A frame work for analysis and optimization of multiclass ECG classifier based on Rough set theory

Detection and delineation of Electrocardiogram has played a vital role in cardiovascular monitoring systems. The enormous database of heart beats which characterize the heart disease, uncertainty, randomness in occurrence of these beats necessitate the use of Rough set theory. Over the years Rough set theory has been effectively used for removal of uncertainties and reduction of dataset. This paper discusses an optimized rough set based algorithm for detection of fiducial points for ten classes of ECG. Fiducial points help determine the peaks, valleys, onset and offset of the waves. Ten morphological features have been identified and investigation of efficiency of Rough set theory to reduce and extract the decision rules from the database has been done. The experimental results show that the proposed method has sensitivity 48%; average specificity 96% and average detection accuracy 91%. Methods involving the use of evolutionary algorithms have also been a powerful tool for dealing with complex optimization problems. Rough-fuzzy approach accompanied with Ant colony optimization, Particle swarm optimization and Genetic algorithm as search methods has also been studied. The results obtained by integrating Multilayer Perceptron or Fuzzy-Rough neural network with fuzzy rough approach for attribute selection as well has shown the highest accuracy of around 96%.