Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status
| Autor: | Caitlin N. Teague, Paul Wolkoff, Mindy L. Millard-Stafford, Omer T. Inan, Geza F. Kogler, Maziyar Baran Pouyan, Sinan Hersek, Michael N. Sawka |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male medicine.medical_specialty Sound Spectrography Knee Joint Health Status medicine.medical_treatment 0206 medical engineering Biomedical Engineering Knee Injuries 02 engineering and technology 01 natural sciences Article Data mining algorithm Wearable Electronic Devices Young Adult Text mining Multiple time Data Mining Humans Medicine Range of Motion Articular Rehabilitation business.industry 010401 analytical chemistry Healthy subjects Acute knee injury Signal Processing Computer-Assisted 020601 biomedical engineering Graph 0104 chemical sciences Physical therapy Female business Knee injuries Algorithms Biomarkers |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 65:1291-1300 |
| ISSN: | 1558-2531 0018-9294 |
| Popis: | OBJECTIVE: To study knee acoustical emission patterns in subjects with acute knee injury immediately following injury and several months after surgery and rehabilitation. METHODS: We employed an unsupervised graph mining algorithm to visualize heterogeneity of the high-dimensional acoustical emission data, and to then derive a quantitative metric capturing this heterogeneity – the graph community factor (GCF). A total of 42 subjects participated in the studies. Measurements were taken once each from 33 healthy subjects with no known previous knee injury, and twice each from 9 subjects with unilateral knee injury: first, within seven days of the injury, and second, 4–6 months after surgery when the subjects were determined ready to start functional activities. Acoustical signals were processed to extract time and frequency domain features from multiple time windows of the recordings from both knees, and k-Nearest Neighbor graphs were then constructed based on these features. RESULTS: The GCF calculated from these graphs was found to be 18.5 ± 3.5 for healthy subjects, 24.8 ± 4.4 (p=0.01) for recently injured and 16.5 ± 4.7 (p=0.01) at 4–6 months recovery from surgery. CONCLUSION: The objective GCF scores changes were consistent with a medical professional’s subjective evaluations and subjective functional scores of knee recovery. SIGNIFICANCE: Unsupervised graph mining to extract GCF from knee acoustical emissions provides a novel objective and quantitative biomarker of knee injury and recovery that can be incorporated with a wearable joint health system for use outside of clinical settings, and austere / under resourced conditions, to aid treatment / therapy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|