Electrocardiogram Pattern Recognition by Means of MLP Network and PCA: A Case Study on Equal Amount of Input Signal Types

At the present scenario, one of the main causes of death in developed and in emerging countries is the cardiovascular related diseases. Most of these deaths could be avoided if there was a pre-monitoring and a prediagnostic of these cardiac arrhythmia and myocardial isquemy by using an electrocardiogram (ECG) tool. In this scenario, this work proposes a system to help the doctor to detect cardiac arrhythmia. As reference, it uses the Normal, Fusion and PVC signals of the MIT database. Then, we extract the principal characteristics of the signal by means of the Principal Component Analysis (PCA) technique. One key-point in this work is the input signals extraction, which are captured in the same amount. So, the number of segments for each signal is the same. After signal preprocessing, they are applied to an Artificial Neural Network Multilayer Perceptron (ANN MLP). The MLP with 5 neurons was verified to have the best accuracy. Based on this idea (the use of the same information amount for all input signal types), we achieved better results in comparison with other works in the field. This consideration is very important due to the fact that the ANN could be more sensible to the signal type with major predominance.