Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios

A collimated, ionized bipolar structure and a high density torus in the young planetary nebula IRAS 17347-3139

We present observations of continuum (lambda = 0.7, 1.3, 3.6 and 18 cm) and OH maser (lambda = 18 cm) emission toward the young planetary nebula IRAS 17347-3139, which is one of the three planetary nebulae that are known to harbor water maser emission. From the continuum observations we show that the ionized shell of IRAS 17347-3139 consists of two main structures: one extended (size ~1". 5) with bipolar morphology along PA=-30 degrees, elongated in the same direction as the lobes observed in the near-infrared images, and a central compact structure (size ~0". 25) elongated in the direction perpendicular to the bipolar axis, coinciding with the equatorial dark lane observed in the near-infrared images. Our image at 1.3 cm suggests the presence of dense walls in the ionized bipolar lobes. We estimate for the central compact structure a value of the electron density at least ~5 times higher than in the lobes. A high resolution image of this structure at 0.7 cm shows two peaks separated by about 0". 13 (corresponding to 100-780 AU, using a distance range of 0.8-6 kpc). This emission is interpreted as originating in an ionized equatorial torus-like structure, from whose edges the water maser emission might be arising. We have detected weak OH 1612 MHz maser emission at VLSR ~ -70 km/s associated with IRAS 17347-3139. We derive a 3 sigma upper limit of < 35% for the percentage of circularly polarized emission. Within our primary beam, we detected additional OH 1612 MHz maser emission in the LSR velocity ranges -5 to -24 and -90 to -123 km/s, associated with the sources 2MASS J17380406-3138387 and OH 356.65-0.15, respectively.Comment: 26 pages, 8 figures. Accepted for publication in Ap

A Raspberry Pi-based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroen-cephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16 s - 64 s epochs for TBI vs control conditions. This work can enable development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.Comment: 12 pages, 6 figure

EEG Slow Waves in Traumatic Brain Injury: Convergent Findings in Mouse and Man

OBJECTIVE: Evidence from previous studies suggests that greater sleep pressure, in the form of EEG-based slow waves, accumulates in specific brain regions that are more active during prior waking experience. We sought to quantify the number and coherence of EEG slow waves in subjects with mild traumatic brain injury (mTBI). METHODS: We developed a method to automatically detect individual slow waves in each EEG channel, and validated this method using simulated EEG data. We then used this method to quantify EEG-based slow waves during sleep and wake states in both mouse and human subjects with mTBI. A modified coherence index that accounts for information from multiple channels was calculated as a measure of slow wave synchrony. RESULTS: Brain-injured mice showed significantly higher theta:alpha amplitude ratios and significantly more slow waves during spontaneous wakefulness and during prolonged sleep deprivation, compared to sham-injured control mice. Human subjects with mTBI showed significantly higher theta:beta amplitude ratios and significantly more EEG slow waves while awake compared to age-matched control subjects. We then quantified the global coherence index of slow waves across several EEG channels in human subjects. Individuals with mTBI showed significantly less EEG global coherence compared to control subjects while awake, but not during sleep. EEG global coherence was significantly correlated with severity of post-concussive symptoms (as assessed by the Neurobehavioral Symptom Inventory scale). CONCLUSION AND IMPLICATIONS: Taken together, our data from both mouse and human studies suggest that EEG slow wave quantity and the global coherence index of slow waves may represent a sensitive marker for the diagnosis and prognosis of mTBI and post-concussive symptoms

EEG Slow Waves in Traumatic Brain Injury: Convergent Findings in Mouse and Man

OBJECTIVE: Evidence from previous studies suggests that greater sleep pressure, in the form of EEG-based slow waves, accumulates in specific brain regions that are more active during prior waking experience. We sought to quantify the number and coherence of EEG slow waves in subjects with mild traumatic brain injury (mTBI). METHODS: We developed a method to automatically detect individual slow waves in each EEG channel, and validated this method using simulated EEG data. We then used this method to quantify EEG-based slow waves during sleep and wake states in both mouse and human subjects with mTBI. A modified coherence index that accounts for information from multiple channels was calculated as a measure of slow wave synchrony. RESULTS: Brain-injured mice showed significantly higher theta:alpha amplitude ratios and significantly more slow waves during spontaneous wakefulness and during prolonged sleep deprivation, compared to sham-injured control mice. Human subjects with mTBI showed significantly higher theta:beta amplitude ratios and significantly more EEG slow waves while awake compared to age-matched control subjects. We then quantified the global coherence index of slow waves across several EEG channels in human subjects. Individuals with mTBI showed significantly less EEG global coherence compared to control subjects while awake, but not during sleep. EEG global coherence was significantly correlated with severity of post-concussive symptoms (as assessed by the Neurobehavioral Symptom Inventory scale). CONCLUSION AND IMPLICATIONS: Taken together, our data from both mouse and human studies suggest that EEG slow wave quantity and the global coherence index of slow waves may represent a sensitive marker for the diagnosis and prognosis of mTBI and post-concussive symptoms

Spectral index of the H2O-maser emitting planetary nebula IRAS 17347-3139

We present radio continuum observations of the planetary nebula (PN) IRAS 17347-3139 (one of the only two known to harbour water maser emission), made to derive its spectral index and the turnover frequency of the emission. The spectrum of the source rises in the whole frequency range sampled, from 2.4 to 24.9 GHz, although the spectral index seems to decrease at the highest frequencies (0.79+-0.04 between 4.3 and 8.9 GHz, and 0.64+-0.06 between 16.1 and 24.9 GHz). This suggests a turnover frequency around 20 GHz (which is unusual among PNe, whose radio emission usually becomes optically thin at frequencies < 10 GHz), and a relatively high emission measure (1.5 x 10^9 cm^{-6} pc). The radio continuum emission has increased by a factor of ~1.26 at 8.4 GHz in 13 years, which can be explained as expansion of the ionized region by a factor of ~1.12 in radius with a dynamical age of ~120 yr and at an expansion velocity of ~5-40 km/s. These radio continuum characteristics, together with the presence of water maser emission and a strong optical extinction suggest that IRAS 17347-3139 is one of the youngest PNe known, with a relatively massive progenitor star.Comment: Five pages, 2 figures, accepted by MNRA

Mimicking diffuse supernova antineutrinos with the Sun as a source

Measuring the electron antineutrino component of the cosmic diffuse supernova neutrino background (DSNB) is the next ambitious goal for low-energy neutrino astronomy. The largest flux is expected in the lowest accessible energy bin. However, for E < 15 MeV a possible signal can be mimicked by a solar electron antineutrino flux that originates from the usual 8B neutrinos by spin-flavor oscillations. We show that such an interpretation is possible within the allowed range of neutrino electromagnetic transition moments and solar turbulent field strengths and distributions. Therefore, an unambiguous detection of the DSNB requires a significant number of events at E > 15 MeV.Comment: 4 pages, 1 figur

Confronting Spin Flavor Solutions of the Solar Neutrino Problem with current and future solar neutrino data

We show that spin flavor precession solutions to the solar neutrino problem, although preferred by the latest solar data, are ruled out by the first results from the KamLAND reactor experiment, at more than 3_sigma. An illustrative chi2 plot comparing these descriptions with oscillations is given.Comment: new appendix added discussing the impact of the KamLAND data. This updates the one published in Phys.Rev.D66:093009,200

Constraining Majorana neutrino electromagnetic properties from the LMA-MSW solution of the solar neutrino problem

In this paper we use solar neutrino data to derive stringent bounds on Majorana neutrino transition moments (TMs). Should such be present, they would contribute to the neutrino--electron scattering cross section and hence alter the signal observed in Super-Kamiokande. Motivated by the growing robustness of the LMA-MSW solution of the solar neutrino problem indicated by recent data, and also by the prospects of its possible confirmation at KamLAND, we assume the validity of this solution, and we constrain neutrino TMs by using the latest global solar neutrino data. We find that all elements of the TM matrix can be bounded at the same time. Furthermore, we show how reactor data play a complementary role to the solar neutrino data, and use the combination of both data sets to improve the current bounds. Performing a simultaneous fit of LMA-MSW oscillation parameters and TMs we find that 6.3 times 10^{-10} mu_B and 2.0 times 10^{-10} mu_B are the 90% C.L. bounds from solar and combined solar + reactor data, respectively. Finally, we perform a simulation of the upcoming Borexino experiment and show that it will improve the bounds from today's data by roughly one order of magnitude.Comment: Latex, 24 pages, 6 figures; misprints correcte

Global Analysis of the post-SNO Solar Neutrino Data for Standard and Non-Standard Oscillation Mechanisms

What can we learn from solar neutrino observations? Is there any solution to the solar neutrino anomaly which is favored by the present experimental panorama? After SNO results, is it possible to affirm that neutrinos have mass? In order to answer such questions we analyze the current available data from the solar neutrino experiments, including the recent SNO result, in view of many acceptable solutions to the solar neutrino problem based on different conversion mechanisms, for the first time, using the same statistical procedure. This allows us to do a direct comparison of the goodness of the fit among different solutions, from which we can discuss and conclude on the current status of each proposed dynamical mechanism. These solutions are based on different assumptions: (a) neutrino mass and mixing, (b) non-vanishing neutrino magnetic moment, (c) the existence of non-standard flavor-changing and non-universal neutrino interactions and (d) the tiny violation of the equivalence principle. We investigate the quality of the fit provided by each one of these solutions not only to the total rate measured by all the solar neutrino experiments but also to the recoil electron energy spectrum measured at different zenith angles by the Super-Kamiokande collaboration. We conclude that several non-standard neutrino flavor conversion mechanisms provide a very good fit to the experimental data which is comparable with (or even slightly better than) the most famous solution to the solar neutrino anomaly based on the neutrino oscillation induced by mass.Comment: Minor changes in the solar magnetic field profile used, and some refferences added. Final version to appear in PR