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Epilepsy is one of the most common neurological disorders affecting up to 1%of the world's population and approximately 2.5 million people in the UnitedStates. Seizures in more than 30% of epilepsy patients are refractory toanti-epileptic drugs. An important biomedical research effort is focused on thedevelopment of an energy efficient implantable device for the real-time controlof seizures. In this paper we propose an Internet of Medical Things (IoMT)based automated seizure detection and drug delivery system (DDS) for thecontrol of seizures. The proposed system will detect seizures and inject a fastacting anti-convulsant drug at the onset to suppress seizure progression. Thedrug injection is performed in two stages. Initially, the seizure detectordetects the seizure from the electroencephalography (EEG) signal using ahyper-synchronous signal detection circuit and a signal rejection algorithm(SRA). In the second stage, the drug is released in the seizure onset area uponseizure detection. The design was validated using a system-level simulation andconsumer electronics proof of concept. The proposed seizure detector reports asensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitryleads to a considerable reduction of power consumption compared to previousapproaches. The proposed approach can be generalized to other sensor modalitiesand the use of both wearable and implantable solutions, or a combination of thetwo.

cSeiz: An Edge-Device for Accurate Seizure Detection and Control for Smart Healthcare