Comparison of Genetic Algorithm-ANN and PCA-ANN for ECG Arrhythmia detection

   This project is designed based on the paper "A Genetic Algorithm-Neural Network Wrapper Approach for Bundle Branch Block Detection".A Bundle Branch Block (BBB) is a delay or obstruction along electrical impulse pathways in the heart. The automated detection and classification of a BBB is important for prompt, accurate diagnosis and treatment of heart conditions, especially in accurate identification, of left BBB. This design is based on hybrid approach for the detection of BBB that uses a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) in combination with Artificial Neural Network to improve classification accuracy. Nineteen temporal features and three morphological features were extracted and normalized for each heartbeat from standard ECG recordings obtained from the MIT-BIH Arrhythmia database. The GA-ANN Hybrid and PCA-ANN results are compared with the patameters i.e sensitivity, specificity and accuracy in the presence of noise. Hybrid methods provides a better, more accurate identification for presence of BBB from ECG recordings leading to more timely diagnosis and treatment outcomes.

Reference Paper-1: A Genetic Algorithm-Neural Network Wrapper Approach for Bundle Branch Block Detection

Author’s Name: Ragheed Allami, Andrew Stranieri, Venki Balasubramanian,and Herbert F Jelinek

Source: Computing in Cardiology


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SIMULATION VIDEO DEMO