This paper analyses seizure detection features and their combinations using a probability-based scalp EEG seizure detection framework developed by Marc Saab and Jean Gotman. Our method was evaluated on 525 h of data, including 88 seizures in 21 patients. The individual performances of the three features used by Saab and Gotman were compared to six alternative features, and combinations of these nine features were analyzed in order to find a superior detector. On a testing set with the combination of their three features, Saab and Gotman reported a sensitivity of 0.78, a false positive rate of 0.86/h, and a median detection delay of 9.8 s. Based on 10-fold cross-validation the testing performance of our implementation of their method achieved a sensitivity of 0.79, a false positive rate of 0.62/h, and a median detection delay of 21.3 s. A detector based on an alternative combination of features achieved sensitivity of 0.81, a false positive rate of 0.60/h, and a median detection delay of 16.9 s. By including filtering techniques, it was possible to achieve performance levels similar to Saab and Gotman using our implementation of their method, although this involved increases in detection delays. Of the seizure detection measures investigated, relative average amplitude, relative power, relative derivative, and coefficent of variation of amplitude provided the best performing combinations. These better-performing features can be employed together to make robust and reliable seizure detectors.