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

TL;DR

This paper introduces cSeiz, an IoMT-based edge device for real-time seizure detection and control, achieving high accuracy with low power consumption, suitable for wearable and implantable healthcare solutions.

Contribution

It presents a novel energy-efficient seizure detection and drug delivery system combining hyper-synchronous detection and signal rejection, validated through simulation and proof of concept.

Findings

Seizure detector sensitivity of 96.9%

Specificity of 97.5%

Reduced power consumption compared to prior methods

Abstract

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 United States. Seizures in more than 30% of epilepsy patients are refractory to anti-epileptic drugs. An important biomedical research effort is focused on the development of an energy efficient implantable device for the real-time control of seizures. In this paper we propose an Internet of Medical Things (IoMT) based automated seizure detection and drug delivery system (DDS) for the control of seizures. The proposed system will detect seizures and inject a fast acting anti-convulsant drug at the onset to suppress seizure progression. The drug injection is performed in two stages. Initially, the seizure detector detects the seizure from the electroencephalography (EEG) signal using a hyper-synchronous signal detection circuit and a signal rejection algorithm (SRA). In the second stage, the drug is released in the seizure onset area upon seizure detection. The design was validated using a system-level simulation and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry leads to a considerable reduction of power consumption compared to previous approaches. The proposed approach can be generalized to other sensor modalities and the use of both wearable and implantable solutions, or a combination of the two.