A Seizure-Based Power Reduction SoC for Wearable EEG in Epilepsy

Saam Iranmanesh,Esther Rodriguez-Villegas,George Raikos,Syed Anas Imtiaz

doi:10.1109/access.2019.2948231

Saam Iranmanesh, Esther Rodriguez-Villegas + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2948231

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Abstract

Epilepsy is one of the most common serious brain disorders affecting 1% of the world population. Epileptic seizure events are caused by abnormal excessive neuronal activity in the brain, which may be associated with behavioural changes that severely affect the patients' quality of life. These events are manifested as abnormal activity in electroencephalography (EEG) recordings of individuals with epilepsy. This paper presents the on-chip implementation of an algorithm that, operating on the principle of data selection applied to seizures, would be able to reduce the power consumption of EEG devices, and consequently their size, thereby significantly increasing their usability. In order to reduce the power consumed by the on-chip implementation of the algorithm, mathematical approximations have been carried out to allow for an analog implementation, resulting in the power consumed by the system to be negligible in comparison to other blocks in an EEG device. The system has been fabricated in a 0.18 μm CMOS process, consumes 1.14 μW from a 1.25 V supply and achieves a sensitivity of 98.5% while only selecting 52.5% of the EEG data for transmission.

Highlights

Epilepsy is a serious neurological condition affecting more than 1% of the world population
This paper presents a scalable EEG data selection SoC targeting a different aspect of epilepsy, which is the diagnosis stage, for which the previously mentioned approaches are unsuitable
The system presented in this paper reduces the amount of data to be transmitted by a Wearable EEG (WEEG) device by continuously selecting sections of EEG data containing likely seizure activity

Summary

INTRODUCTION

Epilepsy is a serious neurological condition affecting more than 1% of the world population. The mentioned seizure detection systems require a training phase using patient-specific EEG data containing marked seizure events, which may be available during the post-diagnosis stage. It is important to note that the process of manually reviewing raw EEG data by neurologists is time consuming, taking approximately 2 h per 24 h of recordings [17] This approach, has the advantages of simultaneously reducing the power of the EEG system, and the amount of time required by neurologists to review the transmitted EEG data, since it still gives them access to the clinically relevant seizure information, whilst eliminating large amounts of recorded brain signal that has nothing diagnostically important in them.

SEIZURE SELECTION

SYSTEM FRONT END AND CONTROL CIRCUITRY

Findings

CONCLUSION