In this study, we investigated three measures capable of detecting absence seizures with increased sensitivity based on different underlying assumptions. Namely, an information-based method known as Approximate Entropy, a nonlinear alternative (Order Index), and a linear variance analysis approach. The results on the long-term EEG data suggest increased accuracy in absence seizure detection achieving sensitivity as high as 97.33% with no further application of any sophisticated classification scheme.