睡眠(系统调用)
炸薯条
软件
计算机科学
电子工程
CMOS芯片
芯片上的系统
灵敏度(控制系统)
功率(物理)
嵌入式系统
脑电图
计算机硬件
工程类
物理
电信
神经科学
操作系统
生物
程序设计语言
量子力学
作者
Saam Iranmanesh,Esther Rodriguez–Villegas
标识
DOI:10.1109/tbcas.2017.2690908
摘要
This paper describes a full system-on-chip to automatically detect sleep spindle events from scalp EEG signals. These events, which are known to play an important role on memory consolidation during sleep, are also characteristic of a number of neurological diseases. The operation of the system is based on a previously reported algorithm, which used the Teager energy operator, together with the Spectral Edge Frequency (SEF50) achieving more than 70% sensitivity and 98% specificity. The algorithm is now converted into a hardware analog based customized implementation in order to achieve extremely low levels of power. Experimental results prove that the system, which is fabricated in a 0.18 μm CMOS technology, is able to operate from a 1.25 V power supply consuming only 515 nW, with an accuracy that is comparable to its software counterpart.
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