Abstract
The present study reports an approach for automatic classification of extracellularly recorded action potentials (spikes). The recorded signal is observed at discrete times and characterized by high level of background noise and occurrence of the spikes at random time. The classification of spike waveform is considered as a pattern recognition problem of special segments of signal that correspond to the appearance of spikes. The spikes generated by one neuron should be recognized as members of the same class. We describe the spike waveform as an ordinary differential equation with perturbation. This allows us to characterize the signal distortions in both amplitude and phase. We have developed an iteration-learning algorithm that estimates the number of classes and their centers according to the distance between spike trajectories in phase space. The estimation of trajectories in phase space required calculation of the first and second order derivatives and the integral operators with piecewise polynomial kernels were used. This approach is computational efficient and of potential use for real time situations, in particular during neurosurgical procedures.
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Krack, P., Pollak, P., Limousin, P., Benazzouz, A., Deuschl, G., Benabid, A. L.: From off-Period Dystonia to Peak-Dose Chorea: the Clinical Spectrum of Varying Subthalamic Nucleus Activity. Brain 122 (1999) 1133–1146
Limousin, P., Krack, P., Pollak, P., Benazzouz, A., Ardouin, C., Hoffmann, D., Benabid, A. L.: Electrical Stimulation of the Subthalamic Nucleus in Advanced Parkinson’s Disease. New England Journal of Medicine 339(16) (1998) 1105–1111
Schmidt, E. M.: Computer Separation of Multi-Unit Neuroelectric Data: a Review. Journal of Neuroscience Methods 12 (1998) 95–111
Chandra, R., Optican, L. M.: Detection, Classification, and Superposition Resolution of Action Potentials in Multiunit Single-Channel Recordings by an On-Line Real-Time Neural Network. IEEE Trans. Biomed. Eng. 44 (1997) 403–412
Kim, K. H., Kim, S. J.: Neural Spike Sorting under Nearly 0-dB Signal-to-Noise Ratio Using Nonlinear Energy Operator and Artificial Neural-Network Classifier, IEEE Trans. Biomed. Eng. 47 (2000) 1406–1411
Zouridakis, G., Tam, D. C.: Identification of Reliable Spike Templates in Multi-Unit Ex-tracellular Recordings Using Fuzzy Clustering. Computer Methods & Programs in Biomedicine 61 (2000) 91–98
Hulata, E., Segev, R., Ben-Jacob, E.: A Method for Spike Sorting and Detection Based on Wavelet Packets and Shannon’s Mutual Information, Journal of Neuroscience Methods 117 (2002) 1–12
Bogoljubov, N. N., Mitropolsky, Y. A.: Asymptotic Methods in the Theory of non-Linear Oscillations. 2nd edn. Gordon and Breach, New York (1961)
Gudzenko, L. I.: Statistical Method for Self-Oscillating System Characteristics Detection. Izvestiia Vuzov Radiophysics 5 (1962) 573–587 (In Russian)
Aksenova, T. I., Tetko, I. V., Ivakhnenko, A. G., Villa, A. E. P., Welsh, W. J., Zielinski W. L.: Pharmaceutical Fingerprinting in Phase Space. 1. Construction of Phase Fingerprints. Anal. Chem. 71(13) (1999) 2423–2430
Aksenova, T. I., Tetko, I. V., Dryga, O. A., Chibirova, O. K., Villa, A. E. P.: Detection and Separation of Extracellular Neuronal Discharges. Smart Engineering System Design, Proc. ANNIE.2001, Vol. 11. ASME Press, New York (2001) 557–562
Aksenova, T. I., Shelekhova, V. Y.: Fast Algorithms of Derivative Estimation on Noisy Observations. SAMS 18–19 (1995) 159–163
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Aksenova, T.I., Chibirova, O.K., Benabid, AL., Villa, A.E.P. (2002). Nonlinear Oscillation Models for Spike Separation. In: Colosimo, A., Sirabella, P., Giuliani, A. (eds) Medical Data Analysis. ISMDA 2002. Lecture Notes in Computer Science, vol 2526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36104-9_7
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DOI: https://doi.org/10.1007/3-540-36104-9_7
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