12.1 Introduction

The application of artificial intelligence (AI) methods has become an important trend in ECG for the recognition and classification of different arrhythmia types. By arrhythmia we mean any disturbance in the regular rhythmic activity of the heart (amplitude, duration, and the shape of rhythm). From the diagnostic point of view of arrhythmia, the most important information is contained in the QRS complex, a sharp biphasic or triphasic wave of about 1-mV amplitude, and duration of approximately 80 to 100 ms.

Many solutions have been proposed for developing automated systems to recognize and classify the ECG on a real-time basis [1 6]. Depending on the type of the applied signal processing approach and its actual implementation, we can identify statistical and syntactic methods [7]. Nowadays the implementation of predictive models through the use of AI methods, especially neural networks, has become an important approach. Many solutions based on this approach have been proposed. Some of the best known techniques are the multilayer perceptron (MLP) [2], self-organizing maps (SOM) [1, 3], learning vector quantization (LVQ) [1], linear discriminant systems [6], fuzzy or neuro-fuzzy systems [8], support vector machines (SVM) [5], and the combinations of different neural-based solutions, so-called hybrid systems [4].

A typical heartbeat recognition system based on neural network classifiers usually builds (trains) different models, exploiting either different classifier network structures or different preprocessing methods of the data, and then the best one is chosen, while the rest are discarded. However, each...