A framework for the atrial fibrillation prediction in electrophysiological studies
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Published on: July 20, 2022
Patrizia Vizza1, Antonio Curcio1, Giuseppe Tradigo2
1Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.
Insights
Researchers developed an atrial fibrillation (AFib) prediction algorithm to identify spontaneous AFib events during electrophysiologic studies (EPS). This tool analyzes cardiac signal patterns to improve patient safety and procedure reliability.
Area of Science:
- Cardiology
- Biomedical Engineering
- Medical Signal Processing
Background:
- Cardiac arrhythmias, particularly atrial fibrillation (AFib), are common heart rhythm disorders.
- Electrophysiologic studies (EPS) are crucial for diagnosing and managing arrhythmias but carry risks from spontaneous AFib.
- Unexpected AFib onset during EPS poses risks to patients and challenges for physicians.
Purpose of the Study:
- To identify predictive signal patterns preceding spontaneous AFib during EPS.
- To develop and implement an algorithm for early detection of spontaneous AFib.
- To enhance the safety and reliability of electrophysiologic study procedures.
Main Methods:
- Collected intracavitary and superficial cardiac signals from 50 patients using a GE Healthcare CardioLab system.
- Analyzed the waveform characteristics (amplitude, energy) of intracavitary signals prior to arrhythmia onset.
- Defined signal patterns associated with AFib onset, distinguishing them from induced fibrillation side effects.
Main Results:
- Developed a framework incorporating an AFib prediction algorithm for electrophysiological studies.
- Validated the algorithm's effectiveness on intracavitary cardiac signal data.
- Clinical validation confirmed the framework's utility in managing patient status during EPS.
Conclusions:
- The developed AFib prediction framework can significantly aid physicians during EPS.
- Early identification of spontaneous AFib improves patient safety and procedural outcomes.
- Integration with existing electrophysiology recording systems offers practical clinical benefits.
Background And 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.
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