Veerabhadrappa R, Lim C P, Nguyen T T, Berk M, Tye S J, Monaghan P, Nahavandi S, Bhatti A
Institute for Intelligent Systems Research and Innovation, Deakin University, Vic 3216, Australia.
IMPACT Strategic Research Centre, Barwon Health, Deakin University, Vic 3216, Australia.
Sci Rep. 2016 Jun 24;6:28533. doi: 10.1038/srep28533.
Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.
细胞外数据分析已成为理解神经对刺激的生理反应的一种典型方法。由于记录环境的复杂性，这需要严格的技术。在本文中，我们强调了细胞外多电极记录和数据分析中的挑战以及与一些当前采用的方法相关的局限性。为了应对其中一些挑战，我们提出了一种以选择性分类形式的统一算法。选择性分类是围绕假设的生成模型构建的，该模型解决了由复杂神经元群体触发的尖峰的自然现象。该算法结合了双谱倒谱、特设聚类算法、小波变换、最小二乘法和相关概念，这些概念策略性地调整序列以进行特征描述并形成独特的聚类。此外，我们展示了噪声建模小波对重叠尖峰分类的影响。使用具有不同复杂程度的原始数据集和合成数据集对该算法进行评估，并使用广泛接受的定性和定量指标将性能制成表格以供比较。
