An Ultra-Low Power RSSI Amplifier for EEG Feature Extraction to Detect Seizures

被引：5

作者：

Zhang, Yuqing ^{[1
]}

Mirchandani, Nikita ^{[1
]}

Abdelfattah, Safaa ^{[1
]}

Onabajo, Marvin ^{[1
]}

Shrivastava, Aatmesh ^{[1
]}

机构：

[1] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2022年 / 69卷 / 02期

基金：

美国国家科学基金会;

关键词：

Machine learning; analog computing; EEG-based seizure detection; support-vector machine; received signal strength indicator (RSSI); switched capacitor circuit;

D O I：

10.1109/TCSII.2021.3099056

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This brief presents an ultra-low power received signal strength indicator (RSSI) amplifier circuit that can be used to detect seizures through feature extraction from electroen-cephalography (EEG) signals. The RSSI-based feature extraction method provides a low-power area-efficient solution for analog computing based seizure detection hardware. A 6-stage RSSI amplifier circuit was designed in 65nm CMOS technology to cover the dynamic range of EEG signals with an area of 266 mu m x 531 mu m. Measurement results of the RSSI circuit show that it has a power consumption of 31.6nW, covers a 45dB dynamic range, and has a linearity error of less than +/- 1dB.

引用

页码：329 / 333

页数：5

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