Publications by Author: Nicholas D. Boltin

An electroencephalogram (EEG) is a medical test that measures brain activity and helps diagnose conditions like acute stress disorder (ASD). According to the World Health Organization (WHO), stress is a state of worry or mental tension caused by challenging situations [1]. When someone experiences an especially traumatic, shocking, or terrifying event, their body can respond with ASD, which is a short-term condition that can develop into post-traumatic stress disorder (PTSD). ASD symptoms, such as anxiety, detachment, and irritability, are similar to those of PTSD [2].

Clinicians regularly face high-stress situations, putting them at greater risk of ASD. Over time, this stress can lead to burnout, which the Mayo Clinic describes as a constant state of exhaustion, fatigue, and emotional overwhelm due to ongoing workplace stress [3]. Burnout doesn’t just affect individual clinicians—it leads many to change careers or leave the medical field entirely, worsening the nationwide healthcare worker shortage. Identifying ASD early could help prevent burnout and improve clinician well-being, making real-time stress monitoring a valuable tool.

A portable, wearable EEG with built-in heart rate monitoring was developed to address this. The prototype was improved using computer-aided design (CAD) and 3D printing to incorporate the heart rate sensor into an earpiece, decreasing the weight, cost, and volume of materials by 54.5%, 85.92%, and 55.95%, respectively. These updates make the device more comfortable and discreet, allowing clinicians to wear it throughout their workday without it being intrusive.

Moving forward, we aim to downsize components further, explore biocompatible 3D-printed materials, and enhance overall ergonomics. As EEG and heart rate sensors continue to get smaller, this technology could eventually be integrated into something as subtle as a pair of glasses, offering a seamless way to monitor stress levels and support clinician well-being.

The effectiveness of material to emit energy as thermal radiation is important in determining the apparent temperature in infrared thermographic measurements. For this reason, a number of measurements of the thermal emissivity in the mid-infrared thermographic (8–12 µm) region have been reported for fabrics. However, many fabrics adsorb moisture from the air, and condensed water has a relatively high thermal emissivity. In this manuscript, we report measurements of adsorption isotherms and mid-infrared thermal emissivity for nylon, cotton, polyester, and acrylic as a function of their moisture content in weight percent at temperatures just above ambient. We find that the order of water mass percentage gain for the fabrics in high humidity conditions are polyester < acrylic < nylon < cotton. The thermal emissivity is ∼0.88 independent of moisture content for the fabrics polyester, cotton, and nylon, while acrylic shows a pronounced increase in thermal emissivity as moisture content increases, ranging from ɛ ∼ 0.81 at low humidity conditions to ɛ ∼ 0.88 under high humidity conditions. In this work, emissivity measurements are made by imaging through a novel infrared window made from household cling wrap and interpreted with equations that are independent of window transmittance and sample temperature.

The emissivity of nylon, cotton, polyester and acrylic fabrics coated with dried rat blood have been determined in the thermographic infrared region (~8–12 µm wavelength) at 40 °C and at the lowest humidity we could attain in the laboratory. Results show the emissivity of known nylon (ε = 0.87), cotton (ε = 0.88) and polyester (ε = 0.88) fabrics in our laboratory increase by 0.01, 0.01 and 0.03 respectively when coated with dried blood at a concentration of 100 µL of whole blood per 0.9 cm2 of fabric. An acrylic fabric (ε = 0.82) shows an increase in emissivity of 0.05 under the same conditions. We also investigated the change in emissivity of an acrylic fabric sample coated heavily with whole rat blood 8 years previously as a function of humidity and report that its emissivity increases from 0.90 at low humidity to nearly 0.94 at 90% humidity.

Background:Chemical exposures pose a significant threat to life. A rapid assessment by first responders and emergency nurses is required to reduce death and disability. Currently, no informatics tools exist to process victims of chemical exposures efficiently. The surge of patients into a hospital emergency department during a mass casualty incident creates additional stress on an already overburdened system, potentially placing patients at risk and challenging staff to process patients for appropriate care and treatment efficacy. Traditional emergency department triage models are oversimplified during highly stressed mass casualty incident scenarios in which there is little margin for error. Emerging mobile technology could alleviate the burden placed on nurses by allowing the freedom to move about the emergency department and stay connected to a decision support system.

Objective:This study aims to present and evaluate a new mobile tool for assisting emergency department personnel in patient management and triage during a chemical mass casualty incident.

Methods:Over 500 volunteer nurses, students, and first responders were recruited for a study involving a simulated chemical mass casualty incident. During the exercise, a mobile application was used to collect patient data through a kiosk system. Nurses also received tablets where they could review patient information and choose recommendations from a decision support system. Data collected was analyzed on the efficiency of the app to obtain patient data and on nurse agreement with the decision support system.

Results:Of the 296 participants, 96.3% (288/296) of the patients completed the kiosk system with an average time of 3 minutes, 22 seconds. Average time to complete the entire triage process was 5 minutes, 34 seconds. Analysis of the data also showed strong agreement among nurses regarding the app’s decision support system. Overall, nurses agreed with the system 91.6% (262/286) of the time when it came to choose an exposure level and 84.3% (241/286) of the time when selecting an action.

Conclusions:The app reliably demonstrated the ability to collect patient data through a self-service kiosk system thus reducing the burden on hospital resources. Also, the mobile technology allowed nurses the freedom to triage patients on the go while staying connected to a decision support system in which they felt would give reliable recommendations.

In this report we examine the effectiveness of WISER in identification of a chemical culprit during a chemical based Mass Casualty Incident (MCI). We also evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks (ANN) using the same experimental conditions as WISER. The reverse engineered set of Signs/Symptoms from the WISER application was used as the training set and 31,100 simulated patient records were used as the testing set. Three sets of simulated patient records were generated by 5%, 10% and 15% perturbation of the Signs/ Symptoms of each chemical record. While all three methods achieved a 100% training accuracy, WISER, BDT and ANN produced performances in the range of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of dimensional reduction using ANN illustrated a dimensional collapse from 79 variables to 40 with little loss of classification performance.