مقاله انگلیسی رایگان در مورد تشخیص تشنج الکتروانسفالوگرافی EEG مقیاس پذیر – IEEE 2016

Abstract

Energy efficient processing architectures represent key elements for wearable and implantable medical devices. Signal processing of neural data is a challenge in new designs of Brain Machine Interfaces (BMI). A highly efficient multicore platform, designed for ultra low power processing allows the execution of complex algorithms complying with real time requirements. This paper describes the implementation and optimization of a seizure detection algorithm on a multi-core digital integrated circuit designed for energy efficient applications. The proposed architecture is able to implement ultra low power parallel processing seizure detection on 23 electrodes within a power budget of 1 mW, outperforming implementations on commercial MCUs by up to 100 times in terms of performance and up to 80 times in terms of energy efficiency still providing high versatility and scalability, opening the way to the development of efficient implantable and wearable smart systems.

Introduction

Recent advancements in Brain Machine Interfaces (BMI) are paving the way to systems for treating various neural diseases. Among these studies, treating epilepsy has a great impact on public health, since this neural disorder affects approximately 1% of the world population and can result in severe and disabling pathologies. In epilepsy, the normal pattern of neuronal activity becomes disturbed, causing depression, convulsions or loss of consciousness. The clinical measurement of the brain electrical activity through the analysis of the EEG traces, and the expertise of the neurologist can diagnose the epileptic seizure recognizing certain changes in patterns of amplitude and frequency of the neural signal. The therapeutic approach is mainly pharmacological or surgical. Unfortunately, for about 30% of epileptic subjects, seizures cannot be controlled with drugs delivery nor surgical techniques; but react to neuromodulation [1], a technique based on direct electrical stimulation of the brain tissue. In this scenario, the development of automatic closed loop neuromodulation systems can reduce the time of reaction many orders of magnitude more than human intervention. Furthermore, a closedloop system provides stimulation only when triggered by seizure detection, hence it is less traumatic wrt first generations of neuromodulators, which just deliver continuous, constant stimulation [2].