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Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. (English) Zbl 1059.94511

Summary: This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or Gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)
92C55 Biomedical imaging and signal processing
65T60 Numerical methods for wavelets
68W01 General topics in the theory of algorithms
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