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A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

  • Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

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Metadaten
Author:Florian Lieb, Hans-Georg Stark, Christiane Thielemann
DOI:https://doi.org/10.1088/1741-2552/aa654b
Parent Title (English):Journal of Neural Engineering
Document Type:Article
Language:English
Year of Completion:2017
Date of first Publication:2017/03/30
Release Date:2017/05/04
Tag:extracellular recording; spike detection; wavelet TEO
GND Keyword:Neuronales Netz
Volume:2017
Issue:14
First Page:1
Last Page:13
Urheberrecht:0
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Intelligent Systems / Clean Tech
Intelligent Systems / Intelligent Sensors and Signals
Materials / Clean Tech
Materials / Material Testing & Sensor Technology
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
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