Comparative study between DD-HMM and RBF in ventricular tachycardia and ventricular fibrillation recognition.
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.
Scolari
Diogo
D
IPCT-PUCRS, Prédio 30, Sala 301-03, Av. Ipiranga 6681, Porto Alegre, RS 90619-900, Brazil. diogoscolari@yahoo.com.br
Fagundes
Rubem D R
RD
Russomano
Thaís
T
Zwetsch
Iuberi Carson
IC
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*