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dc.contributor.advisorÇetin, Ahmet Enis
dc.contributor.authorAhi, Mustafa Arda
dc.date.accessioned2017-07-05T12:51:12Z
dc.date.available2017-07-05T12:51:12Z
dc.date.copyright2017-05
dc.date.issued2017-06
dc.date.submitted2017-06-12
dc.identifier.urihttp://hdl.handle.net/11693/33357
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 42-45).en_US
dc.description.abstractThis thesis proposes a spinal health assessment system based on acoustic biosignals. The aim of this study is to offer an alternative to the conventional spinal health assessment techniques such as MR, CT or x-ray scans. As conventional methods are time-consuming, expensive and harmful (radiation risk caused by medical scanning techniques), a cheap, fast and harmless method is proposed. It is observed that individuals with spinal health problems have unusual sounds. Using automatic speech recognition (ASR) algorithms, a diagnosis algorithm was developed for classifying joint sounds collected from the vertebrae of human subjects. First, feature parameters are extracted from spinal sounds. One of the most popular feature parameters used in speech recognition are Mel Frequency Cepstrum Coefficients (MFCC). MFCC parameters are classified using Artificial Neural Networks (ANN). In addition, the scattering transform cepstral coeffi- cients (STCC) algorithm is implemented as an alternative to the mel filterbank in MFCC. The correlation between the medical history of the subjects and the \click" sound in the collected sound data is the basis of the classification algorithm. In the light of collected data, it is observed that \click" sounds are detected in the individuals who have suffered low back pain (slipped disk) but not in healthy individuals. The identification of the \click" sound is carried out by using MFCC/STCC and ANN. The system has 92.2% success rate of detecting \click" sounds when MFCC based algorithm is used. The success rate is 83.5% when STCC feature extraction scheme is used.en_US
dc.description.statementofresponsibilityby Mustafa Arda Ahi.en_US
dc.format.extentxi, 50 leaves : charts (some color) ; 29 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSpinal soundsen_US
dc.subjectSound analysisen_US
dc.subjectSound and signal processingen_US
dc.subjectWearable medical systemsen_US
dc.titleAnalysis of spine sounds for spinal health assessmenten_US
dc.title.alternativeOmurga seslerinin omurga sağlığı değerlendirmesi amacıyla analizien_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB155747


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