Letters: A new action potential classifier using 3-Gaussian model fitting

Authors:
Seong-eun Roh;Joon Hwan Choi;Taejeong Kim
Affiliations:
Digital Media Business, Samsung Electronics Co. Ltd., Suwon 443-742, Korea;School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul 151-742, Korea;School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul 151-742, Korea
Venue:
Neurocomputing
Year:
2008

Citing 3
Cited 1

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Bayesian modeling and classification of neural signals

Neural Computation
De-noising by soft-thresholding

IEEE Transactions on Information Theory

Automatic extracellular spike detection with piecewise optimal morphological filter

Neurocomputing

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Abstract

Spike sorting is a prerequisite for all researches on multi-channel extracellular neural signal recordings. In this paper, we develop a new method for action potential classification. We introduce a mathematical model consisting of three Gaussian waveforms, which appropriately represents the general shapes of action potentials. Then we search for the best-fit waveform for each noise-corrupted spike based on the model, using peak fitting method. These processes result in increased separability among different classes of action potentials. The performance of the proposed method is assessed with synthesized neural recordings composed by spike templates and white Gaussian noise in various SNR environments.