Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification

Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. The proposed method is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, the proposed method does not require additional preprocessing and extraction of features which is common in the existing methods. The proposed method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, the proposed method outperformed them in terms of sensitivity, specificity and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician

A low cortisol response to stress is associated with musculoskeletal pain combined with increased pain sensitivity in young adults: A longitudinal cohort study

Background: In this study, we investigated whether an abnormal hypothalamic-pituitary-adrenal (HPA) axis response to psychosocial stress at 18 years of age is associated with musculoskeletal (MS) pain alone and MS pain combined with increased pain sensitivity at 22 years of age. Methods: The study sample included 805 participants from the Western Australian Pregnancy Cohort (Raine) Study who participated in the Trier Social Stress Test (TSST) at age 18 years. Number of pain sites, pain duration, pain intensity and pain frequency were assessed at age 22 to measure severity of MS pain. Cold and pressure pain thresholds were determined at age 22. Group-based trajectory modeling was applied to establish cortisol response patterns based on the TSST. Logistic regression was used to study the association of TSST patterns with MS pain alone and MS pain combined with increased cold or pressure pain sensitivity, adjusted for relevant confounding factors. All analyses were stratified by sex. Results: The mean (standard deviation) age during the TSST was 18.3 (0.3) years, and during MS pain assessment it was 22.2 (0.6). Forty-five percent of the participants were female. Three cortisol response patterns were identified, with cluster 1 (34 % of females, 21 % of males) reflecting hyporesponse, cluster 2 (47 %, 54 %) reflecting intermediate response and cluster 3 (18 %, 24 %) reflecting hyperresponse of the HPA axis. MS pain was reported by 42 % of females and 33 % of males at age 22 years. Compared with females in cluster 2, females in cluster 1 had an increased likelihood of having any MS pain (odds ratio 2.3, 95 % confidence interval 1.0-5.0) and more severe MS pain (2.8, 1.1-6.8) if their cold pain threshold was above the median. In addition, females in cluster 1 had an increased likelihood (3.5, 1.3-9.7) of having more severe MS pain if their pressure pain threshold was below the median. No statistically significant associations were observed in males. Conclusions: This study suggests that a hyporesponsive HPA axis at age 18 years is associated with MS pain at 22 years in young females with increased pain sensitivity

Efficient LED-SAC Sparse Estimator Using Fast Sequential Adaptive Coordinate-Wise Optimization (LED-2SAC)

Solving the underdetermined system of linear equations is of great interest in signal processing application, particularly when the underlying signal to be estimated is sparse. Recently, a new sparsity encouraging penalty function is introduced as Linearized Exponentially Decaying penalty, LED, which results in the sparsest solution for an underdetermined system of equations subject to the minimization of the least squares loss function. A sequential solution is available for LED-based objective function, which is denoted by LED-SAC algorithm. This solution, which aims to sequentially solve the LED-based objective function, ignores the sparsity of the solution. In this paper, we present a new sparse solution. The new method benefits from the sparsity of the signal both in the optimization criterion (LED) and its solution path, denoted by Sparse SAC (2SAC). The new reconstruction method denoted by LED-2SAC (LED-Sparse SAC) is consequently more efficient and considerably fast compared to the LED-SAC algorithm, in terms of adaptability and convergence rate. In addition, the computational complexity of both LED-SAC and LED-2SAC is shown to be of order d2, which is better than the other batch solutions like LARS. LARS algorithm has complexity of order d3+nd2, where d is the dimension of the sparse signal and n is the number of observations

Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation

Data Availability Statement: We have made our dataset available to the public at https://github.com (file name: Mojtab2023/Classification-of-Proximal-Femoral-Bone-Using-Geometric-Features-and-Texture-Analysis-in-MR-Images-f (1 January 2022).Copyright © 2023 by the authors.. The aim of this study was to use geometric features and texture analysis to discriminate between healthy and unhealthy femurs and to identify the most influential features. We scanned proximal femoral bone (PFB) of 284 Iranian cases (21 to 83 years old) using different dual-energy X-ray absorptiometry (DEXA) scanners and magnetic resonance imaging (MRI) machines. Subjects were labeled as “healthy” (T-score > −0.9) and “unhealthy” based on the results of DEXA scans. Based on the geometry and texture of the PFB in MRI, 204 features were retrieved. We used support vector machine (SVM) with different kernels, decision tree, and logistic regression algorithms as classifiers and the Genetic algorithm (GA) to select the best set of features and to maximize accuracy. There were 185 participants classified as healthy and 99 as unhealthy. The SVM with radial basis function kernels had the best performance (89.08%) and the most influential features were geometrical ones. Even though our findings show the high performance of this model, further investigation with more subjects is suggested. To our knowledge, this is the first study that investigates qualitative classification of PFBs based on MRI with reference to DEXA scans using machine learning methods and the GA.This research received no external funding