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MATIC——一种心内心动过速分类系统。

MATIC--an intracardiac tachycardia classification system.

作者信息

Leong P H, Jabri M A

机构信息

Department of Electrical Engineering, University of Sydney, Australia.

出版信息

Pacing Clin Electrophysiol. 1992 Sep;15(9):1317-31. doi: 10.1111/j.1540-8159.1992.tb03142.x.

Abstract

The use of an additional atrial sensing electrode together with a morphology recognition algorithm provides a significant improvement in classification performance over the current rate based algorithms used in implantable cardioverter defibrillator (ICD) devices. The classification system, called morphology and timing intracardiac classifier (MATIC), follows a classification process similar to that used by cardiologists. Timing between the atrial and ventricular channels is examined using a decision tree and forms the primary criterion for arrhythmia classification. A neural network based morphology classifier is used for cases such as ventricular tachycardia with 1:1 retrograde conduction where timing alone cannot make a reliable decision. MATIC achieves 99.6% correct classification on a database of intracardiac electrogram (ICEG) signals containing 12,483 QRS complexes recorded from 67 patients during electrophysiological studies. Arrhythmias in this database include sinus tachycardia, normal sinus rhythm, normal sinus rhythm with bundle branch block, sinus tachycardia with bundle branch block, atrial fibrillation (AF), various supraventricular tachycardias, ventricular tachycardia, ventricular tachycardia with 1:1 retrograde conduction, and ventricular fibrillation. Within these arrhythmias, there were numerous ventricular ectopic beats, fusion beats, noise, and other artifacts. MATIC addresses the classification problem from start to finish, inputs being raw intracardiac electrogram signals and the outputs being the recommended ICD therapy. Results achieved with MATIC were compared with a classifier used in the Telectronics Guardian ATP 4210, which achieved 75.9% correct classification on the same database. MATIC is simple and efficient, making it suitable for use in a low power implantable device.

摘要

在植入式心脏转复除颤器(ICD)设备中,使用额外的心房感知电极并结合形态识别算法,与当前基于心率的算法相比,可显著提高分类性能。这种名为形态与时间心内分类器(MATIC)的分类系统,其遵循的分类过程与心脏病专家使用的过程类似。利用决策树检查心房和心室通道之间的时间,这构成了心律失常分类的主要标准。对于诸如伴有1:1逆向传导的室性心动过速等情况,仅靠时间无法做出可靠判断时,则使用基于神经网络的形态分类器。MATIC在一个包含67例患者在电生理研究期间记录的12483个QRS波群的心内电信号(ICEG)数据库上,实现了99.6%的正确分类。该数据库中的心律失常包括窦性心动过速、正常窦性心律、伴有束支传导阻滞的正常窦性心律、伴有束支传导阻滞的窦性心动过速、心房颤动(AF)、各种室上性心动过速、室性心动过速、伴有1:1逆向传导的室性心动过速以及心室颤动。在这些心律失常中,存在大量室性早搏、融合波、噪声及其他伪迹。MATIC从头到尾解决分类问题,输入为原始的心内电信号,输出为推荐的ICD治疗方案。将MATIC所取得的结果与应用于Telectronics Guardian ATP 4210中的分类器进行比较,后者在同一数据库上的正确分类率为75.9%。MATIC简单高效,适合用于低功耗植入式设备。

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