Comparative study between DD-HMM and RBF in ventricular tachycardia and ventricular fibrillation recognition

Scolari, Diogo; Fagundes, Rubem Dutra Ribeiro; Russomano, Thaís; Zwetsch, Iuberi Carson

doi:10.1016/j.medengphy.2007.02.006

Cited by 1 publication

(1 citation statement)

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“…Each waveform is the observable associated to the electrical activation (unobservable) within the heart. HMMs have been used mainly for heartbeat segmentation and arrhythmia detection [88,89,90,92,93].…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

Sansone¹,

Fusco

Pepino³

et al. 2013

Journal of Healthcare Engineering

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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.

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