title: Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.
abstract: Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.
authors.affiliation: Department of Electrical Engineering and Information Technologies, University "Federico II" of Naples, Italy.
authors.forename: Mario
authors.initials: M
authors.lastname: Sansone
authors.forename: Roberta
authors.initials: R
authors.lastname: Fusco
authors.forename: Alessandro
authors.initials: A
authors.lastname: Pepino
authors.forename: Carlo
authors.initials: C
authors.lastname: Sansone
mesh_terms: D001145:Arrhythmias, Cardiac / Q000175:diagnosis / Q000503:physiopathology; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine
keywords: ECG classification; ECG features; arrhythmia detection; electrocardiogram; heart rate variability analysis; human identification; pattern recognition