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Optimization and Implementation of Wavelet-based Algorithms for Detecting High-voltage Spindles in Neuron Signals

Published: 18 July 2019 Publication History

Abstract

This article presents a microcontroller unit (MCU) based simplified discrete wavelet transform (Sim-DWT) algorithm that can detect high-voltage spindles (HVSs) in local field potential (LFP) signals. The Sim-DWT algorithm operates in an 8-bit MCU, 8MHz operating clock and 16 sample points of buffers to detect HVSs with a frequency range of 5−15Hz. The requirement of only sixteen 8-bit sample points as the window length for calculation and no need for a multiplier render the Sim-DWT easy to implement in an MCU with limited hardware resources. The Sim-DWT is applied in an 8-bit MCU with 6mW power consumption (including IO ports) and was tested for detecting LFP signals in vivo. The design methods and the accuracy of three typical types of mother wavelet functions (Haar, DB4, Morlet) in the Sim-DWT were also tested and compared with those of a PC-based system. The experimental results showed that with appropriately designed cMW functions in the Sim-DWT, HVSs could be detected more accurately than they could be in PC-based software. The present study indicates that the optimized HVS detector (Sim-DWT) can be implemented in an 8-bit MCU with limited hardware resources and is suitable to serve as the digital core in a closed-loop deep brain stimulator microsystem in the future.

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      cover image ACM Transactions on Embedded Computing Systems
      ACM Transactions on Embedded Computing Systems  Volume 18, Issue 5
      September 2019
      157 pages
      ISSN:1539-9087
      EISSN:1558-3465
      DOI:10.1145/3349526
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 18 July 2019
      Accepted: 01 May 2019
      Revised: 01 February 2019
      Received: 01 September 2017
      Published in TECS Volume 18, Issue 5

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      Author Tags

      1. Discrete wavelet transform (DWT)
      2. Parkinson disease (PD)
      3. high-voltage spindle (HVS)

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      • National Health Research Institute, Taiwan

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