Abstract: Disclosed are various examples and embodiments of systems, devices, components and methods configured to classify, and to detect at least one location or type of at least one source of, at least one cardiac rhythm disorder in a patient's heart using one or more body surface electrodes, and/or intracardiac electrodes. Body surface electrogram data, and optionally intracardiac electrode data, representative of cardiac signals acquired from the patient are provided to a computing device, which in turn determines the location and type of the at least one source of the at least one cardiac rhythm disorder in the patient's heart using electrographic flow (EGF) methods, and then classifies same using electrographic volatility index (EVI) methods.

Abstract: Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect the locations of sources of cardiac rhythm disorders in a patient's heart, and then to generate an estimate or probability of the patient being free from atrial fibrillation. The various embodiments employ at least one computing device to process a plurality of electrogram surfaces through time to generate at least one electrographical flow (EGF) map, representation, pattern, or data set, and then process the at least one EGF map, representation, pattern, or data set to determine at least two of source activity levels, flow angle variability (FAV) levels, and active fractionation (AFR) levels corresponding thereto. On the basis of a combination of the determined at least two of source activity levels, FAV levels, and AFR levels, an electrographical volatility index (EVI) score or metric representative of the estimate or probability of the patient being free from AF is generated.