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This study focuses on the diagnostic analysis of cartilage damage in the knee
joint based on acoustic signals generated by the joint. The research utilizes a combination
of advanced signal processing techniques, specifically empirical mode decomposition
(EEMD) and detrended fluctuation analysis (DFA), alongside convolutional neural net-
works (CNNs) for classification and detection tasks. Acoustic signals, often reflecting the
mechanical behavior of the joint during movement, serve as a non-invasive diagnostic tool
for assessing the cartilage condition. EEMD is applied to decompose the signals into in-
trinsic mode functions (IMFs), which are then analyzed using DFA to quantify the scaling
properties and detect irregularities indicative of cartilage damage. The separation of indi-
vidual frequency components allows for multi-scale analysis of the signals, with each of
the functions resulting from the analysis reflecting local variations in the amplitude and
frequency over time and allowing for effective removal of noise present in the signal. The
CNN model is trained on features extracted from these signals to accurately classify dif-
ferent stages of cartilage degeneration. The proposed method demonstrates the potential
for early detection of knee joint pathology, providing a valuable tool for preventive
healthcare and reducing the need for invasive diagnostic procedures. The results suggest
that the combination of EEMD-DFA for feature extraction and CNN for classification of-
fers a promising approach for the non-invasive assessment of cartilage damage.
