Parametric Power Spectral Density Estimation-Based Breakthrough Detection for Orthopedic Bone Drilling with Acoustic Emission Signal Analysis

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dc.authoridTORUN, YUNIS/0000-0002-6187-0451
dc.contributor.authorTorun, Yunis
dc.contributor.authorPazarci, Ozhan
dc.date.accessioned2024-10-26T18:04:12Z
dc.date.available2024-10-26T18:04:12Z
dc.date.issued2020
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractManual bone drilling in orthopedic surgical operations may cause injury to patient tissues if the drill bit continues to progress after exiting the bone. In this study, a new bone breakthrough detection algorithm based on acoustic emission (AE) signal analysis has been developed to minimize temporary and permanent injuries that can be caused by surgeon-controlled surgical drills. Three parametric estimation methods, Burg, Yule-Walker and Modified Covariance were used to estimate Power Spectral Density (PSD) of the AE signal during the drilling operation. Four frequency features, Mean Frequency, Median Frequency, Mean-Median and Power Bandwidth were calculated for each PSD estimate. An artificial neural network-based breakthrough detection classification was constructed from the extracted features. The highest breakthrough detection performance was obtained with the features extracted by the Burg method with an accuracy rate of 90.95 +/- 0.97% in the training phase and 92.37 +/- 1.09% in the test phase. In the detection of Not-Breakthrough situations, the highest accuracy was obtained with features extracted with the Covariance method as 99.04 +/- 0.03% in the training phase and 99.05 +/- 0.08% in the testing phase. This new approach which could be integrated into conventional drills with minimum configuration changes and without any major cost has the potential to increase the performance and safety of bone drilling procedures.
dc.identifier.doi10.1007/s40857-020-00182-6
dc.identifier.endpage231
dc.identifier.issn0814-6039
dc.identifier.issn1839-2571
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85081907794
dc.identifier.scopusqualityQ2
dc.identifier.startpage221
dc.identifier.urihttps://doi.org/10.1007/s40857-020-00182-6
dc.identifier.urihttps://hdl.handle.net/20.500.12418/28802
dc.identifier.volume48
dc.identifier.wosWOS:000563939500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Singapore Pte Ltd
dc.relation.ispartofAcoustics Australia
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectOrthopedic drill
dc.subjectBreakthrough detection
dc.subjectPower spectral density estimation
dc.subjectArtificial neural network
dc.titleParametric Power Spectral Density Estimation-Based Breakthrough Detection for Orthopedic Bone Drilling with Acoustic Emission Signal Analysis
dc.typeArticle

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