Kamousi B, Tewfik A, Lin B, Al-Ahmad A, Hsia H, Zei P, Wang P
School of Medicine, Stanford University, Stanford, CA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2478-81. doi: 10.1109/IEMBS.2009.5334794.
Inappropriate shocks due to misclassification of supraventricular and ventricular arrhythmias remain a major problem in the care of patients with Implantable Cardioverter Defibrillators (ICDs). The purpose of this study was to investigate the ability of a new covariance-based support vector machine classifier, to distinguish ventricular tachycardia from other rhythms such as supraventricular tachycardia. The proposed algorithm is applicable on both single and dual chamber ICDs and has a low computational demand. The results demonstrate that suggested algorithm has considerable promise and merits further investigation.
由于室上性和室性心律失常的误分类导致的不适当电击，仍然是植入式心脏复律除颤器（ICD）患者护理中的一个主要问题。本研究的目的是调查一种基于协方差的新型支持向量机分类器区分室性心动过速与其他节律（如室上性心动过速）的能力。所提出的算法适用于单腔和双腔ICD，且计算需求较低。结果表明，所建议的算法具有相当大的前景，值得进一步研究。
