An Empirical Mode Decomposition Based Method for Action Potential Detection in Neural Raw Data
Information in the nervous system is coded as firing patterns of electrical signals called action potential or spike so an essential step in analysis of neural mechanism is detection of action potentials embedded in the neural data. There are several methods proposed in the literature for such a purpose. In this paper a novel method based on empirical mode decomposition (EMD) has been developed. EMD is a decomposition method that extracts oscillations with different frequency range in a waveform. The method is adaptive and no a-priori knowledge about data or parameter adjusting is needed in it. The results for simulated data indicate that proposed method is comparable with wavelet based methods for spike detection. For neural signals with signal-to-noise ratio near 3 proposed methods is capable to detect more than 95% of action potentials accurately.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1090743Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2026
 Kyung H. Kim, Sung J. Kim, "A Wavelet-Based Method for Action Potential Detection From Extracellular Neural Signal Recording With Low Signal-to-Noise Ratio," IEEE Trans. biomed. eng., vol. 50, no. 8, pp. 999-1011, 2003.
 I. Obeid and P. Wolf, "Evaluation of spike detection algorithms for a brain-machine interface application,” IEEE Trans Biomed Eng, vol. 51, no. 6, pp. 905–911, 2004.
 R. Chandra and L.M. Optican, "Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an online real-time neural network,” IEEE Trans. Biomed. Eng., vol. 44, pp. 403–412, 1997.
 Sunghan Kim, James McNames, "Automatic spike detection based on adaptive template matching for extracellular neural recordings,” J. Neurosci. Methods, vol.165, pp. 165–174, 2007.
 Robert J. Brychta et al., "Wavelet Methods for Spike Detection in Mouse Renal Sympathetic Nerve Activity,” IEEE Trans. Biomed Eng., vol. 54(1), pp. 82–93, January 2007.
 Shahjahan Shahid, Jacqueline Walker, and Leslie S Smith, "A new spike detection algorithm for extracellular neural recordings,” IEEE Trans. Biomed Eng., vol. 57, pp.853-866, 2010.
 Lewicki. M. S, "A Review of Methods for Spike Sorting: The Detection and Classification of Neural Action Potentials,” Network, Vol. 9, pp. 53- 78, 1998.
 Huang, N., Z. Shen, S. Long, M. Wu, E. Shih, Q. Zheng, C. Tung, and H. Liu, "The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis,” Proceedings of the Royal Society of London, vol. 454, pp. 903–995, 1998.
 P. Flandrin, G. Rilling, and P. Gonc¸alves, "Empirical mode decomposition as a filter bank,” IEEE Signal Processing Letters, vol. 11, no. 2, part 1, pp. 112–114, 2004.
 Zhaohua Wu, Norden E. Huang, "A study of the characteristics of white noise using the empirical mode decomposition method,” Royal Society of London Proceedings Series A, vol. 460, Issue 2046, pp. 1597-1611, 2004.
 Kais Khaldi et al., "Speech Enhancement via EMD,” Eurasip Journal on Advances in Signal Processing,vol. 8, pp. 1-8, 2008.
 D. L. Donoho and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage,” Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200–1424, 1995.
 Leslie S. Smith, Nhamoinesu Mtetwa, "A tool for synthesizing spike trains with realistic interference,” J. Neurosci. Methods, vol. 159, pp. 170–180, 2007.