Fractal dimension analysis for spike detection in low SNR extracellular signals

Abstract

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.

Low fractal dimension modified drilling‐hole wall for PTFE high‐frequency board copper plating with plasma treatment

“…To characterize the differences in the complexity and roughness of hole wall surface topography after plasma treatment, fractal theory was introduced in this study. The fractal properties of hole morphology were calculated by the box‐counting algorithm.…”

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…By applying a Teager energy operator-detector on data, even higher noise levels are well-tolerated; thus, filtering stages may be skipped (Lieb et al, 2017 ). Another noise-resilient approach represents fractal analysis of neural recordings, and after concluding that segments containing spikes have inferior dimensionality compared to noise, spike detection can be achieved (Salmasi et al, 2016 ). As it can be candidly imagined, an action potential and its propagation in an extracellular space would not let the entropy content of the temporal dimension unchanged, so calculating it with a sliding window method proved to detect spikes with greater specificity (Farashi, 2018 ).…”

“…There is also a difference between first (waveform amplitude) and second (slope of the waveform) principal components (Navratilova et al, 2016 ). Principal component analysis (PCA), as one of the most popular dimensionality reduction methods (Salmasi et al, 2016 ; Allen et al, 2018 ), constructs a matrix of the largest variation-containing orthogonal basis vectors in the feature space (Chen et al, 2021 ), but extensive computations and storage requirements are inevitable (Regalia et al, 2016 ; Yang et al, 2017 ).…”

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

“…More recent algorithms which are entropy based [18] or based on fractal dimensions [12,13] cannot be classified to any of these categories. Despite this diversity, almost all spike detection algorithms mentioned above follow a two-step-procedure.…”

“…Several algorithms for spike detection have already been published [1,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Based on their methodological concept most of them can be divided into three categories (as suggested in [1]):…”