Edenbrandt L, Devine B, Macfarlane P W
University Department of Medical Cardiology, Royal Infirmary, Glasgow, Scotland.
Eur Heart J. 1993 Apr;14(4):464-8. doi: 10.1093/eurheartj/14.4.464.
Artificial neural networks, which can be used for pattern recognition, have recently become more readily available for application in different research fields. In the present study, the use of neural networks was assessed for a selected aspect of electrocardiographic (ECG) waveform classification. Two experienced electrocardiographers classified 1000 ECG complexes singly on the basis of the configuration of the ST-T segments into eight different classes. ECG data from 500 of these ST-T segments together with the corresponding classifications were used for training a variety of neural networks. After this training process, the optimum network correctly classified 399/500 (79.8%) ST-T segments in the separate test set. This compared with a repeatability of 428/500 (85.6%) for one electrocardiographer. Conventional criteria for the classification of one type of ST-T abnormality had a much worse performance than the neural network. It is concluded that neural networks, if carefully incorporated into selected areas of ECG interpretation programs, could be of value in the near future.
人工神经网络可用于模式识别，最近在不同研究领域的应用变得更加容易。在本研究中，对神经网络在心电图（ECG）波形分类的选定方面的应用进行了评估。两位经验丰富的心电图专家根据ST-T段的形态将1000个心电图复合体单独分类为八个不同类别。其中500个ST-T段的心电图数据以及相应的分类用于训练各种神经网络。经过此训练过程后，最佳网络在单独的测试集中正确分类了399/500（79.8%）的ST-T段。相比之下，一位心电图专家的重复性为428/500（85.6%）。一种ST-T异常分类的传统标准表现远不如神经网络。结论是，如果将神经网络谨慎地纳入心电图解读程序的选定领域，在不久的将来可能会有价值。
