Analysis of temporomandibular joint sounds in orthodontic patients

Akan A., Ergin A., Yıldırım M., Oztas E.

COMPUTERS & ELECTRICAL ENGINEERING, cilt.32, sa.4, ss.312-321, 2006 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 4
  • Basım Tarihi: 2006
  • Doi Numarası: 10.1016/j.compeleceng.2005.11.002
  • Dergi Adı: COMPUTERS & ELECTRICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.312-321
  • Anahtar Kelimeler: temporomandibular joint sounds, time-frequency analysis, Gabor expansion, orthodontic treatment, CLASSIFICATION, TMD
  • İstanbul Üniversitesi Adresli: Evet

Özet

In this work, we investigate the progress of temporomandibular joint (TMJ) sounds during orthodontic treatment. The study of changes in TMJ sounds might help to determine whether there are relations between various types of sounds and the dental malocclusions. TMJ vibrations from patients with lateral cross-bite and Class II Division I malocclusions are recorded by means of accelerometers during jaw opening and closing cycles. Then signals are analyzed using the discrete evolutionary transform. Joint time-frequency moments calculated from the evolutionary spectrum are used as features for the classification of TMJ vibrations by a neural network. Signals are classified at different stages of treatment and the results are discussed. (c) 2006 Elsevier Ltd. All rights reserved.

In this work, we investigate the progress of temporomandibular joint (TMJ) sounds during orthodontic treatment. The study of changes in TMJ sounds might help to determine whether there are relations between various types of sounds and the dental malocclusions. TMJ vibrations from patients with lateral cross-bite and Class II Division 1 malocclusions are recorded by means of accelerometers during jaw opening and closing cycles. Then signals are analyzed using the discrete evolutionary transform. Joint time–frequency moments calculated from the evolutionary spectrum are used as features for the classification of TMJ vibrations by a neural network. Signals are classified at different stages of treatment and the results are discussed.