title: Comparative study between DD-HMM and RBF in ventricular tachycardia and ventricular fibrillation recognition.
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.
authors: IPCT-PUCRS, Prédio 30, Sala 301-03, Av. Ipiranga 6681, Porto Alegre, RS 90619-900, Brazil. diogoscolari@yahoo.com.br
authors: Diogo
authors: D
authors: Scolari
authors: Rubem D R
authors: RD
authors: Fagundes
authors: Thaís
authors: T
authors: Russomano
authors: Iuberi Carson
authors: IC
authors: Zwetsch
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D047548:Defibrillators / Q000706:statistics & numerical data; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D017180:Tachycardia, Ventricular / Q000175:diagnosis*; D014693:Ventricular Fibrillation / Q000175:diagnosis*