Cardiac arrhythmia detection using linear and non-linear features of HRV signal

A. Sivanantham, S. Shenbaga Devi
2014 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies  
In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F3, F4, T3 and T4 from 5 patients with brain tumor and 4 healthy participants were recorded. After preprocessing, linear features in time and frequency domains and nonlinear ones such as fractal dimensions and entropies
more » ... s and entropies were extracted. Afterwards, the differentiation between2 groups was analyzed using Davies-Bouldin Index, LDA, KNN and SVM classifiers. According to the results of Davies-Bouldin Index, RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed and RMS and Theta Absolute Power features in resting state with eyes opened, had the most distinction between the two groups. In this stage classification of two groups using single features was done and the most accuracy of 88.89% was obtained for RMS feature in resting state with eyes closed. At the end, classification of two groups using all selected features was conducted and the maximum accuracy of 82.54% was obtained for RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed. According to the results, EEG linear features have a good capability of detecting brain tumor. As these features are simple and have low computational complexity, they can be used in online applications especially for periodic screening tests.
doi:10.1109/icaccct.2014.7019200 fatcat:acqavhge6jgchcjwr66lstuarm