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

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.