Abstract
Analysis of neuronal activities is essential in studying nervous system mechanisms. True interpretation of such mechanisms relies on detecting and sorting neuronal activities, which appear as action potentials or spikes in the recorded neural data. So far, several algorithms have been developed for spike sorting. In this paper, spike sorting was addressed using entropy measures. A method based on a modified version of approximate entropy was proposed for feature extraction, which captured the local variations in spike waveforms as well as global variation to create the feature space. Results showed that the entropy-based feature extraction method created more distinguishing features, which reduces spike sorting errors. The proposed method was capable of separate different spikes in small-scale structures, where the technique such as principal component analysis fails.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The codes used for the presented results in the manuscript have been publicly available through Figshare open access repository. The codes and data can be accessed via the following links: Codes: https://figshare.com/articles/journal_contribution/Neural_data_processing/17869667. Data: https://figshare.com/articles/dataset/RawNeuralDataFromCockroachBrain/16607747.
Code availability
Not applicable.
References
Eliades SJ, Wang X. Chronic multi-electrode neural recording in free-roaming monkeys. J Neurosci Methods. 2008;172:201–14.
Vargas-Irwin C, Donoghue JP. Automated spike sorting using density grid contour clustering and subtractive waveform decomposition. J Neurosci Methods. 2007;164:1–18.
Letelier JC, Weber PP. Spike sorting based on discrete wavelet transform coefficients. J Neurosci Methods. 2000;101:93–106.
Shoham S, Fellows MR, Normann RA. Robust, automatic spike sorting using mixtures of multivariate t-distributions. J Neurosci Methods. 2003;127:111–22.
Kyung Hwan K, Sung June K. Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier. IEEE Trans Biomed Eng. 2000;47:1406–11.
Hermle T, Schwarz C, Bogdan M. Employing ICA and SOM for spike sorting of multielectrode recordings from CNS. J Physiol Paris. 2004;98:349–56.
Aksenova TI, Chibirova OK, Dryga OA, Tetko IV, Benabid AL, Villa AE. An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animals. Methods. 2003;30:178–87.
Hulata E, Segev R, Ben-Jacob E. A method for spike sorting and detection based on wavelet packets and Shannon’s mutual information. J Neurosci Methods. 2002;117:1–12.
Farashi S, Abolhassani MD, Salimpour Y, Alirezaie J. Combination of PCA and undecimated wavelet transform for neural data processing. Ann Int Conf IEEE Eng Med Biol. 2010;2010:6666–9.
Pavlov A, Makarov V, Makarova I, Panetsos F. Sorting of neural spikes: when wavelet based methods outperform principal component analysis. Nat Comput. 2007;6:269–81.
Quiroga RQ, Nadasdy Z, Ben-Shaul Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 2004;16:1661–87.
Farashi S. Spike sorting method using exponential autoregressive modeling of action potentials. World Acad Sci Eng Technol Int J Med Health Biomed Bioeng Pharm Eng. 2015;8:864–70.
Golnar-Nik P, Farashi S, Safari M-S. The application of EEG power for the prediction and interpretation of consumer decision-making: a neuromarketing study. Physiol Behav. 2019;207:90–8.
Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA. 1991;88:2297–301.
Jung HK, Choi JH, Kim T. Solving alignment problems in neural spike sorting using frequency domain PCA. Neurocomputing. 2006;69:975–8.
Pouzat C, Mazor O, Laurent G. Using noise signature to optimize spike-sorting and to assess neuronal classification quality. J Neurosci Methods. 2002;122:43–57.
Franke F, Natora M, Boucsein C, Munk MH, Obermayer K. An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes. J Comput Neurosci. 2010;29:127–48.
Gray PR. Conditional probability analyses of the spike activity of single neurons. Biophys J. 1967;7:759–77.
Benesty J, Chen J, Huang Y. On the importance of the Pearson correlation coefficient in noise reduction. IEEE Trans Audio Speech Lang Process. 2008;16:757–65.
Agustín-Blas LE, Salcedo-Sanz S, Jiménez-Fernández S, Carro-Calvo L, Del Ser J, Portilla-Figueras JA. A new grouping genetic algorithm for clustering problems. Expert Syst Appl. 2012;39:9695–703.
Acknowledgements
I would like to thank Dr Mahmoud Sobhani, Dr Leila Kiani and Dr Reza Farahani for their helpful advices during spike detection and sorting procedures. Furthermore, I would like to thank Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences for sharing the neural data.
Funding
None.
Author information
Authors and Affiliations
Contributions
All analyses and writing the manuscript were performed by SF.
Corresponding author
Ethics declarations
Conflict of interest
Author has nothing to declare.
Ethics approval
The study was approved by the Ethical Council of Research of the Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran (Ref. 289218720).
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Farashi, S. Offline Spike Sorting Using Approximate Entropy. SN COMPUT. SCI. 3, 134 (2022). https://doi.org/10.1007/s42979-022-01025-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-022-01025-z