reconstructed state space features for classification of ecg signals

Background: cardiac arrhythmias are considered as one of the most serious health conditions; therefore, accurate and quick diagnosis of these conditions is highly paramount for the electrocardiogram (ecg) signals. moreover, are rather difficult for the cardiologists to diagnose with unaided eyes due to a close similarity of these signals in the time domain. objective: in this paper, an imagebased and machine learning method were presented in order to investigate the differences between the three cardiac arrhythmias of vf, vt, svt and the normal signal.material and methods: in this simulation study, the ecg data used are collected from 3 databases, including boston beth university arrhythmias center, creighton university, and mit-bih. the proposed algorithm was implemented using matlab r2015a software and its simulation. at first, the signal is transmitted to the state space using an optimal time delay. then, the optimal delay values are obtained using the particle swarm optimization algorithm and normalized mutual information criterion. furthermore, the result is considered as a binary image. then, 19 features are extracted from the image and the results are presented in the multilayer perceptron neural network for the purpose of training and testing. results: in order to classify n-vf, vt-svt, n-svt, vf-vt, vt-n-vf, n svtvf, vt-vf-svt and vt-vf-svt-n in the conducted experiments, the accuracy rates were determined at 99.5%, 100%, 94.98%, 100%,100%, 100%, 99.5%, 96.5% and 95%, respectively.conclusion: in this paper, a new approach was developed to classify the abnormal signals obtained from an ecg such as vt, vf, and svt compared to a normal signal. compared to other related studies, our proposed system significantly performed better.