Adaptive cancellation of muscle contraction interference in vibroarthrographic signals

• Published in: IEEE transactions on biomedical engineering Vol. 41; no. 2; pp. 181 - 191
• Main Authors: Zhang, Y.T., Rangayyan, R.M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.1994; Institute of Electrical and Electronics Engineers
• Subjects: Adaptive filters; Adult; Algorithms; Arthrography - methods; Band pass filters; Biological and medical sciences; Cartilage, Articular - physiology; Filtering; Humans; Interference cancellation; Investigative techniques, diagnostic techniques (general aspects); Isotonic Contraction - physiology; Joints; Knee; Knee Joint - physiology; Medical sciences; Models, Biological; Muscles; Noninvasive treatment; Osteoarticular system. Muscles; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Reference Values; Signal detection; Signal Processing, Computer-Assisted; Skin; Vibration; Knee; Human; Filtering; Vibration; Noise reduction; Interference; Theoretical study; Adaptive filtering; Exploration; Experimental study; Muscle contraction; Adaptive method; Optimization; Articular cartilage; Signal processing; Vibroarthrography; Technique; Biomedical engineering
• ISSN: 0018-9294
• DOI: 10.1109/10.284929

Vibroarthrography (VAG) is an innovative, objective, noninvasive technique for obtaining diagnostic information concerning the articular cartilage of a joint. Knee VAG signals can be detected using a contact sensor over the skin surface of the knee joint during knee movement such as flexion and/or extension. These measured signals. However, contain significant interference caused by muscle contraction that is required for knee movement. Quality improvement of VAG signals is an important subject, and crucial in computer-aided diagnosis of cartilage pathology. While simple frequency domain high-pass (or band-pass) filtering could be used for minimizing muscle contraction interference (MCI), it could eliminate possible overlapping spectral components of the VAG signals. In this work, an adaptive MCI cancellation technique is presented as an alternative technique for filtering VAG signals. Methods of measuring the VAG and reference signals (MCI) are described, with details on MCI identification. Characterization, and step size optimization for the adaptive filter. The performance of the method is evaluated by simulated signals as well as signals obtained from human subjects under isotonic contraction.< >