electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review

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ID: 247639
2013
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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.
Reference Key
sansone2013journalelectrocardiogram Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Mario Sansone;Roberta Fusco;Alessandro Pepino;Carlo Sansone
Journal journal of political philosophy
Year 2013
DOI
10.1260/2040-2295.4.4.465
URL
http://dx.doi.org/10.1260/2040-2295.4.4.465
Keywords
medicine (general)medical technology

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