Nothing Special   »   [go: up one dir, main page]

Krishnan et al., 2000 - Google Patents

Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology

Krishnan et al., 2000

Document ID
5644734337003748853
Author
Krishnan S
Rangayyan R
Bell G
Frank C
Publication year
Publication venue
IEEE Transactions on Biomedical Engineering

External Links

Snippet

Vibroarthrographic (VAG) signals emitted by human knee joints are nonstationary and multicomponent in nature; time-frequency distributions (TFD's) provide powerful means to analyze such signals. The objective of this paper is to construct adaptive TFD's of VAG …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4528—Joints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings

Similar Documents

Publication Publication Date Title
Krishnan et al. Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology
Rangayyan et al. Parametric representation and screening of knee joint vibroarthrographic signals
Turkoglu et al. An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks
Ali et al. Detection of voice pathology using fractal dimension in a multiresolution analysis of normal and disordered speech signals
Übeylı et al. Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Aydin et al. Embolic Doppler ultrasound signal detection using discrete wavelet transform
Tavathia et al. Analysis of knee vibration signals using linear prediction
Avci et al. An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases
Legarreta et al. R-wave detection using continuous wavelet modulus maxima
Sharma et al. Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank
Gupta et al. Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method
Keeton et al. Application of wavelets in Doppler ultrasound
Koçer Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases
Güraksın et al. Classification of heart sounds based on the least squares support vector machine
Boashash et al. Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities
Krishnan et al. Auditory display of knee-joint vibration signals
Shieh et al. Synthesis of vibroarthrographic signals in knee osteoarthritis diagnosis training
Nalband et al. Analysis of knee joint vibration signals using ensemble empirical mode decomposition
Khosropanah et al. Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization
Gunawan Denoising images using wavelet transform
Bhuiyan et al. Advantages and limitations of using matrix pencil method for the modal analysis of medical percussion signals
Nougarou et al. Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain
Güler et al. Determination of Behcet disease with the application of FFT and AR methods
Ikawa Automated averaging of auditory evoked response waveforms using wavelet analysis
Quang-Huy et al. Shear wave imaging and classification using extended Kalman filter and decision tree algorithm