiovascular system; Chronicle recognition; Computer Science; Data Structures and Algorithms; Diagnosis, Computer-Assisted; ECG; Electrocardiograms; Electrocardiography; Engineering Sciences; Expert systems; Health; Human health and pathology; Humans; Inductive logic programming; Life Sciences; Logic; Machine Learning; Medical data analysis; Medicine; Neural networks; Pattern recognition; Reproducibility of Results; Signal and Image processing; Signal Processing, Computer-Assisted; Temporal abstraction; Temporal aspects; Time; Medical data analysis; Temporal abstraction; ECG; Artificial neural network; Cardiac arrhythmia classification; Inductive logic programming; Chronicle recognition
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/S0933-3657(03)00066-6

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable.