Journal of Medical Signals and Sensors
Volume:15 Issue: 3, Mar 2025

Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject‑dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data.

In this article, a GSP‑based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert‑transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials.

The performance of the proposed approach was evaluated on brain–computer interface competition III (IVa) dataset.

Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state‑of‑the‑art methods.

Aging of societies in recent and upcoming years has made musculoskeletal disorders a significant challenge for healthcare system. Knee osteoarthritis (KOA) is a progressive musculoskeletal disorder that is typically diagnosed using radiographs. Considering the drawbacks of X‑ray imaging, such as exposure to ionizing radiation, the need for a noninvasive, low‑cost alternative method for diagnosing KOA is essential. The purpose of this study was to evaluate the ability of a wearable device to differentiate between healthy individuals and those with severe osteoarthritis (grade 4).

The wearable device consisted of two inertial measurement unit (IMU) sensors, one on the lower leg and one on the thigh. One of the sensors is used as a dynamic coordinate system to improve the accuracy of the measurements. In this study, to discriminate between 1433 labeled IMU signals collected from 15 healthy individuals and 15 people with severe KOA aged over 45, new features were extracted and defined in dynamic coordinates. These features were employed in four different classifiers: (1) naive Bayes, (2) K‑nearest neighbors (KNNs), (3) support vector machine, and (4) random forest. Each classifier was evaluated using the 10‑fold cross‑validation method (K = 10). The data were applied to these models, and based on their outputs, four performance metrics – accuracy, precision, sensitivity, and specificity – were calculated to assess the classification of these two groups using the mentioned software.

The evaluation of the selected classifiers involved calculating the four specified metrics and their average and variance values. The highest accuracy was achieved by KNN, with an accuracy of 93.71 ± 1.1 and a precision of 93 ± 1.31.

The novel features based on the dynamic coordinate system, along with the success of the proposed KNN model, demonstrate the effectiveness of the proposed algorithm in diagnosing between signals received from healthy individuals and patients. The proposed algorithm outperforms existing methods in similar articles in sensitivity showing an improvement of 4% and at least. The main objective of this study is to investigate the feasibility of using a wearable device as an auxiliary tool in the diagnosis of arthritis. The reported results in this study are related to two groups of individuals with severe arthritis (grade 4), and there is a possibility of weaker results with the current method.

Acrylic bone cement, which is used to fix implants in the knee and hip, is prone to contamination with various types of infections. Adding small amounts of different antibiotics to the cement can help prevent and treat infections. Rifampin antibiotic has been added to bone cement to create an appropriate antimicrobial response in the treatment of resistant coagulase‑negative staphylococci (CoNS) biofilms, but there are some challenges such as reducing mechanical properties and prolonging the setting time of the cement. Loading the antibiotic in the nanoparticle could eliminate these challenges.

In this study, rifampin‑loaded mesoporous silica nanoparticles (MSNs) were added to bone cement, and the polymerization components, mechanical properties, drug release, antibacterial activity, and cellular response were investigated and compared with commercial pure cement and the cement containing free rifampin.

Loading rifampin into MSN improved compressive strength by 57.52%. Cement containing rifampin loaded into MSN showed remarkable success in antibacterial activity. The growth inhibition zone created by it in the culture medium of Staphylococcus aureus and CoNS was 15.44% and 11.8% greater, respectively, than in the cement containing free rifampin. In other words, according to the results of spectrophotometric analysis of cement samples over 5 weeks, MSNs caused a 33.2 ± 0.21‑fold increase in rifampin washout from the cement. Cellular examination of the cement containing rifampin loaded into MSN compared to commercial pure cement showed an acceptable level of cell viability.

Rifampin loading in MSN limited the reduction of cement strength. It also improved the drug release pattern and prevented antibiotic resistance.