异常心电图的自动分析与诊断

Intelligent diagnosis of abnormal ECG waveform

目的当前自动诊断系统中,专家系统不具备学习功能,神经网络系统由于"黑箱"特性缺乏可解释性,因此提出一种结合专家系统和神经网络优势的心电图自动诊断算法。方法本系统包含有特征提取模块、诊断矩阵模块和诊断推理模块等主要模块。在诊断系统的实现过程中,首先从心电信号中提取语义特征,然后结合描述语义特征与病类关系的诊断矩阵计算出该患者患每种疾病的可能性概率,最后根据阈值判断患者所患的疾病。在实验验证部分,利用系统以前没有诊断过的数据进行了测验,通过分析诊断的准确率对系统进行了验证。结果从Physio Bank数据库提取了1200条数据进行预测和结果分析,平均准确率为95.2%。结论本文提出的心电图自动诊断算法,以语义特征作为诊断依据,结合了神经网络和专家系统二者的优势,在各种病类的诊断上准确率都较高。

Objective In the current ECG diagnosis system,the expert system lacks self-learning mechanism and the neural network system does not have interpretability for its black-box characteristic. Therefore,in this paper we propose an auto diagnosis system with the advantages of both expert system and network system. Methods There are several modules in our system,including feature extraction module, diagnosis matrix module and diagnosis inference system. First,we extract semantic features from ECG wave data,then,we combine this with diagnosis matrix to compute the probability of each disease the patient may have. Finally,we diagnose the disease by comparing with a threshold value. In the experiment,we use disease records which never used before to test our system. Results In the experiment,we apply our algorithm to 1200 patients’records from the PhysioBank database, and the average accuracy is 95. 2% . Conclusions The algorithm based on semantic features has a high diagnosis accuracy with the advantages of the accuracy of the neural network and the interpretability of expert system.