Transactions on Machine Intelligence

Transactions on Machine Intelligence

Design of a Low-Power and Low-Noise Neural Recording Front-End Block for Seizure Detection

Document Type : Original Article

Authors
M. Pourkarimi Khiavi 1
J. Javidan 2
1 M.Sc. in Electrical Electronics, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2 Associated Professor, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
10.47176/TMI.2018.130
Abstract
The design of a dedicated block within an epilepsy seizure detection system, intended for both medical and localized applications, plays a crucial role in amplifying vital and neural signals from the body, particularly brain and heart signals, thereby assisting in the precise diagnosis of various disease types. This paper focuses on the design and development of the front-end circuit of neural signal recording systems, which primarily consists of an amplifier and a bandpass filter. A key objective in this design is achieving low power consumption and minimal noise while maintaining high performance. To accomplish this, an amplifier with an RFC (resistor-feedback capacitor) structure is selected, offering the advantage of delivering high gain and reduced noise at comparable power levels. Furthermore, by employing an elliptic bandpass filter configured as a Gm-C (transconductance-capacitor) filter, the system effectively addresses the challenges posed by signal ripple, resulting in enhanced signal quality, lower power usage, minimized noise, and a smaller circuit footprint. The proposed design is implemented using 180 nm CMOS technology, leveraging the TSMC BSIM library, and simulations are conducted using HSPICE 2008 software to validate the system’s performance. This work contributes valuable insights for developing efficient, compact, and reliable neural signal processing modules for biomedical applications.
Keywords
Epilepsy Seizure Detection
Front-End Block
Amplifier
Gm-C Filter
Low Power
Low Noise
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Transactions on Machine Intelligence
Volume 1, Issue 3
Summer 2018
Pages 130-145

  • Receive Date 03 June 2018
  • Revise Date 15 July 2018
  • Accept Date 11 September 2018