{"title":"An Empirical Mode Decomposition Based Method for Action Potential Detection in Neural Raw Data","authors":"Sajjad Farashi, Mohammadjavad Abolhassani, Mostafa Taghavi Kani","volume":85,"journal":"International Journal of Medical and Health Sciences","pagesStart":45,"pagesEnd":50,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9997436","abstract":"
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.<\/p>\r\n","references":"[1]\tKyung 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.\r\n[2]\tI. Obeid and P. Wolf, \"Evaluation of spike detection algorithms for a brain-machine interface application,\u201d IEEE Trans Biomed Eng, vol. 51, no. 6, pp. 905\u2013911, 2004.\r\n[3]\tR. 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,\u201d IEEE Trans. Biomed. Eng., vol. 44, pp. 403\u2013412, 1997.\r\n[4]\tSunghan Kim, James McNames, \"Automatic spike detection based on adaptive template matching for extracellular neural recordings,\u201d J. Neurosci. Methods, vol.165, pp. 165\u2013174, 2007.\r\n[5]\tRobert J. Brychta et al., \"Wavelet Methods for Spike Detection in Mouse Renal Sympathetic Nerve Activity,\u201d IEEE Trans. Biomed Eng., vol. 54(1), pp. 82\u201393, January 2007.\r\n[6]\tShahjahan Shahid, Jacqueline Walker, and Leslie S Smith, \"A new spike detection algorithm for extracellular neural recordings,\u201d IEEE Trans. Biomed Eng., vol. 57, pp.853-866, 2010.\r\n[7]\tLewicki. M. S, \"A Review of Methods for Spike Sorting: The Detection and Classification of Neural Action Potentials,\u201d Network, Vol. 9, pp. 53- 78, 1998.\r\n[8]\tHuang, 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,\u201d Proceedings of the Royal Society of London, vol. 454, pp. 903\u2013995, 1998.\r\n[9]\tP. Flandrin, G. Rilling, and P. Gonc\u00b8alves, \"Empirical mode decomposition as a filter bank,\u201d IEEE Signal Processing Letters, vol. 11, no. 2, part 1, pp. 112\u2013114, 2004.\r\n[10]\tZhaohua Wu, Norden E. Huang, \"A study of the characteristics of white noise using the empirical mode decomposition method,\u201d Royal Society of London Proceedings Series A, vol. 460, Issue 2046, pp. 1597-1611, 2004.\r\n[11]\tKais Khaldi et al., \"Speech Enhancement via EMD,\u201d Eurasip Journal on Advances in Signal Processing,vol. 8, pp. 1-8, 2008.\r\n[12]\t D. L. Donoho and I. M. Johnstone, \"Adapting to unknown smoothness via wavelet shrinkage,\u201d Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200\u20131424, 1995.\r\n[13]\tLeslie S. Smith, Nhamoinesu Mtetwa, \"A tool for synthesizing spike trains with realistic interference,\u201d J. Neurosci. Methods, vol. 159, pp. 170\u2013180, 2007.\r\n","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 85, 2014"}