Optimization of Continuous Wavelet Coefficients for Neural Spike Sorting

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Soleymankhani Amir; Shalchyan Vahid

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Paper Information

Journal: IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING Year:2018 | Volume:12 | Issue:2 Page(s): 85-96

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Information Journal Paper

Title

Optimization of Continuous Wavelet Coefficients for Neural Spike Sorting

Author(s)

Soleymankhani Amir | Shalchyan Vahid | Issue Writer Certificate

Keywords

Spike SortingQ1

Action PotentialQ2

Continuous Wavelet TransformQ1

Principal Component AnalysisQ1

OptimizationQ2

Abstract

The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, Spike Sorting methods should be utilized to obtain the activity of single neurons. Based on the structural properties of the neuron, such as its dendritic tree, and the distance and direction of it relative to the electrode, it can be claimed that the form of its spike waveform is unique and constant. However, Spike Sorting under low signal-to-noise ratio (SNR) conditions is always accompanied with challenges. A Spike Sorting algorithm usually consists of three sections including the spike detection, feature extraction, and classification. In this paper, a method based on Optimization of continuous wavelet coefficients is presented which is effective in low SNR values. In the proposed method, after the calculation of the parameterized wavelet coefficients, using the Euclidean distance and the area under the receiver operator characteristic curve, the best parameters were chosen to increase the separation of the features, so that a suitable scale was first found with the Euclidean distance criterion and then the translation parameter was obtained with the second criterion. In this research k-means algorithm was used for the clustering as a simple but efficient method. For evaluation, three simulated data sets were made in 9 different SNRs with a modeled background noise. The obtained results from simulated data showed that the Optimization of parameters in Continuous Wavelet Transform using the proposed algorithm could effectively improve the Spike Sorting performance compared to Principal Component Analysis method.

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APA: Copy

Soleymankhani, Amir, & Shalchyan, Vahid. (2018). Optimization of Continuous Wavelet Coefficients for Neural Spike Sorting. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, 12(2 ), 85-96. SID. https://sid.ir/paper/81622/en

Vancouver: Copy

Soleymankhani Amir, Shalchyan Vahid. Optimization of Continuous Wavelet Coefficients for Neural Spike Sorting. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING[Internet]. 2018;12(2 ):85-96. Available from: https://sid.ir/paper/81622/en

IEEE: Copy

Amir Soleymankhani, and Vahid Shalchyan, “Optimization of Continuous Wavelet Coefficients for Neural Spike Sorting,” IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, vol. 12, no. 2 , pp. 85–96, 2018, [Online]. Available: https://sid.ir/paper/81622/en

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