Contribution to differential electromyographic diagnostics of low back pain and radiculopathy

Ostojić, Saša

prikaz prve stranice dokumenta Contribution to differential electromyographic diagnostics of low back pain and radiculopathy

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doctoral thesis

Contribution to differential electromyographic diagnostics of low back pain and radiculopathy

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:448344

Ostojić, Saša

University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

APA 6th Edition

Ostojić, S. (2019). Contribution to differential electromyographic diagnostics of low back pain and radiculopathy (Doctoral thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:448344

MLA 8th Edition

Ostojić, Saša. "Contribution to differential electromyographic diagnostics of low back pain and radiculopathy." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:448344

Chicago 17th Edition

Harvard

Ostojić, S. (2019). 'Contribution to differential electromyographic diagnostics of low back pain and radiculopathy', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 01 April 2023, https://urn.nsk.hr/urn:nbn:hr:168:448344

Vancouver

Ostojić S. Contribution to differential electromyographic diagnostics of low back pain and radiculopathy [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2023 April 01] Available at: https://urn.nsk.hr/urn:nbn:hr:168:448344

IEEE

S. Ostojić, "Contribution to differential electromyographic diagnostics of low back pain and radiculopathy", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:448344

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:448344

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Metadata

Title	Contribution to differential electromyographic diagnostics of low back pain and radiculopathy
Title (croatian)	Prilog diferencijalnojelektromiografskoj dijagnostici križobolje i radikulopatije
Author	Saša Ostojić MBZ: -346762
Mentor	Mario Cifrek (mentor)
Mentor	Stanislav Peharec (komentor)
Committee member	Mario Cifrek (predsjednik povjerenstva)
Committee member	Stanislav Peharec (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing Zagreb
Defense date and country	2019, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing Data Processing
Universal decimal classification (UDC)	004 - Computer science and technology. Computing. Data processing
Abstract	Low back pain is one of the leading causes of absence from work. Diagnosing low back pain patients or mere differentiation from healthy subjects is therefore an interesting problem. One way to diagnose comes from properties of surface EMG signals to reflect complex changes occurring within skeletal muscles during fatiguing static contractions. This doctoral work introduces differentiation of low back pain patients with radiculopathy from chronic nonspecific low back pain patients and from healthy subjects based only on four surface EMG signals. The measurement protocol was simplified, and subject’s own upper body weight is used to induce muscle fatigue in low back muscles. Surface EMG signals were recorded during static contractions above m. erector spinae. The adaptive time-frequency method, Hilbert-Huang transform, was utilized to estimate power spectrum of the recorded surface EMG signals. As a descriptor of surface EMG spectral changes, the regression line of slope of the median frequency of the power spectrum was calculated and used as classification feature for the decision tree classification. Proposed measurement protocol, defined classification feature and classification model enable electromyographic differentiation of low back pain patients with radiculopathy and chronic nonspecific low back pain patients, where achieved overall accuracy of classification is up to 86.8 %.
Abstract (croatian)	Križobolja je jedan od vodećih razloga izostajanja s posla. Zato je dijagnosticiranje križobolje u pacijenata ili već i samo njihovo razlikovanje od zdravih osoba zanimljiv problem za proučavanje. Jedan od načina dijagnosticiranja proizlazi iz svojstva površinskih EMG signala da odražavaju složene promjene u skeletnim mišićima što nastaju tijekom umarajućih statičkih kontrakcija. Ovaj doktorski rad uvodi diferencijaciju pacijenata s križoboljom s radikulopatijom od pacijenata s kroničnom nespecifičnom križoboljom, te od zdravih ispitanika, temeljem samo četiri površinska EMG signala. Mjerni protokol je pojednostavljen te se težina gornjeg dijela tijela ispitanika koristi za izazivanje mišićnog umora u mišićima donjeg dijela leđa. Površinski EMG signali snimani su tijekom statičkih kontrakcija iznad m. erector spinae. Adaptivna vremensko-frekvencijska metoda, Hilbert-Huang transformacija, korištena je za procjenu spektra snage snimljenih površinskih EMG signala. Kao deskriptor promjene spektra površinskih EMG signala, izračunat je nagib regresijskog pravca frekvencije medijana spektra snage i korišten kao klasifikacijska značajka za klasifikaciju stablom odlučivanja. Predloženi mjerni protokol, definirana klasifikacijska značajka i klasifikacijski model omogućavaju elektromiografsku diferencijaciju pacijenata s križoboljom s radikulopatijom i pacijenata s kroničnom nespecifičnom križoboljom, pri čemu je ostvarena ukupna točnost klasifikacije do 86,8 %.
Keywords
Keywords (croatian)
Language	english
URN:NBN	urn:nbn:hr:168:448344
Study programme	Title: Computer Science Study programme type: university Study level: postgraduate Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resource	Text
Extent	str. 85; 30 cm
File origin	Born digital
Access conditions	Closed access
Terms of use
Repository	FER Repository
Created on	2019-03-26 08:28:23