Epilepsy is a neurological disorder which has negative impact on human life quality. Epilepsy affects almost 1% of the world population necessitating a unified system for fast seizure detection as well as remote health monitoring to enhance the daily lives of the epilepsy patients. We envision a smart seizure detection framework in the edge of the Internet of Things (IoT) which is capable of detecting seizures as well as monitoring the patient's healthcare activity remotely. Detection of seizure is performed using the discrete wavelet transform, statistical feature extraction, and a naive Bayes (NB) classifier. The proposed system was implemented and validated using Simulink, ThingSpeak, and off-the-shelf microcontrollers. Experimental results show that the proposed system reduces latency by 44% compared to a cloud-IoT based system and reports a classification accuracy of 98.65%.