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Biosystems
Volume 79, Issues 1-3, January-March 2005, Pages 159-171
Selection of Papers from the 5th International Workshop on Neural Coding
 
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doi:10.1016/j.biosystems.2004.09.028    
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Copyright © 2004 Elsevier Ireland Ltd All rights reserved.

Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients

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Olga K. Chibirovaa, Corresponding Author Contact Information, E-mail The Corresponding Author, Tetyana I. Aksenovaa, b, E-mail The Corresponding Author, Alim-Louis Benabida, c, E-mail The Corresponding Author, Stephan Chabardesa, E-mail The Corresponding Author, Steeve Larouched, E-mail The Corresponding Author, Jean Rouatd, E-mail The Corresponding Author and Alessandro E.P. Villaa, c, E-mail The Corresponding Author

aLaboratory of Preclinical Neuroscience; INSERM U318; CHUG Michallon Pavillon B; BP 217; F-38043 Grenoble Cedex 9; France

bInstitute of Applied System Analysis; Ukrainian Academy of Sciences; Prospekt Peremogy; 37; Kyiv 03056; Ukraine

cLaboratoire de Neurobiophysique; Université Joseph Fourier Grenoble 1; UFR Médecine CHUG Michallon Pavillon B; BP 217; F-38043 Grenoble Cedex 9; France

dDépartement de génie électronique et de génie informatique; IMSI; Univercité de Sherbrooke 2500 boul. de l’Univercité; Sherbrooke; Que.; Canada J1K 2R1


Available online 14 November 2004.

Abstract

The present study demonstrates the application of the Unsupervised Spike Sorting algorithm (USS) to separation of multi-unit recordings and investigation of neuronal activity patterns in the subthalamic nucleus (STN). This nucleus is the main target for deep brain stimulation (DBS) in Parkinsonian patients. The USS comprises a fast unsupervised learning procedure and allows sorting of multiple single units, if any, out of a bioelectric signal. The algorithm was tested on a simulated signal with different levels of noise and with application of Time and Spatial Adaptation (TSA) algorithm for denoising. The results of the test showed a good quality of spike separation and allow its application to investigation of neuronal activity patterns in a medical application. One hundred twenty-four single channel multi-unit records from STN of 6 Parkinsonian patients were separated with USS into 492 single unit trains. Auto- and crosscorrellograms for each unit were analyzed in order to reveal oscillatory, bursting and synchronized activity patterns. We analyzed separately two brain hemispheres. For each hemisphere the percentage of units of each activity pattern were calculated. The results were compared for the first and the second operated hemispheres of each patient and in total.

Keywords: Spike sorting; Neuronal activity patterns; Basal ganglia; STN; Parkinson’s disease

Article Outline

1. Introduction
2. Methods
2.1. Model
2.2. USS algorithm
2.2.1. Extracting spikes from the signal and estimation of its trajectory in phase space
2.2.2. Unsupervised iterative learning procedure
2.2.3. On-line classification
2.3. USS software implementation
2.4. Test files
2.5. Data files
3. Resuls
3.1. Evaluation of USS performance
3.2. Time domain analysis of spike trains
3.3. Frequency domain analysis of spike trains
4. Discussion
References






Corresponding Author Contact InformationCorresponding author.

Biosystems
Volume 79, Issues 1-3, January-March 2005, Pages 159-171
Selection of Papers from the 5th International Workshop on Neural Coding
 
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