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Pregled bibliografske jedinice broj: 991023

Differentiating patients with radiculopathy from chronic low back pain patients by single surface EMG parameter


Ostojić, Saša; Peharec, Stanislav; Srhoj-Egekher, Vedran; Cifrek, Mario
Differentiating patients with radiculopathy from chronic low back pain patients by single surface EMG parameter // Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 59 (2018), 3-4; 400-407 doi:10.1080/00051144.2018.1553669 (međunarodna recenzija, članak, znanstveni)


Naslov
Differentiating patients with radiculopathy from chronic low back pain patients by single surface EMG parameter

Autori
Ostojić, Saša ; Peharec, Stanislav ; Srhoj-Egekher, Vedran ; Cifrek, Mario

Izvornik
Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije (0005-1144) 59 (2018), 3-4; 400-407

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Biomedical signal processing ; classification ; electromyography ; Hilbert–Huang transform ; low back pain ; radiculopathy

Sažetak
The classification potential of surface electromyographic (EMG) parameters needs to be explored beyond classification of subjects onto low back pain subjects and control subjects. In this paper, a classification model based on surface EMG parameter is introduced to differentiate low back pain patients with radiculopathy from chronic low back pain patients and control subjects. A variant of the Roman chair was used to perform static contractions, where subject’s own upper body weight was used to induce muscle fatigue in low back muscles. Surface EMG signals were recorded over the paraspinal muscles at L1–L2 and L4–L5 interspace level. As a descriptor of spectral changes, the median frequency of the power spectrum was estimated by use of Hilbert–Huang transform. Student’s t–test detected that regression line slope of the median frequency is significantly different (p<0.05) only between low back pain patients with radiculopathy and other two groups. There was no significant difference between chronic low back pain patients and control subjects. The achieved overall accuracy of the implemented decision tree classification model was at best 86.8%. The results suggest possibility of differentiating low back pain patients to subgroups depending on clinical symptoms.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Kliničke medicinske znanosti



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike i računarstva, Zagreb,
Medicinski fakultet, Rijeka

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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