Artificial neural networks, when combined with appropriate data pre- and post-processing, offer a viable approach for the automatic classification of ECG features such as arrhythmias and ischemia.
The analysis of ECGs can benefit from the wide availability of computing technology. This paper presents some results achieved by carrying out the classification tasks of equipment integrating the most common features of the ECG analysis: arrhythmia, myocardial ischemia, chronic alterations. Several ANN architectures are implemented, tested, and compared with competing alternatives. The approach, structure, and learning algorithm of ANNs are designed according to the features of each particular classification task. The trade-off between the time consuming training of ANNs and their performance is also explored. Data pre- and post-processing efforts for system performance are critically tested. The crucial role of these efforts for the reduction of input space dimensions, for a more significant description of the input features, and for improving new or ambiguous event processing is also documented. Finally, algorithm assessment is done on data coming from available ECG databases.
Silipo et al. (1998) studied this question.