An Automated Approach for Epilepsy Detection Based on Tunable<i> Q</i>-Wavelet and Firefly Feature Selection Algorithm

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/ijbi/volume-2018/5812872/figures/5812872.fig.003a.svgz" name="5812872.fig.003a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>EEG Sample for patient with seizure-free</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/ijbi/volume-2018/5812872/figures/5812872.fig.003b.svgz" name="5812872.fig.003b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>EEG Sample for patient with seizures</td></tr></table>

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International Journal of Biomedical Imaging

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Figure 3

Figure 3: An Automated Approach for Epilepsy Detection Based on Tunable<i> Q</i>-Wavelet and Firefly Feature Selection Algorithm 

International Journal of Biomedical Imaging

An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

Figure 3