A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm Optimization

• Personalized classification of ECG heartbeats in five heartbeat types according to AAMI recommendation.
• A Block-based Neural Network (BBNN) has been used as the classifier.
• Particle Swarm Optimization algorithm has been used for training the BBNN.
• The performance evaluation using the MIT-BIH arrhythmia database shows a high classification accuracy of 97%.

The purpose of this paper is the classification of ECG heartbeats of a patient in five heartbeat types according to AAMI recommendation, using an artificial neural network. In this paper a Block-based Neural Network (BBNN) has been used as the classifier. The BBNN is created from 2-D array of blocks which are connected to each other. The internal structure of each block depends on the number of incoming and outgoing signals. The overall construction of the network is determined by the moving of signals through the network blocks. The Network structure and the weights are optimized using Particle Swarm Optimization (PSO) algorithm. The input of the BBNN is a vector which its elements are the features extracted from the ECG signals. In this paper Hermit function coefficient and temporal features which have been extracted from ECG signals, create the input vector of the BBNN. The BBNN parameters have been optimized by PSO algorithm which can overcome the possible changes of ECG signals from time-to-time and/or person-to-person variations. Therefore the trained BBNN has an unique structure for each person. The performance evaluation using the MIT-BIH arrhythmia database shows a high classification accuracy of 97%.