Epilepsy is a common neurological disorder, and epileptic seizure detection is a scientific challenge since sometimes patient do not experience any alert. The objective of this research is to reduce the seizure detection time while maintaining high accuracy, and locate the brain hemisphere that is mostly affected by seizure. We argue that by using a decision forest (i.e., an ensemble of carefully built decision trees), instead of a single classifier such as a decision tree, we can afford to reduce epoch lengths (used for converting the ECoG and EEG signal into datasets) without compromising accuracy. This will allow us to build the future records in a shorter time resulting in a quicker seizure detection. In this paper, we apply two decision forest classifiers, called SysFor and Forest CERN, on an ECoG brain dataset. Our initial experiments on the dataset of a single patient indicate that decision forest algorithms such as SysFor and Forest CERN can reduce the seizure detection time significantly while maintaining 100% accuracy. They can also be used to identify the region of the brain of a patient that is mostly affected by seizure.