A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability.

ABSTRACT:

Background

Computationally efficient spike recognition methods are required for real-time analysis of extracellular neural recordings. The enteric nervous system (ENS) is important to human health but less well-understood with few appropriate spike recognition algorithms due to large waveform variability.

New method

Here we present a method based on dynamic time warping (DTW) with high tolerance to variability in time and magnitude. Adaptive temporal gridding for "fastDTW" in similarity calculation significantly reduces the computational cost. The automated threshold selection allows for real-time classification for extracellular recordings.

Results

Our method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and

SUBMITTER: Cao Y 

PROVIDER: S-EPMC4749467 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature