umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Biomechanical methods and error analysis related to chronic musculoskeletal pain
Umeå University, Faculty of Medicine, Department of Radiation Sciences.ORCID iD: 0000-0003-3363-7414
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.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1240
Keyword [en]
cervical spine, ensemble theory, error analysis, helical axis, kinematics, movement analysis, neural coding, pattern recognition, spike sorting, whiplash
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:umu:diva-18470ISBN: 978-91-7264-717-6 (print)OAI: oai:DiVA.org:umu-18470DiVA: diva2:159773
Distributor:
Institutionen för strålningsvetenskaper, 90185, Umeå
Public defence
2009-02-27, Sal 260, Röntgens föreläsningssal, by 3A, Norrlands universitetssjukhus, Umeå, 13:00 (Swedish)
Opponent
Available from: 2009-02-10 Created: 2009-02-10 Last updated: 2015-11-30Bibliographically approved
List of papers
1. A neural network appoach to real-time spike discrimination during simultaneous recording from several multi-unit nerve filaments
Open this publication in new window or tab >>A neural network appoach to real-time spike discrimination during simultaneous recording from several multi-unit nerve filaments
Show others...
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.

Keyword
Spike discrimination, neural network, self organising map, real-time, multi-unit train, multiple electrode, nerve filament
Identifiers
urn:nbn:se:umu:diva-18389 (URN)10.1016/0165-0270(95)00132-8 (DOI)8699879 (PubMedID)
Available from: 2009-02-10 Created: 2009-02-04 Last updated: 2009-02-11Bibliographically approved
2. Ensamble coding of muscle stretches in afferent populations containing different types of muscle afferents
Open this publication in new window or tab >>Ensamble coding of muscle stretches in afferent populations containing different types of muscle afferents
Show others...
1996 (English)In: Brain Research, ISSN 0006-8993, Vol. 734, no 1-2, 157-166 p.Article in journal (Refereed) Published
Abstract [en]

Ensemble coding of simple mechanical stimuli (small sinusoidal stretches) was studied in simultaneously recorded mixed ensembles of primary- and secondary muscle spindle afferents (MSAs), and Golgi tendon organ (GTO) afferents recorded from L7-S1 dorsal root filaments. The experiments were made on 48 recorded afferents (29 primary MSAs, 6 secondary MSAs and 13 GTO afferents) in chloralose anaesthetised cats. For the analyses, we used a combination of principal component analysis and algorithms for quantification of stimulus discrimination. Mixed ensembles of primary- and secondary MSAs, and GTO afferents, discriminated significantly better between different muscle stretches than ensembles of only one or two types of these afferents. All kinds of ensembles showed a successive increase in discriminative ability with increased ensemble size, and this ability seemed to level at larger populations. However, the increase in discriminative ability was significantly greater for the mixed ensembles. It is hypothesised that the main reason for the greater discriminative ability achieved by mixed ensembles, might be that the variation in response profiles (sensitivity tuning) among the individual afferents of the mixed ensemble will be larger than that for ensembles of only one type of afferent. Finally, the results in the present study give experimental support to some of the teleological arguments in favour of the ensemble coding theory.

Keyword
Ensemble coding, stimulus discrimination, simultaneous recording, afferent single, afferent sensory, afferent muscle, mechanoreceptor
Identifiers
urn:nbn:se:umu:diva-18390 (URN)10.1016/0006-8993(96)00642-7 (DOI)8896821 (PubMedID)
Available from: 2009-02-10 Created: 2009-02-04 Last updated: 2009-02-11Bibliographically approved
3. Chronic whiplash associated disorders and neck movement measurements: an instantaneous helical axis approach.
Open this publication in new window or tab >>Chronic whiplash associated disorders and neck movement measurements: an instantaneous helical axis approach.
Show others...
2003 (English)In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 7, no 4, 274-282 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents an assessment tool for objective neck movement analysis of subjects suffering from chronic whiplash-associated disorders (WAD). Three-dimensional (3-D) motion data is collected by a commercially available motion analysis system. Head rotation, defined in this paper as the rotation angle around the instantaneous helical axis (IHA), is used for extracting a number of variables (e.g., angular velocity and range, symmetry of motion). Statistically significant differences were found between controls and subjects with chronic WAD in a number of variables.

National Category
Physiology Neurosciences
Identifiers
urn:nbn:se:umu:diva-22045 (URN)15000354 (PubMedID)
Available from: 2009-04-22 Created: 2009-04-22 Last updated: 2017-06-01
4. Classification of neck movement patterns related to whiplash-associated disorders using neural networks
Open this publication in new window or tab >>Classification of neck movement patterns related to whiplash-associated disorders using neural networks
Show others...
2003 (English)In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, Vol. 7, no 4, 412-418 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.

National Category
Neurosciences Physiology
Identifiers
urn:nbn:se:umu:diva-3066 (URN)15000367 (PubMedID)
Available from: 2008-03-31 Created: 2008-03-31 Last updated: 2017-06-01Bibliographically approved
5. The effect of anisotropic systematic errors in estimating helical angles.
Open this publication in new window or tab >>The effect of anisotropic systematic errors in estimating helical angles.
2008 (English)In: Comput Methods Biomech Biomed Engin, ISSN 1025-5842, Vol. 11, no 2, 205-213 p.Article in journal (Refereed) Published
Abstract [en]

A common question in movement studies is how the results should be interpreted with respect to systematic and random errors. In this study, simulations are made in order to see how a rigid body's orientation in space (i.e. helical angle between two orientations) is affected by (1) a systematic error added to a single marker (2) a combination of this systematic error and Gaussian white noise. The orientation was estimated after adding a systematic error to one marker within the rigid body. This procedure was repeated with Gaussian noise added to each marker.

In conclusion, results show that the systematic error's effect on estimated orientation depends on number of markers in the rigid body and also on which direction the systematic error is added. The systematic error has no effect if the error is added along the radial axis (i.e. the line connecting centre of mass and the affected marker).

Keyword
screw, helical, angle, systematic, accuracy, error
Identifiers
urn:nbn:se:umu:diva-9496 (URN)doi:10.1080/10255840701722498 (DOI)18297498 (PubMedID)
Available from: 2008-04-10 Created: 2008-04-10 Last updated: 2015-11-30Bibliographically approved

Open Access in DiVA

fulltext(2434 kB)1293 downloads
File information
File name FULLTEXT01.pdfFile size 2434 kBChecksum SHA-512
827aba0b0fbc8533f75bbdcbcea49634860feb9dfd4c9cfc2f3fd26b4aa86df3058d27817933536542b37bec77f815b8fa5b78ace8510f989a4bb168ada9fe2a
Type fulltextMimetype application/pdf

Authority records BETA

Öhberg, Fredrik

Search in DiVA

By author/editor
Öhberg, Fredrik
By organisation
Department of Radiation Sciences
Biomedical Laboratory Science/Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 1293 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1263 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf