Time and frequency domain analysis of physiological features during autonomic dysreflexia after spinal cord injury

IntroductionAutonomic dysreflexia (AD) affects about 70% of individuals with spinal cord injury (SCI) and can have severe consequences, including death if not promptly detected and managed. The current gold standard for AD detection involves continuous blood pressure monitoring, which can be inconvenient. Therefore, a non-invasive detection device would be valuable for rapid and continuous AD detection.MethodsImplanted rodent models were used to analyze autonomic dysreflexia after spinal cord injury. Skin nerve activity (SKNA) features were extracted from ECG signals recorded non-invasively, using ECG electrodes. At the same time, blood pressure and ECG data sampled was collected using an implanted telemetry device. Heart rate variability (HRV) features were extracted from these ECG signals. SKNA and HRV parameters were analyzed in both the time and frequency domain.ResultsWe found that SKNA features showed an increase approximately 18 seconds before the typical rise in systolic blood pressure, indicating the onset of AD in a rat model with upper thoracic SCI. Additionally, low-frequency components of SKNA in the frequency domain were dominant during AD, suggesting their potential inclusion in an AD detection system for improved accuracy.DiscussionUtilizing SKNA measurements could enable early alerts to individuals with SCI, allowing timely intervention and mitigation of the adverse effects of AD, thereby enhancing their overall well-being and safety

Intravenous Polyethylene Glycol Inhibits the Loss of Cerebral Cells after Brain Injury

We have tested the effectiveness of polyethylene glycol (PEG) to restore the integrity of neuronal membranes after mechanical damage secondary to severe traumatic brain injury (TBI) produced by a standardized head injury model in rats. We provide additional detail on the standardization of this model, particularly the use and storage of foam bedding that serves to both support the animal during the impact procedure and to dampen the acceleration of the brass weight. Further, we employed a dye exclusion technique using ethidium bromide (EB; quantitative evaluation) and horseradish peroxidase (HRP; qualitative evaluation). Both have been successfully used previously to evaluate neural injury in the spinal cord since they enter cells when their plasma membranes are damaged. We quantified EB labeling (90 M in 110 L of sterile saline) after injection into the left lateral ventricle of the rat brain 2 h after injury. At six h after injection and 8 h after injury, the animals were sacrificed and the brains were analyzed. In the injured rat brain, EB entered cells lining and medial to the ventricles, particularly the axons of the corpus callosum. There was minimal EB labeling in uninjured control brains, limited to cells lining the luminal surfaces of the ventricles. Intravenous injections of PEG (1 cc of saline, 30% by volume, 2000 MW) immediately after severe TBI resulted in significantly decreased EB uptake compared with injured control animals. A similar result was achieved using the larger marker, HRP. PEG-treated brains closely resembled those of uninjured animals

Access to Personal Transportation for People with Disabilities with Autonomous Vehicles

The objective of this paper was to explore the potential of emerging technology of autonomous vehicles in accessible transportation and incorporate these findings a standardized transportation solution that readily accommodates future travelers with disabilities based on careful study on current trends in accessible transportation and interviews and surveys that were conducted as a part of this effort. The suggested solution and design principles associated with it took in account, the popular opinions of people with disabilities as well as various experts in the field of accessible transportation. The presented solution is based on emerging technology that is being actively pursued by the automotive industry and research institutions and seriously being considered through current and pending state legislation as a viable product in the near future. This paper explores the legal, technical and safety obstacles that lay in the path to making this a reality

Report on the Challenges of Air Transportation Experienced by People with Disabilities

Boarding an airplane is difficult for persons with mobility impairments and increases the risk of injury to both passengers and employees. Airplane seats are uncomfortable and lack the necessary support for many individuals with disabilities. Additionally, airplane restrooms can be inaccessible to wheelchair users. Potential solutions for these issues include the use of detachable plane seats or personal wheelchairs on board and an airplane redesign to provide additional restroom space. The number of service and emotional support animals being brought on airplanes have also increased substantially over the past few years. Passengers that travel with their service animals must contend with having to follow different rules for different airlines carriers and not having sufficient space for animals to be safe and comfortable

Challenges Faced by Persons with Disabilities Using Self-Service Technologies

Foreseeable game changing solutions to SSTs will allow for better universal access by better implementing features that are easy and intuitive to use from the inception. Additional robotic advancements will allow for better and easier delivery of goods for consumers. Improvements to artificial intelligence will allow for better communication through natural language and alternative forms of communication. Furthermore, artificial intelligence will aid consumers at SSTs by remembering the consumers preferences and needs. With all foreseeable game changing solutions people with disabilities will be consulted when new and improved SSTs are being developed allowing for the SST to maximize its potential

Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI

Abstract This paper presents an AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries. Current AD detection methods are limited, lacking non-invasive monitoring systems. We propose a model that combines skin nerve activity (SKNA) signals with a deep neural network (DNN) architecture to overcome this limitation. The DNN is trained on a meticulously curated dataset obtained through controlled colorectal distension, inducing AD events in rats with spinal cord surgery above the T6 level. The proposed system achieves an impressive average classification accuracy of 93.9% ± 2.5%, ensuring accurate AD identification with high precision (95.2% ± 2.1%). It demonstrates a balanced performance with an average F1 score of 94.4% ± 1.8%, indicating a harmonious balance between precision and recall. Additionally, the system exhibits a low average false-negative rate of 4.8% ± 1.6%, minimizing the misclassification of non-AD cases. The robustness and generalizability of the system are validated on unseen data, maintaining high accuracy, F1 score, and a low false-negative rate. This AI-powered solution represents a significant advancement in non-invasive, real-time AD monitoring, with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries. This research contributes a promising solution to the critical healthcare challenge of AD detection and monitoring