Modified Fuzzy-Neuro Generalized Learning Vector Quantization for early detection of Arrhytmias

In this paper we modified Fuzzy-Neuro Generalized Learning Vector Quantization for Arrhythmia heart beat detection. The original algorithm was used triangle membership function. In this research we propose another membership function is Pi membership function, the Pi membership functionis a product of sigmoid membership function and z membership function was adapted from twin sigmoid membership function recognition rate of the classifier is able to be enhanced. The overall classification system are comprised of three components including data pre-processing, feature extraction and classification system. Data preprocessing related to how the initial data prepared, while for the feature extraction and selection, we using wavelet algorithm. From experiments show perform of a new extension can increasing the accuracy classifier compared with the original FNGLVQ with triangle membership function. The average accuracy of original FNGLVQ comparison with FNGLVQ with Pi membership function is 94.3% and 97.96%. Also precision and recall for both algorithm respectively, 93.49% and 86.23% for original FNGLVQ and 94.16% and 9.75% for FNGLVQ with Pi membership function.