Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to regulate seizures, or alerting patients of potential episodes. Next-generation seizure detection systems heavily rely on high-accuracy machine learning-based classifiers to detect the seizure onset. Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal. We empirically demonstrate, for the first time, that converting raw EEG signals to appropriate embeddings can significantly boost the performance of seizure detection algorithms. Importantly, we show that embedding features, which converts the raw EEG into an alternative representation, is beneficial for various machine learning models such as Logistic Regression, Multi-Layer Perceptron, Support Vector Machines, and Gradient Boosted Trees. The experiments were conducted on the CHB-MIT scalp EEG dataset. With the proposed EEG feature embeddings, we achieve significant improvements in sensitivity, specificity, and AUC score across multiple models. By employing this approach alongside an SVM classifier, we were able to attain state-of-the-art classification performance with a sensitivity of 100% and specificity of 99%, setting a new benchmark in the field

Your Out-of-Distribution Detection Method is Not Robust!

Out-of-distribution (OOD) detection has recently gained substantial attention due to the importance of identifying out-of-domain samples in reliability and safety. Although OOD detection methods have advanced by a great deal, they are still susceptible to adversarial examples, which is a violation of their purpose. To mitigate this issue, several defenses have recently been proposed. Nevertheless, these efforts remained ineffective, as their evaluations are based on either small perturbation sizes, or weak attacks. In this work, we re-examine these defenses against an end-to-end PGD attack on in/out data with larger perturbation sizes, e.g. up to commonly used $\epsilon=8/255$ for the CIFAR-10 dataset. Surprisingly, almost all of these defenses perform worse than a random detection under the adversarial setting. Next, we aim to provide a robust OOD detection method. In an ideal defense, the training should expose the model to almost all possible adversarial perturbations, which can be achieved through adversarial training. That is, such training perturbations should based on both in- and out-of-distribution samples. Therefore, unlike OOD detection in the standard setting, access to OOD, as well as in-distribution, samples sounds necessary in the adversarial training setup. These tips lead us to adopt generative OOD detection methods, such as OpenGAN, as a baseline. We subsequently propose the Adversarially Trained Discriminator (ATD), which utilizes a pre-trained robust model to extract robust features, and a generator model to create OOD samples. Using ATD with CIFAR-10 and CIFAR-100 as the in-distribution data, we could significantly outperform all previous methods in the robust AUROC while maintaining high standard AUROC and classification accuracy. The code repository is available at https://github.com/rohban-lab/ATD .Comment: Accepted to NeurIPS 202

The Effectiveness of Intravenous lidocaine in Burn Pain Relief: A Randomized Double-Blind Controlled Trial

Objectives: Poor pain control in burn patients as a great public health problem disrupts the healing and rehabilitation process and results in several adverse outcomes. The aim of this study was to investigate the efficacy and safety of intravenous lidocaine in reducing the pain of burn injuries. Materials and Methods: From August 2014 to March 2015, 66 eligible burn patients participated in the study and were randomly divided into two groups of lidocaine (L) and placebo (P). In group L, lidocaine 2% was injected at a bolus dose of 1.5 mg/kg followed by infusion at the dosage of 1.5 mg/kg/h, and in group P, saline was administrated. Pain severity was measured during 24 hours at baseline and 1, 2, 4, 8, 12, 16, 20, 24 hours after intervention based on Numerical Rating Scale (NRS-11). Morphine consumption, Ramsay score, and side effects were also documented. Results: Finally the data from 60 patients were analyzed. Comparing baseline with 24 hours after intervention, NRS-11 scores decreased from 7.12±1.42 to 3.33±0.76 (P<0.001) in group P and from 6.45±1.02 to 2.50±0.72 (P<0.001) in group L. Moreover, the mean of NRS scores during 24 hours in the lidocaine group was significantly lower compared to the placebo group, 3.93±0.72 vs 4.73 ±1.14, (P=0.03). The mean amounts of morphine consumption in group L were significantly lower compared to group P, 14.41 ± 4.86 vs 21.07±6.86, (P=0.001). The mean of Ramsay score in group L was significantly lower compared to group P, 1.38±0.59 vs 1.45±0.6, (P=0.014). Conclusions: This study revealed that intravenous lidocaine was an effective and safe drug for pain reduction in burn patients

Global injury morbidity and mortality from 1990 to 2017 : results from the Global Burden of Disease Study 2017

Correction:Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.Peer reviewe

Estimating global injuries morbidity and mortality : methods and data used in the Global Burden of Disease 2017 study

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.Peer reviewe

Development of pulsed fibre lasers and supercontinuum light source based on nonlinear effect / Arman Zarei

This thesis aims to investigate and demonstrate the generation of different types of pulse lasers and supercontinuum (SC) light based on nonlinear effects. At first, Brillouin fiber laser (BFL) is demonstrated using a long piece of Single mode fiber (SMF), Erbium-doped fiber (EDF) and highly nonlinear fiber (HNLF) as the gain medium. With 10 km long SMF, the BFL exhibits temporal characteristics where the pulse width and repetition frequency of the laser are obtained at 440 μs and 2 kHz, respectively. The Brillouin Erbium fiber laser (BEFL) also shows a self-pulsing characteristic with repetition rates of 66.7 kHz while mode-locked BFL is obtained by replacing the SMF with a 100m-long HNLF. Several passively mode-locked and Q-switched fiber lasers are then demonstrated based on nonlinear effects inside the ring laser cavity. For instance, a nanosecond optical pulse operating in fundamental mode is successfully generated in the EDF laser (EDFL) cavity by incorporating a 100 m long HNLF based on nonlinear polarization rotation (NPR) technique. The laser operates at 1567.2 nm and produced a pulse train with a repetition rate of 1.56 MHz, pulse width of 297 ns and the maximum pulse energy of 1.4 nJ. SC generation is then demonstrated by launching Q-switched mode-locking (QML), continuous-wave mode-locking (CWML) and dark pulse trains into various nonlinear fibers. With the amplified QML laser, the SC ranging from 1350 nm and 1900 nm has been successfully generated in 100 m long HNLF. With the amplified dark pulse, SC ranging from 1200 nm to 1810 nm, 1200 nm to 1920 nm and 1480 nm to 1740 nm are produced with the use of 50 m long photonic Crystal fiber (PCF), 100 m long HNLF and 20 km long PCF, respectively. Pulsed lasers have many applications in micromachining while super-continuum light sources are very useful for spectroscopy, frequency metrology, device characterization and medical science

Pain experience after oral mucosal biopsy: A quasi-experimental study

consequence of the oral mucosal biopsy. The aim of this study was to examine the incidence and severity of postoperative pain following the biopsy of oral mucosal lesions in patients attending in an oral medicine department of Kerman Dental School. METHODS: Visual analogue scale (VAS) was used to assess post-operative pain in 60 patients. Seven days after the biopsy of oral mucosa, patients were asked about overall pain experiences and analgesic usage over 3 days following the biopsy. RESULTS: Forty percent of patients reported moderate pain in the day of the biopsy and 58% of patients experienced no pain in the third day after the biopsy. Thirty percent of patients used analgesic in the day of the biopsy and there was not any relationship between the average level of patient's pain and the location of the lesion removal, type of biopsy, type of coverage, maximum diameter and type of the lesions (P > 0.05). CONCLUSIONS: Pain after biopsy from oral mucosal lesion is mild to moderate. KEY WORDS: Biopsy, Pain, Oral Mucos