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

Fiber Optic Sensor Embedded Smart Helmet for Real-Time Impact Sensing and Analysis through Machine Learning

Background: Mild traumatic brain injury (mTBI) strongly associates with chronic neurodegenerative impairments such as post-traumatic stress disorder (PTSD) and mild cognitive impairment. Early detection of concussive events would significantly enhance the understanding of head injuries and provide better guidance for urgent diagnoses and the best clinical practices for achieving full recovery. New method: A smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The transient signals provide both magnitude and directional information about the impact event, and the data can be used for training machine learning (ML) models. Results: The FBG-embedded smart helmet prototype successfully achieved real-time sensing of concussive events. Transient data “fingerprints” consisting of both magnitude and direction of impact, were found to correlate with types of blunt-force impactors. Trained ML models were able to accurately predict (R2 ∼ 0.90) the magnitudes and directions of blunt-force impact events from data not used for model training. Comparison with existing methods: The combination of the smart helmet data with analyses using ML models provides accurate predictions of the types of impactors that caused the events, as well as the magnitudes and the directions of the impact forces, which are unavailable using existing devices. Conclusion: This work resulted in an ML-assisted, FBG-embedded smart helmet for real-time identification of concussive events using a highly accurate multi-metric strategy. The use of ML-FBG smart helmet systems can serve as an early-stage intervention strategy during and immediately following a concussive event

Discounting Women: Doubting Domestic Violence Survivors’ Credibility and Dismissing Their Experiences

In recent months, we’ve seen an unprecedented wave of testimonials about the serious harms women all too frequently endure. The #MeToo moment, the #WhyIStayed campaign, and the Larry Nassar sentencing hearings have raised public awareness not only about workplace harassment, domestic violence, and sexual abuse, but also about how routinely women survivors face a Gaslight-style gauntlet of doubt, disbelief, and outright dismissal of their stories. This pattern is particularly disturbing in the justice system, where women face a legal twilight zone: laws meant to protect them and deter further abuse often fail to achieve their purpose, because women telling stories of abuse by their male partners are simply not believed. To fully grasp the nature of this new moment in gendered power relations—and to cement the significant gains won by these public campaigns—we need to take a full, considered look at when, how, and why the justice system and other key social institutions discount women’s credibility. We use the lens of intimate partner violence to examine the ways in which women’s credibility is discounted in a range of legal and social service system settings. First, judges and others improperly discount as implausible women’s stories of abuse, based on a failure to understand both the symptoms arising from neurological and psychological trauma, and the practical constraints on survivors’ lives. Second, gatekeepers unjustly discount women’s personal trustworthiness, based on both inaccurate interpretations of survivors’ courtroom demeanor and negative cultural stereotypes about women and their motivations for seeking assistance. Moreover, even when a woman manages to overcome all the initial modes of institutional skepticism that minimize her account of abuse, she often finds that the systems designed to furnish her with help and protection dismiss the importance of her experiences. Instead, all too often, the arbiters of justice and social welfare adopt and enforce legal and social policies and practices with little regard for how they perpetuate patterns of abuse. Two distinct harms arise from this pervasive pattern of credibility discounting and experiential dismissal. First, the discrediting of survivors constitutes its own psychic injury—an institutional betrayal that echoes the psychological abuse women suffer at the hands of individual perpetrators. Second, the pronounced, nearly instinctive penchant for devaluing women’s testimony is so deeply embedded within survivors’ experience that it becomes a potent, independent obstacle to their efforts to obtain safety and justice. The reflexive discounting of women’s stories of domestic violence finds analogs among the kindred diminutions and dismissals that harm so many other women who resist the abusive exercise of male power, from survivors of workplace harassment to victims of sexual assault on and off campus. For these women, too, credibility discounts both deepen the harm they experience and create yet another impediment to healing and justice. Concrete, systematic reforms are needed to eradicate these unjust, gender-based credibility discounts and experiential dismissals, and to enable women subjected to male abuses of power at long last to trust the responsiveness of the justice system

An exploration of sarcasm detection in children with Attention Deficit Hyperactivity Disorder

This document is the Accepted Manuscript version of the following article: Amanda K. Ludlow, Eleanor Chadwick, Alice Morey, Rebecca Edwards, and Roberto Gutierrez, ‘An exploration of sarcasm detection in children with Attention Deficit Hyperactivity Disorder’, Journal of Communication Disorders, Vol. 70: 25-34, November 2017. Under embargo. Embargo end date: 31 October 2019. The Version of Record is available at doi: https://doi.org/10.1016/j.jcomdis.2017.10.003.The present research explored the ability of children with ADHD to distinguish between sarcasm and sincerity. Twenty-two children with a clinical diagnosis of ADHD were compared with 22 age and verbal IQ matched typically developing children using the Social Inference–Minimal Test from The Awareness of Social Inference Test (TASIT, McDonald, Flanagan, & Rollins, 2002). This test assesses an individual’s ability to interpret naturalistic social interactions containing sincerity, simple sarcasm and paradoxical sarcasm. Children with ADHD demonstrated specific deficits in comprehending paradoxical sarcasm and they performed significantly less accurately than the typically developing children. While there were no significant differences between the children with ADHD and the typically developing children in their ability to comprehend sarcasm based on the speaker’s intentions and beliefs, the children with ADHD were found to be significantly less accurate when basing their decision on the feelings of the speaker, but also on what the speaker had said. Results are discussed in light of difficulties in their understanding of complex cues of social interactions, and non-literal language being symptomatic of children with a clinical diagnosis of ADHD. The importance of pragmatic language skills in their ability to detect social and emotional information is highlighted.Peer reviewe

Use of the Kaufman Brief Intelligence Test - Second Edition as an embedded measure of malingering in a college population

In today’s economic decline, there is a growing pressure for the reform of healthcare. Clinicians need to treat only those individuals who have true symptoms and problems. Individuals who exaggerate or feigning cognitive impairments are straining an already over-burdened healthcare system (Haines & Norris, 2001). A collaborative approach in which a clinician gathers information from an interview, behavior observations, collateral information, and assessments is recommended to detect if an individual is attempting to malinger. Assessments are especially important if a clinician should be called to court. Over two-thirds of neuropsychologists use at least one specialized technique for detecting malingering (Slick, Tan, Strauss, & Hultsch, 2004). This research has primarily focused on finding if the Kaufman Brief Intelligence Test- Second Edition (KBIT-2) would be able to have a cut-off score that would help determine if an individual is malingering. The KBIT-2 was not designed to measure malingering; however, the purpose of this study is to determine if there is any promise

Automated monitoring of early neurobehavioral changes in mice following traumatic brain injury

Traumatic brain injury often causes a variety of behavioral and emotional impairments that can develop into chronic disorders. Therefore, there is a need to shift towards identifying early symptoms that can aid in the prediction of traumatic brain injury outcomes and behavioral endpoints in patients with traumatic brain injury after early interventions. In this study, we used the SmartCage system, an automated quantitative approach to assess behavior alterations in mice during an early phase of traumatic brain injury in their home cages. Female C57BL/6 adult mice were subjected to moderate controlled cortical impact (CCI) injury. The mice then received a battery of behavioral assessments including neurological score, locomotor activity, sleep/wake states, and anxiety-like behaviors on days 1, 2, and 7 after CCI. Histological analysis was performed on day 7 after the last assessment. Spontaneous activities on days 1 and 2 after injury were significantly decreased in the CCI group. The average percentage of sleep time spent in both dark and light cycles were significantly higher in the CCI group than in the sham group. For anxiety-like behaviors, the time spent in a light compartment and the number of transitions between the dark/light compartments were all significantly reduced in the CCI group than in the sham group. In addition, the mice suffering from CCI exhibited a preference of staying in the dark compartment of a dark/light cage. The CCI mice showed reduced neurological score and histological abnormalities, which are well correlated to the automated behavioral assessments. Our findings demonstrate that the automated SmartCage system provides sensitive and objective measures for early behavior changes in mice following traumatic brain injury