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

• Published in: Medical engineering & physics Vol. 30; no. 2; pp. 213 - 217
• Main Authors: Scolari, Diogo, Fagundes, Rubem D.R, Russomano, Thaís, Zwetsch, Iuberi Carson
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-03-2008
• Subjects: Algorithms; Databases, Factual; DD-HMM; Defibrillators - statistics & numerical data; Diagnosis, Computer-Assisted - methods; ECG; Electrocardiography - statistics & numerical data; Humans; Markov Chains; Neural network; Neural Networks (Computer); Radiology; RBF; Signal Processing, Computer-Assisted; Tachycardia, Ventricular - diagnosis; Ventricular fibrillation; Ventricular Fibrillation - diagnosis; Ventricular tachycardia; ECG; Ventricular tachycardia; Neural network; DD-HMM; RBF; Ventricular fibrillation
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2007.02.006

Abstract This paper deals with automatic recognition of cardiac arrhythmias that require immediate electrical defibrillation therapy (ventricular fibrillation and ventricular tachycardia), through ECG (electrocardiogram) samples. The DD-HMM (discrete density hidden Markov model) and RBF (radial basis function) neural network algorithms were compared in the following aspects: precision, defined as correct recognition percentage and process time, defined as the delay since the ECG input until the result, indicating shock or non-shock events. The results show that RBF is more precise than DD-HMM but not so fast to evaluate. PhysioNet database files were used to train and to validate the algorithms.