Vizza Patrizia, Curcio Antonio, Tradigo Giuseppe, Indolfi Ciro, Veltri Pierangelo
Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy.
Department of Computer Science, Modelling, Electronics and Systems Engineering (DIMES), University of Calabria, Italy.
Comput Methods Programs Biomed. 2015 Jul;120(2):65-76. doi: 10.1016/j.cmpb.2015.04.001. Epub 2015 Apr 17.
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.
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.
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.
心律失常是心脏电系统在速度或节律方面的紊乱。心房颤动（房颤）是影响大量人群的最常见持续性心律失常。电生理研究（EPS）程序用于研究患者的颤动；它们包括在手术室诱导可控的颤动，以分析心脏电反应或决定植入医疗设备（如起搏器）。然而，自发诱导可能会产生不期望的房颤，这可能给患者带来风险，因此对医生来说是一个关键问题。我们研究意外的房颤发作，旨在识别在自发（即非诱导）颤动事件之前的时间间隔内出现的信号模式。对这些信号模式进行剖析，从而设计并实现一种能够早期识别自发颤动的房颤预测算法。目的是提高EPS程序的可靠性。
我们收集了通用电气医疗集团的CardioLab电生理记录系统（即一种多导记录仪）采集的数据信号。我们提取了关于在大希腊大学心脏病学系研究的50名不同患者的体表和心腔内心脏信号。通过研究心律失常发作前心腔内信号的波形（即振幅和能量），我们能够定义与房颤发作相关的模式，这些模式是诱导颤动的副作用。
已开发出一种用于电生理研究期间房颤预测的框架。它包括一种预测算法，用于提醒即将发生的房颤发作。已对与在电生理室研究的患者相关的心腔内心脏信号数据集进行了测试。此外，结果已得到临床医生的验证，证明该框架在与多导记录仪集成时可能有用，有助于医生在EPS期间管理和控制患者状态。
