Complexity measure and complexity rate information based detection of ventricular tachycardia and fibrillation.

On the basis of non-linear dynamics, the paper uses a Lempel-Ziv complexity measure and presents a new definition of the information complexity rate: cc(n). Using such a definition, relative properties are obtained to help identify chaotic process accurately. Applying complexity analysis to abnormal ECGs recorded from patients with an implantable cardioverter defibrillator, the reasonableness of this information complexity and complexity rate approach are confirmed by means of biological experiments and computer simulations. Finally, objective analysis and explanations of the mechanisms of VT and VF are reported. The results indicate that, with the help of the complexity measure and complexity rate, recognition of ventricular tachycardia (VT) and ventricular fibrillation (VF) signals can be achieved with accuracy up to 100% (VT: 100%; VF: 98.7%).