ArticleJournal of neuroscience methods2016
A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability.
Article in Journal of neuroscience methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A semi-automated MEA spike sorting method for high-throughput assessment of cultured neurons.Stem cell reports · 2026Article
- Time dynamics of symptom progression in patients with acute pancreatitis: a Dynamic Time Warping analysis.Frontiers in medicine · 2025Article
- Dynamic Time Warping Identifies Functionally Distinct fMRI Resting State Cortical Networks Specific to VTA and SNc: A Proof of Concept.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Opportunities and Challenges for Single-Unit Recordings from Enteric Neurons in Awake Animals.Micromachines · 2018Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundComputationally 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
methodHere 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.
resultsOur method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and computational complexity over the conventional cross-correlation based template-matching method (CCTM) and PCA+k-means clustering without time warping. Our method is then applied to analyze the mouse enteric neural recording with mechanical and chemical stimuli. Successful classification of biphasic and monophasic spikes is achieved even when the spike variability is larger than millisecond in width and millivolt in magnitude. COMPARISON WITH EXISTING METHOD(S): In comparison with conventional template matching and clustering methods, the fastDTW method is computationally efficient with high tolerance to waveform variability.
conclusionsWe have developed an adaptive fastDTW algorithm for real-time spike classification of ENS recording with large waveform variability against colony motility, ambient changes and cellular heterogeneity.
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