Salmasi Mehrdad, Büttner Ulrich, Glasauer Stefan
Center for Sensorimotor Research, Ludwig-Maximilian University, Munich, Germany. German Center for Vertigo and Balance Disorders, Ludwig-Maximilian University, Munich, Germany. Graduate School of Systemic Neurosciences, Ludwig-Maximilian University, Munich, Germany.
J Neural Eng. 2016 Jun;13(3):036004. doi: 10.1088/1741-2560/13/3/036004. Epub 2016 Apr 11.
Many algorithms have been suggested for detection and sorting of spikes in extracellular recording. Nevertheless, it is still challenging to detect spikes in low signal-to-noise ratios (SNR). We propose a spike detection algorithm that is based on the fractal properties of extracellular signals and can detect spikes in low SNR regimes. Semi-intact spikes are low-amplitude spikes whose shapes are almost preserved. The detection of these spikes can significantly enhance the performance of multi-electrode recording systems.
Semi-intact spikes are simulated by adding three noise components to a spike train: thermal noise, inter-spike noise, and spike-level noise. We show that simulated signals have fractal properties which make them proper candidates for fractal analysis. Then we use fractal dimension as the main core of our spike detection algorithm and call it fractal detector. The performance of the fractal detector is compared with three frequently used spike detectors.
We demonstrate that in low SNR, the fractal detector has the best performance and results in the highest detection probability. It is shown that, in contrast to the other three detectors, the performance of the fractal detector is independent of inter-spike noise power and that variations in spike shape do not alter its performance. Finally, we use the fractal detector for spike detection in experimental data and similar to simulations, it is shown that the fractal detector has the best performance in low SNR regimes.
The detection of low-amplitude spikes provides more information about the neural activity in the vicinity of the recording electrodes. Our results suggest using the fractal detector as a reliable and robust method for detecting semi-intact spikes in low SNR extracellular signals.
已经提出了许多用于细胞外记录中尖峰检测和分类的算法。然而，在低信噪比（SNR）情况下检测尖峰仍然具有挑战性。我们提出了一种基于细胞外信号分形特性的尖峰检测算法，该算法能够在低SNR条件下检测尖峰。半完整尖峰是低幅度尖峰，其形状几乎得以保留。这些尖峰的检测可以显著提高多电极记录系统的性能。
通过向尖峰序列添加三种噪声成分来模拟半完整尖峰：热噪声、峰间噪声和尖峰水平噪声。我们表明，模拟信号具有分形特性，这使其成为分形分析的合适候选对象。然后，我们将分形维数用作尖峰检测算法的核心，并将其称为分形检测器。将分形检测器的性能与三种常用的尖峰检测器进行比较。
我们证明，在低SNR情况下，分形检测器具有最佳性能，检测概率最高。结果表明，与其他三种检测器不同，分形检测器的性能与峰间噪声功率无关，并且尖峰形状的变化不会改变其性能。最后，我们将分形检测器用于实验数据中的尖峰检测，与模拟结果类似，结果表明分形检测器在低SNR条件下具有最佳性能。
低幅度尖峰的检测提供了有关记录电极附近神经活动的更多信息。我们的结果表明，使用分形检测器作为一种可靠且强大的方法来检测低SNR细胞外信号中的半完整尖峰。
