Seizures affect each patient differently, so personalization is a vital part of developing a reliable nonEEG based seizure detection system. This personalization must be done while the patient is undergoing video EEG monitoring in an epilepsy monitoring unit (EMU) because seizure detection by EEG is considered to be the ground truth. We propose the use of confidence interval analysis for determining how many seizures must be captured from a patient before we can reliably personalize such a seizure detection system for him/her. Our analysis indicates that 6 to 8 seizures are required. In addition, we create seizure likelihood tables for future use by said system by comparing the number of times a prespecified biosignal activity level is induced by seizure to the total number of occurrences of that level of activity. We focus on complex partial seizures in this paper because they are more difficult to detect than are generalized seizures.