Classification of electrocardiographic ST-T segments--human expert vs artificial neural network

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.