Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately.

中文翻译：

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HTsort：在多电极阵列上实现快速准确的尖峰排序

尖峰排序用于对尖峰（生理电极获得的动作电位）进行分类，旨在识别它们各自的发射单元。现在随着微电极技术的改进，已经开发出对多电极阵列（MEA）记录的尖峰进行分类。然而，如何同时提高分类精度和保持低时间复杂度成为一个难点。本文针对高密度多电极阵列提出了一种快速准确的尖峰排序方法，称为 HTsort。已对由阈值检测和聚类方法组成的传统管道进行了一些改进。首先，采用分治法利用电极空间信息实现预聚类。第二，聚类方法HDBSCAN（基于带噪声的应用程序的基于密度的空间聚类）用于对尖峰进行分类并检测重叠事件（多个尖峰同时触发）。第三，模板合并方法根据模板相似度和电极的空间分布，合并冗余导出的模板。最后，模板匹配方法用于解决重叠事件。我们的方法在我们自己构建的模拟数据和公开可用的数据上得到了验证，并与其他最先进的尖峰分拣机进行了比较。我们发现所提出的 HTsort 在准确性和时间消耗之间具有更有利的权衡。与 MountainSort 和 SpykingCircus 相比，当电极数为 64 及以下时，时间消耗至少减少 40%。与 HerdingSpikes 相比，分类准确率通常可以提高 10% 以上。同时，HTsort 对背景噪声表现出比其他分类器更强的鲁棒性。我们更先进的尖峰分选器将有助于神经生理学家更快、更准确地完成尖峰分选。