Journal of Medical Signals & Sensors最新文献

Background: 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.

Methods: 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.

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

Conclusions: 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.

Background: 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.

Methods: 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.

Results: 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.

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

Background: 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).

Methods: 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.

Results: 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.

Conclusion: 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.

Background: Multiple sclerosis (MS) is one of the most common reasons of neurological disabilities in young adults. The disease occurs when the immune system attacks the central nervous system and destroys the myelin of nervous cells. This results in appearing several lesions in the magnetic resonance (MR) images of patients. Accurate determination of the amount and the place of lesions can help physicians to determine the severity and progress of the disease.

Method: Due to the importance of this issue, this challenge has been dedicated to the segmentation and localization of lesions in MR images of patients with MS. The goal was to segment and localize the lesions in the flair MR images of patients as close as possible to the ground truth masks.

Results: Several teams sent us their results for the segmentation and localization of lesions in MR images. Most of the teams preferred to use deep learning methods. The methods varied from a simple U-net structure to more complicated networks.

Conclusion: The results show that deep learning methods can be useful for segmentation and localization of lesions in MR images. In this study, we briefly described the dataset and the methods of teams attending the competition.

Background: Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance-pH (MII-pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection.

Method: In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data.

Result: A variety of signal-analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations.

Discussion: This article outlines the datasets provided to participants and offers an overview of the competition results.

Background: Treatment of locally advanced rectal cancer (LARC) involves neoadjuvant chemoradiotherapy (nCRT), followed by total mesorectal excision. Examining the response to treatment is one of the most important factors in the follow-up of patients; therefore, in this study, radiomics patterns derived from pretreatment computed tomography images in rectal cancer and its relationship with treatment response measurement criteria have been investigated.

Methods: Fifty patients with rectal adenocarcinoma who were candidates for nCRT and surgery were included. The information obtained from the tumor surgical pathology report, including pathological T and N, the degree of tumor differentiation, lymphovascular invasion, and perineural invasion along with radiomics characteristics to each patient, was assessed. Modeling with disturbed forest model was used for radiomics data. For other variables, Shapiro-Wilk, Chi-Square, and Pearson Chi-square tests were used.

Results: The participants of this study were 50 patients (23 males [46%] and 27 females [54%]). There was no significant difference in the rate of response to neoadjuvant treatment in between age and gender groups. According to the modeling based on combined clinical and radiomics data together, area under the curves for the nonresponders and complete respond group (responder group) was 0.97 and 0.99, respectively.

Conclusion: Random forests modeling based on combined radiomics and clinical characteristics of the pretreatment tumor images has the ability to predict the response or non-response to neoadjuvant treatment in LARC to an acceptable extent.

Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer.

Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC).

Results: On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%.

Conclusion: Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs.

Advances in knowledge: Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.

Proton therapy is a cancer treatment method that uses high-energy proton beams to target and destroy cancer cells. In recent years, the use of proton therapy in cancer treatment has increased due to its advantages over traditional radiation methods, such as higher accuracy and reduced damage to healthy tissues. For accurate planning and delivery of proton therapy, advanced software tools are needed to model and simulate the interaction between the proton beam and the patient's body. One of these tools is the Monte Carlo simulation software called Geant4, which provides accurate modeling of physical processes during radiation therapy. The purpose of this study is to investigate the effectiveness of the Geant4 toolbox in proton therapy in the conducted research. This review article searched for published articles between 2002 and 2023 in reputable international databases including Scopus, PubMed, Scholar, Google Web of Science, and ScienceDirect. Geant4 simulations are reliable and accurate and can be used to optimize and evaluate the performance of proton therapy systems. Obtaining some data from experiments carried out in the real world is very effective. This makes it easy to know how close the values obtained from simulations are to the behavior of ions in reality.

Background: Different dose calculation methods vary in accuracy and speed. While most methods sacrifice precision for efficiency Monte Carlo (MC) simulation offers high accuracy but slower calculation. ISOgray treatment planning system (TPS) uses Clarkson, collapsed cone convolution (CCC), and fast Fourier transform (FFT) algorithms for dose distribution. This study's primary goal is to evaluate the dose calculation accuracy for ISOgray TPS algorithms in the presence of a wedge.

Methods: This study evaluates the dose calculation algorithms using the ISOgray TPS in the context of radiation therapy. The authors compare ISOgray TPS algorithms on an Elekta Compact LINAC through MC simulations. The study compares MC simulations for open and wedge fields with ISOgray algorithms by using gamma index analysis for validation.

Results: The percentage depth dose results for all open and wedge fields showed a more than 98% pass rate for points. However, there were differences in the dose profile gamma index results. Open fields passed the gamma index analysis in the in-plane direction, but not all points passed in the cross-plane direction. Wedge fields passed in the cross-plane direction, but not all in the in-plane direction, except for the Clarkson algorithms.

Conclusion: In all investigated algorithms, error increases in the penumbra areas, outside the field, and at cross-plane of open fields and in-plane direction of wedged fields. By increasing the wedge angle, the discrepancy between the TPS algorithms and MC simulations becomes more pronounced. This discrepancy is attributed to the increased presence of scattered photons and the variation in the delivered dose within the wedge field, consequently impacts the beam quality. While the CCC and FFT algorithms had better accuracy, the Clarkson algorithm, particularly at larger effective wedge angles, exhibited greater effectiveness than the two mentioned algorithms.

Background: Autism spectrum disorders are a type of developmental disorder that primarily disrupt social interactions and communications. Autism has no treatment, but early diagnosis of it is crucial to reduce these effects. The incidence of autism is represented in repetitive patterns of children's motion. When walking, these children tighten their muscles and cannot control and maintain their body position. Autism is not only a mental health disorder but also a movement disorder.

Method: This study aims to identify autistic children based on data recorded from their gait patterns using a Kinect sensor. The database used in this study comprises walking information, such as joint positions and angles between joints, of 50 autistic and 50 healthy children. Two groups of features were extracted from the Kinect data in this study. The first one was statistical features of joints' position and angles between joints. The second group was the features based on medical knowledge about autistic children's behaviors. Then, extracted features were evaluated through statistical tests, and optimal features were selected. Finally, these selected features were classified by naïve Bayes, support vector machine, k-nearest neighbors, and ensemble classifier.

Results: The highest classification accuracy for medical knowledge-based features was 87% with 86% sensitivity and 88% specificity using an ensemble classifier; for statistical features, 84% of accuracy was obtained with 86% sensitivity and 82% specificity using naïve Bayes.

Conclusion: The dimension of the resulted feature vector based on autistic children's medical knowledge was 16, with an accuracy of 87%, showing the superiority of these features compared to 42 statistical features.