Detection of ectopic beats in the electrocardiogram using an auto-associative neural network

被引：9

作者：

Tarassenko, L ^{[1
]}

Clifford, G ^{[1
]}

Townsend, N ^{[1
]}

机构：

[1] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England

来源：

NEURAL PROCESSING LETTERS | 2001年 / 14卷 / 01期

关键词：

Neural Network; Artificial Intelligence; Complex System; Nonlinear Dynamics; Variance Ratio;

D O I：

10.1023/A:1011373923479

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Abnormal rhythms of the heart are often preceded by the occurrence of ectopic beats. These are difficult to detect as their shape is not very different from that of a normal QRS complex, the main feature in the electrocardiogram. We show how an auto-asociative multi-layer perceptron can be trained to detect normal beats only, so that the subtle abnormalities in shape of ectopic beats become clearly identifiable. This is a generic detector of abnormal beats (i.e. beats whose morphology is different from that of a normal beat) and we use ventricular ectopic beats to illustrate the performance of the algorithm. We also propose a new parameter, the variance ratio, to monitor the progress of learning in an auto-associative network.

引用

页码：15 / 25

页数：11