A novel extracellular spike detection algorithm based on sparse representation

Identification of spikes in the extracellular recording signals is a technical challenge because of large amounts of background noise and contributions of many neurons to recorded signals. In this paper, a novel method based on sparse representation is proposed for high accuracy and robust spike detection. Considering the diversity of spikes, a universal dictionary is first learned for giving a sparse representation to various spike signals. In addition, in order to improve the robustness to noise, we propose to use sparse coefficients as features for the discrimination of spikes. Finally, the number and locations of spike events in the recorded signal are determined through a thresholding process. Experimental results on both synthesized extracellular neural recordings and real data demonstrate that the proposed method performs much better than the existing methods in terms of both robustness and flexibility.

[1]  Leslie S. Smith,et al.  A tool for synthesizing spike trains with realistic interference , 2007, Journal of Neuroscience Methods.

[2]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[3]  Joel W. Burdick,et al.  Spike detection using the continuous wavelet transform , 2005, IEEE Transactions on Biomedical Engineering.

[4]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[5]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[6]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[7]  Zhigang Shang,et al.  Automatic extracellular spike denoising using wavelet neighbor coefficients and level dependency , 2015, Neurocomputing.

[8]  Sunghan Kim,et al.  Automatic spike detection based on adaptive template matching for extracellular neural recordings , 2007, Journal of Neuroscience Methods.

[9]  Sung June Kim,et al.  Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier , 2000, IEEE Transactions on Biomedical Engineering.

[10]  J. Okada,et al.  Multineuronal spike classification based on multisite electrode recording, whole-waveform analysis, and hierarchical clustering , 1999, IEEE Transactions on Biomedical Engineering.

[11]  G. Buzsáki.  Large-scale recording of neuronal ensembles , 2004, Nature Neuroscience.

[12]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[13]  Guangming Shi,et al.  Visual Orientation Selectivity Based Structure Description , 2015, IEEE Transactions on Image Processing.

[14]  Hagai Bergman,et al.  A personal computer-based spike detector and sorter: implementation and evaluation , 1992, Journal of Neuroscience Methods.

[15]  Markus Meister,et al.  Multi-neuronal signals from the retina: acquisition and analysis , 1994, Journal of Neuroscience Methods.

[16]  Panayiota Poirazi,et al.  A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings , 2015, Front. Neurosci..

[17]  Terrence J. Sejnowski,et al.  Learning Overcomplete Representations , 2000, Neural Computation.

[18]  Reid R. Harrison,et al.  A low-power integrated circuit for adaptive detection of action potentials in noisy signals , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[19]  Wenjun Yan,et al.  Medial prefrontal activity during delay period contributes to learning of a working memory task , 2014, Science.

[20]  Alain Rakotomamonjy,et al.  Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms , 2011, Signal Process..

[21]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..