Design of a Low-Power and Low-Noise Neural Recording Front-End Block for Seizure Detection
سال انتشار: 1397
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 68
فایل این مقاله در 16 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_TMCH-1-3_002
تاریخ نمایه سازی: 23 تیر 1404
چکیده مقاله:
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 ۱۸۰ nm CMOS technology, leveraging the TSMC BSIM library, and simulations are conducted using HSPICE ۲۰۰۸ 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.
کلیدواژه ها:
نویسندگان
M.
M.Sc. in Electrical Electronics, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
J.
Associated Professor, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :