Identification and classification of ventricular arrhythmias such as rhythmic ventricular tachycardia (VT) and disorganized ventricular fibrillation (VF) are vital tasks in guiding implantable devices to deliver appropriate therapy in preventing sudden cardiac deaths. Recent studies have shown VF can exhibit strong regional organizations, which makes the overlap zone between the fast paced rhythmic VT and VF even more ambiguous. Considering that implantable cardioverter-defibrillator (ICD) are primarily rate dependent detectors of arrhythmias and that there may be patients who suffer from arrhythmias that fall in the overlap zone, it is essential to identify the degree of affinity of the arrhythmia toward VT or organized/disorganized VF. The method proposed in this work better categorizes the overlap zone using Wavelet analysis of surface ECGs. Sixty-three surface ECG signal segments from the MIT-BIH database were used to classify between VT, organized VF (OVF), and disorganized VF (DVF). A two-level binary classifier was used to first extract VT with an overall accuracy of 93.7% and then the separation between OVF and DVF with an accuracy of 80.0%. The proposed approach could assist clinicians to provide optimal therapeutic solutions for patients in the overlap zone of VT and VF.

Ventricular arrhythmias arise from abnormal electrical activity of the lower chambers (ventricles) of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two major subclasses of ventricular arrhythmias. While VT has treatment options that can be performed in catheterization labs, VF is a lethal cardiac arrhythmia, often when detected the patient receives an implantable defibrillator which restores the normal heart rhythm by the application of electric shocks whenever VF is detected. The classification of these two subclasses are important in making a decision on the therapy performed. As in the case of all real world process the boundary between VT and VF is ill defined which might lead to many of the patients experiencing arrhythmias in the overlap zone (that might be predominately VT) to receive shocks by the an implantable defibrillator. There may also be a small population of patients who could be treated with anti-arrhythmic drugs or catheterization procedure if they can be diagnosed to suffer from predominately VT after objectively analyzing their intracardiac electrogram data obtained from implantable defibrillator. The proposed work attempts to arrive at a quantifiable way to scale the ventricular arrhythmias into VT, VF, and the overlap zone arrhythmias as VT-VF candidates using features extracted from the wavelet analysis of surface electrograms. This might eventually lead to an objective way of analyzing arrhythmias in the overlap zone and computing their degree of affinity towards VT or VF. A database of 24 human ventricular arrhythmia tracings obtained from the MIT-BIH arrhythmia database was analyzed and wavelet-based features that demonstrated discrimination between the VT, VF, and VT-VF groups were extracted. An overall accuracy of 75% in classifying the ventricular arrhythmias into 3 groups was achieved.

Background-High-frequency periodic sources during cardiac fibrillation can be detected by phase mapping techniques.To enable practical therapeutic options for modulating periodic sources (existing techniques require high density multielectrode arrays and real time simultaneous mapping capability), a method to identify electrogram morphologies colocalizing to rotors that can be implemented on few electrograms needs to be devised. Method and Results-Multichannel ventricular fibrillation electrogram data from 7 isolated human hearts using Langendorff setup and intraoperative clinical data from 2 human hearts were included in the analysis. The spatial locations of rotors were identified using phase maps constructed from 112 electrograms. Electrograms were analyzed for repeating patterns and discriminating signal morphologies around the locations of rotors and nonrotors were identified and quantified. Features were extracted from the unipolar electrogram patterns, which corroborated well with the spatial location of rotors. The results suggest that using the proposed modulation index feature, and as low as 1 sample point in the vicinity of the rotors, an accuracy as high as 86% (P<0.001) was obtained in separating rotor locations versus nonrotor locations.