Literature DB >> 27638107

An implantable ENG detector with in-system velocity selective recording (VSR) capability.

Chris Clarke1, Robert Rieger2, Martin Schuettler3, Nick Donaldson4, John Taylor5.   

Abstract

Detection and classification of electroneurogram (ENG) signals in the peripheral nervous system can be achieved by velocity selective recording (VSR) using multi-electrode arrays. This paper describes an implantable VSR-based ENG recording system representing a significant development in the field since it is the first system of its type that can record naturally evoked ENG and be interfaced wirelessly using a low data rate transcutaneous link. The system consists of two CMOS ASICs one of which is placed close to the multi-electrode cuff array (MEC), whilst the other is mounted close to the wireless link. The digital ASIC provides the signal processing required to detect selectively ENG signals based on velocity. The design makes use of an original architecture that is suitable for implantation and reduces the required data rate for transmission to units placed outside the body. Complete measured electrical data from samples of the ASICs are presented that show that the system has the capability to record signals of amplitude as low as 0.5 μV, which is adequate for the recording of naturally evoked ENG. In addition, measurements of electrically evoked ENG from the explanted sciatic nerves of Xenopus Laevis frogs are presented.

Entities:  

Keywords:  Biomedical signal processing; Biomedical transducers; Microelectronic implants; Neural prosthesis

Mesh:

Year:  2016        PMID: 27638107     DOI: 10.1007/s11517-016-1567-9

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  9 in total

1.  The theory of velocity selective neural recording: a study based on simulation.

Authors:  John Taylor; Martin Schuettler; Chris Clarke; Nick Donaldson
Journal:  Med Biol Eng Comput       Date:  2012-02-24       Impact factor: 2.602

2.  Velocity Selective Neural Signal Recording Using a Space-Time Electrode Array.

Authors:  Fatemeh Karimi; Saeid R Seydnejad
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-12-11       Impact factor: 3.802

3.  Multiple-electrode nerve cuffs for low-velocity and velocity-selective neural recording.

Authors:  J Taylor; N Donaldson; J Winter
Journal:  Med Biol Eng Comput       Date:  2004-09       Impact factor: 2.602

4.  Very low-noise ENG amplifier system using CMOS technology.

Authors:  Robert Rieger; Martin Schuettler; Dipankar Pal; Chris Clarke; Peter Langlois; John Taylor; Nick Donaldson
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-12       Impact factor: 3.802

5.  Experimental validation of the nerve conduction velocity selective recording technique using a multi-contact cuff electrode.

Authors:  K Yoshida; G A M Kurstjens; K Hennings
Journal:  Med Eng Phys       Date:  2009-09-16       Impact factor: 2.242

6.  Fibre-selective recording from the peripheral nerves of frogs using a multi-electrode cuff.

Authors:  Martin Schuettler; Nick Donaldson; Vipin Seetohul; John Taylor
Journal:  J Neural Eng       Date:  2013-05-03       Impact factor: 5.379

7.  The Cooper cable: an implantable multiconductor cable for neurological prostheses.

Authors:  P E Donaldson
Journal:  Med Biol Eng Comput       Date:  1983-05       Impact factor: 2.602

8.  A new method for spike extraction using velocity selective recording demonstrated with physiological ENG in Rat.

Authors:  B W Metcalfe; D J Chew; C T Clarke; N de N Donaldson; J T Taylor
Journal:  J Neurosci Methods       Date:  2015-05-14       Impact factor: 2.390

9.  A microchannel neuroprosthesis for bladder control after spinal cord injury in rat.

Authors:  Daniel J Chew; Lan Zhu; Evangelos Delivopoulos; Ivan R Minev; Katherine M Musick; Charles A Mosse; Michael Craggs; Nicholas Donaldson; Stéphanie P Lacour; Stephen B McMahon; James W Fawcett
Journal:  Sci Transl Med       Date:  2013-11-06       Impact factor: 17.956

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.