一种用于心律失常分类的强大序列检测算法。

A robust sequential detection algorithm for cardiac arrhythmia classification.

作者信息

Chen S W, Clarkson P M, Fan Q

机构信息

Biomedical Engineering Center, Ohio State University, Columbus 43210, USA.

出版信息

IEEE Trans Biomed Eng. 1996 Nov;43(11):1120-5. doi: 10.1109/10.541254.

DOI:10.1109/10.541254

PMID:9214830

Abstract

In [1] qnd [2] Thakor et al. describe a sequential probability ratio test (SPRT) based on threshold crossing intervals (TCI) for the discrimination of ventricular fibrillation (VF) from ventricular tachycardia (VT). However, in applying their algorithm to data from the MIT-BIH malignant arrhythmia database, we observed some overlap in the distributions of TCI for VF and VT resulting in 16% overall error rate for the discrimination. In this communication, we describe a modified SPRT algorithm, using a new feature dubbed blanking variability (BV) as the basis for discrimination. Using the MIT-BIH database, the preliminary results showed that the proposed method decreases the overall error rate to 5%.

摘要

在[1]和[2]中，萨科尔等人描述了一种基于阈值穿越间隔（TCI）的序贯概率比检验（SPRT），用于区分室颤（VF）和室性心动过速（VT）。然而，在将他们的算法应用于麻省理工学院-布列根和妇女医院（MIT-BIH）恶性心律失常数据库的数据时，我们观察到VF和VT的TCI分布存在一些重叠，导致区分的总体错误率为16%。在本通信中，我们描述了一种改进的SPRT算法，使用一种名为消隐变异性（BV）的新特征作为区分的基础。使用MIT-BIH数据库，初步结果表明，所提出的方法将总体错误率降低到了5%。