基于多阶人工神经网络的ECG信号诊断模型研究

Novel ECG diagnosis model based on multi-stage artificial neural networks

目前已经有多种不同的ECG(心电图)信号辅助诊断工具得到应用,其中包含了基于人工神经网络的ECG分类器应用系统。本文介绍一种基于多阶前馈人工神经网络的新型ECG信号诊断模型,其目标是设计一种结构简单、成本低、响应速度快,识别率高的ECG信号辅助诊断系统。首先给出多个不同结构的神经网络,然后针对6种不同的心脏状况,比较这些神经网络之间的性能差异和辨别能力。网络的输入数据来自于M IT/B IH数据库,包括12种ECG特征信号和相应的每次心脏搏动的13段压缩信号。通过研究测试发现,基于二阶神经网络的ECG模型识别率最高,正确率达到了90.57%。

Different ECG diagnosis tools are currently available, which include Artificial Neural Network-based ECG classifier application system. This paper proposes a novel model of multi-stage feed forward neural networks for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool, which can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their abilities to classify six different heart conditions. The input data of the networks comprise 12 ECG features and 13 compressed components of each heart beat signal, which are obtained from the MIT/BIH database. Among the different architectures tested, a multi-stage network gave the highest recognition rate of 90.57%. This network is proposed as a suitable candidate to be used in intelligent ECG signal diagnosis systems.