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Mtetwa, N. and Smith, L.S. (2006) Smoothing and Thresholding in Neuronal Spike Detection. Neurocomputing, 69, 1366-1370.
http://dx.doi.org/10.1016/j.neucom.2005.12.108

has been cited by the following article:

  • TITLE: Non-Parametric Local Maxima and Minima Finder with Filtering Techniques for Bioprocess

    AUTHORS: K. K. L. B. Adikaram, M. A. Hussein, M. Effenberger, T. Becker

    KEYWORDS: Extrema Point, First Derivative, Peak Finder, Peaks and Valleys, Maxima and Minima, Second Derivative

    JOURNAL NAME: Journal of Signal and Information Processing, Vol.7 No.4, October 11, 2016

    ABSTRACT: Typically extrema filtration techniques are based on non-parametric properties such as magnitude of prominences and the widths at half prominence, which cannot be used with data that possess a dynamic nature. In this work, an extrema identification that is totally independent of derivative-based approaches and independent of quantitative attributes is introduced. For three consecutive positive terms arranged in a line, the ratio (R) of the sum of the maximum and minimum to the sum of the three terms is always 2/n, where n is the number of terms and 2/3 ≤ R ≤ 1 when n = 3. R > 2/3 implies that one term is away from the other two terms. Applying suitable modifications for the above stated hypothesis, the method was developed and the method is capable of identifying peaks and valleys in any signal. Furthermore, three techniques were developed for filtering non-dominating, sharp, gradual, low and high extrema. Especially, all the developed methods are non-parametric and suitable for analyzing processes that have dynamic nature such as biogas data. The methods were evaluated using automatically collected biogas data. Results showed that the extrema identification method was capable of identifying local extrema with 0% error. Furthermore, the non-parametric filtering techniques were able to distinguish dominating, flat, sharp, high, and low extrema in the biogas data with high robustness.