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

 

مشخصات مقاله
ترجمه عنوان مقاله تشخیص تشنج الکتروانسفالوگرافی EEG مقیاس پذیر بر روی یک معماری چند هسته ای بسیار کم قدرت
عنوان انگلیسی مقاله Scalable EEG seizure detection on an ultra low power multi-core architecture
انتشار مقاله سال 2016
تعداد صفحات مقاله انگلیسی  4 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
رشته های مرتبط  مهندسی پزشکی – مهندسی کامپیوتر – پزشکی
گرایش های مرتبط  بیوالکتریک – معماری کامپیوتری – مغز و اعصاب
نوع ارائه مقاله
کنفرانس
مجله / کنفرانس  Biomedical Circuits and Systems Conference
دانشگاه Energy Efficient Embedded Systems Lab, DEI, University of Bologna, Italy
شناسه دیجیتال – doi
https://doi.org/10.1109/BioCAS.2016.7833731
کد محصول E11746
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I.Introduction
II.Materials and Methods
III.Experimental Results
IV.Conclusion and Future Work

 

بخشی از متن مقاله:

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].

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