Equivalent tree representation of electrocardiogram using genetic algorithm

Electrocardiogram (ECG) gives the electrical activity of the heart. The number of data points required to represent the ECG signal is reduced by using a complete-tree representation. This reduced data structure (ECG Tree) is obtained by fitting the ECG signal in a grid structure consisting of both horizontal and vertical lines. The leaf nodes are the points where the vertical grid lines intersect with the ECG signal. These leaf nodes now form the features of the ECG signal. Some of these leaf nodes may be redundant and hence the reduction in the number of leaf nodes and thus optimization of the tree (equivalent tree) is done using a novel technique based on the Genetic Algorithm (GA). In this work, the equivalent tree is formed using GA consisting of four stages. First, from the group of generated leaf nodes various combinations of strings are constructed to form the population. Second, the fitness function is taken as the measure of the vertical distances between two neighbouring leaf nodes in order to evaluate the population with respect to their fitness values. Third, the selection procedure is used to give offsprings based on an assigned threshold value. Finally, crossover and mutation operations are performed repeatedly till an optimized population is obtained. The optimal nodes represent the equivalent tree. The Backpropagation Neural Network as a classifier is used to test the efficacy of the GA in this optimization problem.