A framework for the atrial fibrillation prediction in electrophysiological studies.
OBJECTIVE
Cardiac arrhythmias are disorders in terms of speed or rhythm in the heart's electrical system. Atrial fibrillation (AFib) is the most common sustained arrhythmia that affects a large number of persons. Electrophysiologic study (EPS) procedures are used to study fibrillation in patients; they consist of inducing a controlled fibrillation in surgical room to analyze electrical heart reactions or to decide for implanting medical devices (i.e., pacemaker). Nevertheless, the spontaneous induction may generate an undesired AFib, which may induce risk for patient and thus a critical issue for physicians. We study the unexpected AFib onset, aiming to identify signal patterns occurring in time interval preceding an event of spontaneous (i.e., not inducted) fibrillation. Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation. The objective is to increase the reliability of EPS procedures.


METHODS
We gathered data signals collected by a General Electric Healthcare's CardioLab electrophysiology recording system (i.e., a polygraph). We extracted superficial and intracavitary cardiac signals regarding 50 different patients studied at the University Magna Graecia Cardiology Department. By studying waveform (i.e., amplitude and energy) of intracavitary signals before the onset of the arrhythmia, we were able to define patterns related to AFib onsets that are side effects of an inducted fibrillation.


RESULTS
A framework for atrial fibrillation prediction during electrophysiological studies has been developed. It includes a prediction algorithm to alert an upcoming AFib onset. Tests have been performed on an intracavitary cardiac signals data set, related to patients studied in electrophysiological room. Also, results have been validated by the clinicians, proving that the framework can be useful in case of integration with the polygraph, helping physicians in managing and controlling of patient status during EPS.
Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.
Patrizia
P
Vizza
Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.
Antonio
A
Curcio
Department of Computer Science, Modelling, Electronics and Systems Engineering (DIMES), University of Calabria, Italy.
Giuseppe
G
Tradigo
Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.
Ciro
C
Indolfi
Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. Electronic address: veltri@unicz.it.
Pierangelo
P
Veltri
D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D004562:Electrocardiography; D006339:Heart Rate; D006801:Humans; D012307:Risk Factors
Atrial fibrillation; Heart rate variability; Intracardiac signal; Prediction algorithm; Signal processing