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A neural network appoach to real-time spike discrimination during simultaneous recording from several multi-unit nerve filaments
Umeå University, Faculty of Medicine, Radiation Sciences.
Umeå University, Faculty of Medicine, Integrative Medical Biology, Physiology.
Umeå University, Faculty of Medicine, Integrative Medical Biology, Physiology.
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1996 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, Vol. 64, no 2, 181-187 p.Article in journal (Refereed) Published
Abstract [en]

A multi-channel, real-time, unsupervised spike discriminator was developed in order to reconstruct single spike trains from several simultaneously recorded multi-unit nerve filaments. The program uses a Self Organising Map (SOM) algorithm for the classification of the spikes. In contrast to previous similar techniques, the described method is made for use on a PC, and the method may thus be implemented at relatively low cost. In order to test the accuracy of the program, a robustness test was performed, where noise with different RMS levels was superimposed on the spikes. Furthermore, the maximal classification rate was determined. The program is easy to use, since the only manual inputs needed are the voltage threshold for spike detection, and the number of units present in each recorded nerve filament.

Place, publisher, year, edition, pages
1996. Vol. 64, no 2, 181-187 p.
Spike discrimination, neural network, self organising map, real-time, multi-unit train, multiple electrode, nerve filament
URN: urn:nbn:se:umu:diva-18389DOI: 10.1016/0165-0270(95)00132-8PubMedID: 8699879OAI: diva2:158828
Available from: 2009-02-10 Created: 2009-02-04 Last updated: 2009-02-11Bibliographically approved
In thesis
1. Biomechanical methods and error analysis related to chronic musculoskeletal pain
Open this publication in new window or tab >>Biomechanical methods and error analysis related to chronic musculoskeletal pain
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background Spinal pain is one of humanity’s most frequent complaints with high costs for the individual and society, and is commonly related to spinal disorders. There are many origins behind these disorders e.g., trauma, disc hernia or of other organic origins. However, for many of the disorders, the origin is not known. Thus, more knowledge is needed about how pain affects the neck and neural function in pain affected regions. The purpose of this dissertation was to improve the medical examination of patients suffering from chronic whiplash-associated disorders or other pain related neck-disorders.

Methods A new assessment tool for objective movement analysis was developed. In addition, basic aspects of proprioceptive information transmission, which can be of relevance for muscular tension and pain, are investigated by studying the coding of populations of different types of sensory afferents by using a new spike sorting method. Both experiments in animal models and humans were studied to accomplish the goals of this dissertation. Four cats where were studied in acute animal experiments. Mixed ensembles of afferents were recorded from L7-S1 dorsal root filaments when mechanical stimulating the innervated muscle. A real-time spike sorting method was developed to sort units in a multi-unit recording. The quantification of population coding was performed using a method based on principal component analysis. In the human studies, 3D neck movement data were collected from 59 subjects with whiplash-associated disorders (WAD) and 56 control subjects. Neck movement patterns were identified by processing movement data into parameters describing the rotation of the head for each subject. Classification of neck movement patterns was performed using a neural network using processed collected data as input. Finally, the effect of marker position error on the estimated rotation of the head was evaluated by computer simulations.

Results Animal experiments showed that mixed ensembles of different types of afferents discriminated better between different muscle stimuli than ensembles of single types of these afferents. All kinds of ensembles showed an increase in discriminative ability with increased ensemble size. It is hypothesized that the main reason for the greater discriminative ability might be the variation in sensitivity tuning among the individual afferents of the mixed ensemble will be larger than that for ensembles of only one type of afferent. In the human studies, the neural networks had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88 when discriminating between control and WAD subjects. Also, a systematic error along the radial axis of the rigid body added to a single marker had no affect on the estimated rotation of the head.

Conclusion The developed spike sorting method, using neural networks, was suitable for sorting a multiunit recording into single units when performing neurophysiological experiments. Also, it was shown that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD or other pain related neck-disorders.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2009. 110 p.
Umeå University medical dissertations, ISSN 0346-6612 ; 1240
cervical spine, ensemble theory, error analysis, helical axis, kinematics, movement analysis, neural coding, pattern recognition, spike sorting, whiplash
National Category
Biomedical Laboratory Science/Technology
urn:nbn:se:umu:diva-18470 (URN)978-91-7264-717-6 (ISBN)
Institutionen för strålningsvetenskaper, 90185, Umeå
Public defence
2009-02-27, Sal 260, Röntgens föreläsningssal, by 3A, Norrlands universitetssjukhus, Umeå, 13:00 (Swedish)
Available from: 2009-02-10 Created: 2009-02-10 Last updated: 2015-11-30Bibliographically approved

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