Malik Muhammad H, Saeed Maryam, Kamboh Awais M
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:774-777. doi: 10.1109/EMBC.2016.7590816.
In neural spike sorting systems, the performance of the spike detector has to be maximized because it affects the performance of all subsequent blocks. Non-linear energy operator (NEO), is a popular spike detector due to its detection accuracy and its hardware friendly architecture. However, it involves a thresholding stage, whose value is usually approximated and is thus not optimal. This approximation deteriorates the performance in real-time systems where signal to noise ratio (SNR) estimation is a challenge, especially at lower SNRs. In this paper, we propose an automatic and robust threshold calculation method using an empirical gradient technique. The method is tested on two different datasets. The results show that our optimized threshold improves the detection accuracy in both high SNR and low SNR signals. Boxplots are presented that provide a statistical analysis of improvements in accuracy, for instance, the 75th percentile was at 98.7% and 93.5% for the optimized NEO threshold and traditional NEO threshold, respectively.
在神经尖峰分类系统中，尖峰检测器的性能必须最大化，因为它会影响所有后续模块的性能。非线性能量算子（NEO）因其检测精度和硬件友好架构而成为一种流行的尖峰检测器。然而，它涉及一个阈值阶段，其值通常是近似的，因此不是最优的。这种近似会降低实时系统中的性能，在实时系统中，信噪比（SNR）估计是一项挑战，尤其是在较低信噪比的情况下。在本文中，我们提出了一种使用经验梯度技术的自动且稳健的阈值计算方法。该方法在两个不同的数据集上进行了测试。结果表明，我们优化后的阈值提高了高信噪比和低信噪比信号的检测精度。文中给出了箱线图，对精度的提高进行了统计分析，例如，优化后的NEO阈值和传统NEO阈值的第75百分位数分别为98.7%和93.5%。
