Cao Yingqiu, Rakhilin Nikolai, Gordon Philip H, Shen Xiling, Kan Edwin C
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.
J Neurosci Methods. 2016 Mar 1;261:97-109. doi: 10.1016/j.jneumeth.2015.12.006. Epub 2015 Dec 21.
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.
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.
Our 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.
We 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.
细胞外神经记录的实时分析需要计算效率高的尖峰识别方法。肠神经系统（ENS）对人类健康很重要，但由于波形变化大，目前对其了解较少，且缺乏合适的尖峰识别算法。
在此，我们提出一种基于动态时间规整（DTW）的方法，该方法对时间和幅度的变化具有高耐受性。在相似度计算中对“快速DTW”采用自适应时间网格化显著降低了计算成本。自动阈值选择允许对细胞外记录进行实时分类。
我们的方法首先在不同噪声水平的合成数据上进行评估，与传统的基于互相关的模板匹配方法（CCTM）以及无时间规整的主成分分析+k均值聚类方法相比，提高了分类准确率和计算复杂度。然后我们的方法被应用于分析小鼠在机械和化学刺激下的肠神经记录。即使尖峰变化在宽度上大于毫秒且在幅度上大于毫伏，也能成功分类双相和单相尖峰。
与传统的模板匹配和聚类方法相比，快速DTW方法计算效率高，对波形变化具有高耐受性。
我们开发了一种自适应快速DTW算法，用于对具有大波形变化的ENS记录进行实时尖峰分类，以应对群体运动、环境变化和细胞异质性。
