A novel automatic Knee Osteoarthritis detection method based on vibroarthrographic signals. (July 2021)

• Record Type: Journal Article
• Authors: Wang, Yuntang; Zheng, Tiantian; Song, Jiangling; Gao, Weidong
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Knee osteoarthritis -- Vibroarthrographic -- Automatic KOA detection -- Feature extraction -- Back propagation neural network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102796
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: It is a hard problem for doctors to evaluate the condition of KOA patients by visually detecting VAG signal. We develop a novel KOA auxiliary diagnostic method using VAG signals, where a new VAG feature extraction method is designed. Simulation results convey that the proposed method gives the detection accuracy of 98.2% with sensitivity of 97.9% and specificity of 98.5%. It shows that the proposed methodology may provide an effective non-invasive diagnostic tool for KOA disorders. Abstract: Knee Osteoarthritis (KOA) is a common and chronic degenerative joint disease. Comparing with the traditional examinations (e.g., X-ray, MRI), the vibroarthrographic (VAG) examination, which is a low-costly, atraumatic, and at-home way, may open up new alternatives to KOA detection in clinic. However, it is a hard problem for doctors to evaluate the condition of KOA patients by visually detecting VAG signal due to the very limited understanding of pathological information included in VAG signals. Originated from this, we focus on exploring a reliable KOA auxiliary diagnostic method using VAG signals. In this paper, a new feature extraction method is first proposed, where the kernel-radius-based feature (KR-F) and statistic-based feature (S-F) are extracted respectively in the transient phase space of VAG signal. Furthermore, two features are integrated in the feature-fusion level (KR-S-FF), and then fed into the back propagation neural network (BPNN) to complete the KOA detection automatically. Finally, the proposed automatic KOA detection method is verified in a clinical VAG dataset, which is collected from one hospital in Xi'an, China. Simulation results convey that the proposed method gives the high detection accuracy of 98.2 % with sensitivity of 97.9 % and specificity of 98.5 %, showing that it may provide an effective non-invasive tool for KOA disorders.