Hedén B, Edenbrandt L, Haisty W K, Pahlm O
Department of Clinical Physiology, Lund University, Sweden.
Am J Cardiol. 1994 Jul 1;74(1):5-8. doi: 10.1016/0002-9149(94)90482-0.
Artificial neural networks are computer-based expert systems that learn by example, in contrast to the currently used rule-based electrocardiographic interpretation programs. For the purpose of this study, 1,107 electrocardiograms (ECGs) from patients who had undergone cardiac catheterization were used to train and test neural networks for the diagnosis of myocardial infarction. Different combinations of QRS and ST-T measurements were used as input to the neural networks. In a learning process, the networks automatically adjusted their characteristics to correctly diagnose anterior or inferior wall myocardial infarction from the ECG. Two thirds of the ECGs were used in this process. Thereafter, the performance of the networks was studied in a separate test set, using the remaining third of the ECGs. The results from the networks were also compared with that of conventional electrocardiographic criteria. The sensitivity for the diagnosis of anterior myocardial infarction was 81% for the best network and 68% for the conventional criteria (p < 0.01), both having a specificity of 97.5%. The corresponding sensitivities of the network and the criteria for the diagnosis of inferior myocardial infarction were 78% and 65.5% (p < 0.01), respectively, compared at a specificity of 95%. The results indicate that artificial neural networks may be of interest in the attempt to improve computer-based electrocardiographic interpretation programs.
人工神经网络是基于计算机的专家系统，它通过示例进行学习，这与当前使用的基于规则的心电图解释程序不同。在本研究中，使用了1107例接受过心脏导管插入术患者的心电图来训练和测试用于诊断心肌梗死的神经网络。QRS波群和ST段 - T波测量的不同组合被用作神经网络的输入。在学习过程中，网络自动调整其特征，以根据心电图正确诊断前壁或下壁心肌梗死。在此过程中使用了三分之二的心电图。此后，使用其余三分之一的心电图在一个单独的测试集中研究网络的性能。网络的结果也与传统心电图标准的结果进行了比较。最佳网络对前壁心肌梗死诊断的敏感性为81%，传统标准为68%（p < 0.01），两者的特异性均为97.5%。在特异性为95%的情况下，网络和标准对下壁心肌梗死诊断的相应敏感性分别为78%和65.5%（p < 0.01）。结果表明，人工神经网络在尝试改进基于计算机的心电图解释程序方面可能具有价值。
