The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks of a possible 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. Approach, structure, and learning algorithm of ANN were designed according to the features of each particular classification task, The trade-off between the time consuming training of ANN's and their performances is also explored. Data pre-and post-processing efforts on the system performance were critically tested. These efforts' crucial role on the reduction of the input space dimensions, on a more significant description of the input features, and on improving new or ambiguous event processing has been also documented. Finally the algorithm assessment was done on data coming from all the currently available ECG databases.
