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This paper proposes a new algorithm for seizure detection based on the evolution-like characteristics of a seizure. Most of the existing algorithms for automatic detection of the epileptic seizures in electroencephalograms (EEG) rely upon some pre-defined/patient-tunable detection threshold to classify the data as normal or abnormal. In this paper, we present a method for seizure detection in stereoencephalograms (SEEG) using frequency-weighted energy. The method does not require a threshold or any a priori information about the seizure for its detection. The method is gradient-based and any activity that exceeds the minimum duration satisfying our criteria is considered as a potential seizure activity. The performance of the algorithm is evaluated on 100 hours of single channel SEEG data obtained from five different patients. An overall sensitivity of 96.6% and a false detection rate of 0.21/h is obtained on the complete data.