prikaz prve stranice dokumenta Classification and clustering of low back pain patients by analyzing surface myoelectric signals
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doctoral thesis
Classification and clustering of low back pain patients by analyzing surface myoelectric signals
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:794897
Srhoj-Egekher, Vedran
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronic Systems and Information Processing

Institutional repository: FER Repository

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Srhoj Egekher, V. (2022). Classification and clustering of low back pain patients by analyzing surface myoelectric signals (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:794897

Srhoj Egekher, Vedran. "Classification and clustering of low back pain patients by analyzing surface myoelectric signals." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:794897

Srhoj Egekher, Vedran. "Classification and clustering of low back pain patients by analyzing surface myoelectric signals." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:794897

Srhoj Egekher, V. (2022). 'Classification and clustering of low back pain patients by analyzing surface myoelectric signals', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 07 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:794897

Srhoj Egekher V. Classification and clustering of low back pain patients by analyzing surface myoelectric signals [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 November 07] Available at: https://urn.nsk.hr/urn:nbn:hr:168:794897

V. Srhoj Egekher, "Classification and clustering of low back pain patients by analyzing surface myoelectric signals", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:794897

Metadata
TitleClassification and clustering of low back pain patients by analyzing surface myoelectric signals
Title (croatian)Klasifikacija i grupiranje pacijenata s križoboljom analizom površinskih mioelektričnih signala
AuthorVedran Srhoj-Egekher
MentorMario Cifrek (mentor)
MentorStanislav Peharec (komentor)
Committee memberTomislav Pribanić (predsjednik povjerenstva)
Committee memberIgor Lacković (član povjerenstva)
Committee memberAlan Jović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Electronic Systems and Information Processing)
Zagreb
Defense date and country2022-12-22, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Universal decimal classification
(UDC)		621.3 - Electrical engineering
Abstract
Low back pain (LBP) is a global health problem phenomenon. Most patients are categorized as non-specific, thus requiring an individualized approach which still poses a major challenge. Myoelectric recordings from two pairs of lumbar muscle sites were collected during an isometric trunk extension exercise. Ninety-one subjects were included in the study; 29 patients with non-specific chronic LBP (CLBP), 25 patients with radiculopathy (RLBP), and 37 control healthy subjects (HS). Six best-performing time-domain raw features were employed to model contextual secondary feature groups. Neuromuscular LBP characteristics were described with coordination, co-activation, trends, and frequency-based fatigue measures. The initial large secondary feature set was significantly reduced by employing neighborhood component analysis (NCA), alongside preserving features in the original interpretable domain. Support vector machines (SVM) and k-nearest neighbor (kNN) consistently resulted in high classification accuracy, among the 23 classifiers exploited. Based on thirteen NCA components, from CLBP vs. HS model, CLBP subgrouping was performed by exploiting Hierarchical clustering analysis (HCA), resulting in seven CLBP subgroups. The formal procedure for assigning new CLBP patients, to one of the subgroups, was enabled by introducing a new distance metric CLBP Pattern Distance (CPD). As a final step, for each CLBP subgroup, a corresponding medical interpretation was proposed, thus enabling a direct connection between mathematical procedure and the deeper inference from the medical perspective to account for the complex multifactorial interaction among different neuromotor control, biomechanical, or even psychological aspects.
Abstract (croatian)
Križobolja (LBP) je prepoznata kao globalni zdravstveni problem. Većina pacijenata se kategorizira kao nespecifična, stoga je nužan individualizirani pristup koji još uvijek predstavlja veliki izazov. Elektromiografske snimke, s dva para pozicija lumbalnih mišića, prikupljene su tijekom izometrijske vježbe ekstenzije trupa. U istraživanje je bio uključen devedeset i jedan ispitanik; 29 bolesnika s nespecifičnim kroničnim LBP-om (CLBP), 25 bolesnika s radikulopatijom (RLBP) te 37 kontrolnih zdravih ispitanika (HS). Analizom dobivene najučinkovitije jednostavne značajke iz vremenske domene, njih šest, upotrijebljene su za modeliranje grupa kontekstualnih sekundarnih značajki. Neuromuskularne karakteristike LBP-a su opisane kroz grupe značajki koordinacije, koaktivacije, trendova i mjera umora temeljenih na frekvencijskoj analizi. Inicijalno veliki skup sekundarnih značajki je značajno reduciran tehnikom analize komponenti metodom susjedstva (NCA), uz očuvanje značajki u izvornoj interpretabilnoj domeni. Metoda potpornih vektora (SVM) i klasifikatori temeljeni na k-najbližem susjedstvu (kNN) konzistentno su rezultirali visokim klasifikacijskim točnostima, iz skupa 23 upotrijebljena klasifikatora. Koristeći trinaest NCA komponenti, dobivenih iz modela diferencijacije CLBP u odnosu na HS, provedeno je podgrupiranje CLBP pacijenata koristeći metodu grupiranja hijerarhijskom analizom, što je rezultiralo s ukupno sedam CLBP podskupina. Formalni postupak za dodjelu novih pacijenata s CLBP-om u jednu od sedam podskupina, osiguran je uvođenjem nove metrike udaljenosti, CLBP Pattern Distance (CPD). Kao posljednji korak, za svaku podskupinu CLBP-a predloženo je odgovarajuće medicinsko tumačenje, čime je omogućena izravna veza između matematičkog postupka i dubljeg zaključivanja iz medicinske perspektive, a kako bi se objasnila složena multifaktorijalna interakcija između različitih aspekata neuromotoričkih kontrole, biomehaničkih ili čak psiholoških aspekata.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:794897
Promotion2023
Study programmeTitle: Doctoral study programme "Electrical Engineering and Computing"
Study programme type: university
Study level: postgraduate
Academic / professional title: doktor/doktorica znanosti, po-dručje tehničkih znanosti (doktor/doktorica znanosti, po-dručje tehničkih znanosti)
Type of resourceText
Extentv, x, 135 str. : graf. prikazi ; 31 cm + CD-ROM
File originBorn digital
Access conditionsOpen access
Embargo expiration date: 2023-07-01
Terms of useLicence In copyright icon
Created on2023-03-27 09:13:03