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Signal Compression Methods for Low-Power Implants

  • Author / Creator
    Dodd, Russell D
  • The pairing of implantable micro-electrode arrays with micro-electronic devices allows the observation of neural activity within nervous systems in a more comprehensive and potentially more effective way compared to single electrode recording. State-of-the-art tethered recording systems use implanted micro-electrode arrays and can record raw data rates of at least 24 Mbps over 100 channels. To make the systems fully implantable, the communication link needs to be wireless with the system power dissipation low enough for battery and/or near-field wireless operation. This thesis details a study of signal compression methods intended for implanted wireless low-power neural signal recording implementations. ASIC simulations of compression methods are presented for proof of concept and comparisons, while ASIC implementations provide power consumption measurements for select methods. It is shown by using a windowed noise-based dual-threshold neural spike detector that an energy savings of 90% can be expected compared to systems with no compression. Furthermore, an additional 80% compression per spike can be achieved using multilevel wavelet decomposition with no effect on the classification accuracy and relatively small increase in power consumption. Finally, a feature extraction method is presented and is simulated to demonstrate a neural spike compression ratio of 87% and classification accuracy of 96%.

  • Subjects / Keywords
  • Graduation date
    2014-11
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R35X0Q
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Electrical and Computer Engineering
  • Specialization
    • Integrated Circuits and Systems
  • Supervisor / co-supervisor and their department(s)
    • Cockburn, Bruce (Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Mushahwar, Vivian (Biomedical Engineering)
    • Vorobyov, Sergiy (Electrical and Computer Engineering)
    • Wilton, Steve (Electrical and Computer Engineering, University of British Columbia)