Abstract:
Knee Osteoarthritis (KOA) is a disease that causes the knee joint cartilages to thin and the
surfaces of the joint to become rougher, resulting in bone-on-bone contact, inflammation, and the
formation of bone spurs, leading to pain, stiffness, so that the knee not able to move as the
expected. KOA can affect any age range however; it is more common in the growing age of the
population and women above 50 years. Osteoarthritis is the most common cause of years lived
with disability (YLDs) globally. These conditions have a profound impact on people’s ability to
live and function. KOA can be diagnosed using various imaging and signal-based techniques
such as X-ray, Computed Tomography (CT)-scan, magnetic resonance imaging (MRI), and
Vibroarthrographic signal.
This research aimed to detect and determine the stage of knee osteoarthritis using features
extracted by empirical wavelet transform from vibroarthrographic signal. The developed pipeline
involved designing an electronic stethoscope using SG Electret microphone and single-headed
stethoscope to optimize joint sound detection in knee osteoarthritis. Acoustic signals were
recorded from both healthy (with no symptoms) and knee osteoarthritic subjects with age limit
above 45 years with symptoms of pain, stiffness, crepitus, and functional limitation. The
recorded signals were preprocessed using a fourth-order low-pass filter to remove noise and
artifacts. Signal decomposition was performed using empirical wavelet transform (EWT) to
extract audio features. Various features were extracted from the EWT coefficients, including
kurtosis, skewness, and entropy. Long short term memory deep learning and different machine
learning models, including support vector machine, KNN, and ensemble classifiers, were trained
and tested using the extracted features. An ensemble machine learning model with an accuracy
of 90.00% was selected for knee osteoarthritis diagnosis. The findings of this thesis demonstrate
that the proposed ensemble machine learning method achieved a high-level performance in the
classification task, as evidenced by the precision of 96.34%, recall of 97.53%, and f1_score of
96.93%. The sensitivity and specificity metrics 97.53% and 92.86%, respectively, also indicated
the effectiveness of the method in correctly identifying positive and negative instances. The
proposed method delivered a considerable result for the classification of knee osteoarthritis as
stage 0(healthy) up to stage 4(severe). Vibroarthrographic signal-based diagnosis is preferable
because of its portability, ease of use, reliability, and inexpensiveness. Moreover, as a further
III
consideration for future endeavors, the diagnosis approach would be more powerful if wireless
sensors were used because signals could be collected at different loading styles using wireless
sensors.
