EFFECT OF CHRONIC AIRWAY INFLAMMATION INDUCED BY ALLERGEN SENSITIZATION ON VAGAL BRONCHOPULMONARY SENSORY NERVES IN RATS

Airway hyperresponsivness (AHR) is one of most prominent pathophysiological features of asthma. Increasing evidence suggests that vagal bronchopulmonary afferents may be involved in the development of AHR. However, the underlying mechanisms are not clear. Therefore, the purpose of this dissertation was to investigate the effect of chronic airway inflammation induced by allergen sensitization on vagal bronchopulmonary afferents. The study was carried out in an animal model of allergic asthma. Brown-Norway rats were sensitized by intraperitoneal Ovalbumin (Ova) and exposed to aerosolized Ova 3 times/week for three weeks. Control rats received the vehicle. In vivo single-fiber recording technique was applied in this study. Our results showed that chronic Ova exposure caused an elevated baseline activity of pulmonary Cfibers, and a distinctly higher sensitivity of these afferents to chemical stimulants and lung inflation. After an acute Ova inhalation challenge, the increase in baseline activity and the excitability of pulmonary C-fibers were further augmented in sensitized rats, but not in control rats. In addition, sensitivity of pulmonary myelinated afferents to capsaicin was significantly elevated after chronic airway inflammation was induced by allergen. Furthermore, immunohistochemsitry data showed that, in nodose ganglia the proportion of transient receptor potential vanilloids type 1 channels (TRPV1)-expressing bronchopulmonary neurons was significantly higher in sensitized rats than in controls. This increase of TRPV1 expression was found mainly in neurofilament-positive neurons (myelinated neurons), but this effect was absent in jugular ganglia. In conclusion, allergen-induced airway inflammation caused a pronounced sensitizing effect on vagal pulmonary non-myelinated (C-fiber) afferents and elevated the sensitivity of vagal pulmonary myelinated afferents to capsaicin. The latter was accompanied by the upregulation of TRPV1 expression in these myelinated neurons

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Individualized Cognitive Modeling for Close-Loop Task Mitigation

An accurate real-time operator functional state assessment makes it possible to perform task management, minimize risks, and improve mission performance. In this paper, we discuss the development of an individualized operator functional state assessment model that identifies states likely leading to operational errors. To address large individual variations, we use two different approaches to build a model for each individual using its data as well as data from subjects with similar responses. If a subject\u27s response is similar to that of the individual of interest in a specific functional state, all the training data from this subject will be used to build the individual model. The individualization methods have been successfully verified and validated with a driving test data set provided by the University of Iowa. With the individualized models, the mean squared error can be significantly decreased (by around 20%)

Deep Models for Engagement Assessment With Scarce Label Information

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled 20 min of the EEG data for each pilot. The EEG signals were preprocessed and power spectral features were extracted. The deep models were pretrained by the unlabeled data and were fine-tuned by a different proportion of the labeled data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for engagement assessment. The models were then tested on the remaining labeled data. We compared performances of the new data representations with the original EEG features for engagement assessment. Experimental results show that the representations learned by the deep models yielded better accuracies for the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based on different proportions of the labeled data for training, as compared with the corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%) achieved by the original EEG features. Deep models are effective for engagement assessment especially when less label information was used for training

A Systematic Approach for Engagement Analysis Under Multitasking Environments

An overload condition can lead to high stress for an operator and further cause substantial drops in performance. On the other extreme, in automated systems, an operator may become underloaded; in which case, it is difficult for the operator to maintain sustained attention. When an unexpected event occurs, either internal or external to the automated system, a disengaged operation may neglect, misunderstand, or respond slowly/inappropriately to the situation. In this paper, we discuss a systematic approach monitor for extremes of cognitive workload and engagement in multitasking environments. Inferences of cognitive workload ar engagement are based on subjective evaluations, objective performance measures, physiological signals, and task analysis results. The systematic approach developed In this paper aggregates these types of information collected under the multitasking environment and can provide a real-time assessment or engagement

Engagement Assessment Using EEG Signals

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance

Model Individualization for Real-Time Operator Functional State Assessment

Proper assessment of Operator Functional State (OFS) and appropriate workload modulation offer the potential to improve mission effectiveness and aviation safety in both overload and under-load conditions. Although a wide range of research has been devoted to building OFS assessment models, most of the models are based on group statistics and little or no research has been directed towards model individualization, i.e., tuning the group statistics based model for individual pilots. Moreover, little emphasis has been placed on monitoring whether the pilot is disengaged during low workload conditions. The primary focus of this research is to provide a real-time engagement assessment technique considering individual variations in an aviation environment. This technique is based on an advanced machine learning technique, called enhanced committee machine. We have investigated two different model individualization approaches: similarity-based and dynamic ensemble selection-based. The basic idea of the similarity-based technique is to find similar subjects from the training data pool and use their data together with the limited training data from the test subject to build an individualized OFS assessment model. The dynamic ensemble selection dynamically select data points in a validation dataset (with labels) that are adjacent to each test sample, and evaluate all the trained models using the identified data points. The best performing models will be selected and maximum voting can be applied to perform individualized assessment for the test sample. To evaluate the developed approaches, we have collected data from a high fidelity Boeing 737 simulator. The results show that the performance of the dynamic ensemble selection approach is comparable to that achieved from an individual model (assuming sufficient data is available from each individual)

EEG Artifact Removal Using a Wavelet Neural Network

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data

A Systematic Approach for Real-Time Operator Functional State Assessment

A task overload condition often leads to high stress for an operator, causing performance degradation and possibly disastrous consequences. Just as dangerous, with automated flight systems, an operator may experience a task underload condition (during the en-route flight phase, for example), becoming easily bored and finding it difficult to maintain sustained attention. When an unexpected event occurs, either internal or external to the automated system, the disengaged operator may neglect, misunderstand, or respond slowly/inappropriately to the situation. In this paper, we discuss an approach for Operator Functional State (OFS) monitoring in a typical aviation environment. A systematic ground truth finding procedure has been designed based on subjective evaluations, performance measures, and strong physiological indicators. The derived OFS ground truth is continuous in time compared to a very sparse estimation of OFS based on an expert review or subjective evaluations. It can capture the variations of OFS during a mission to better guide through the training process of the OFS assessment model. Furthermore, an OFS assessment model framework based on advanced machine learning techniques was designed and the systematic approach was then verified and validated with experimental data collected in a high fidelity Boeing 737 simulator. Preliminary results show highly accurate engagement/disengagement detection making it suitable for real-time applications to assess pilot engagement

TPM: Cloud-Based Tele PTSD Monitor Using Multi-Dimensional Information

An automated system that can remotely and non-intrusively screen individuals at high risk for Post-Traumatic Stress Disorder (PTSD) and monitor their progress during treatment would be desired by many Veterans Affairs (VAs) as well as other PTSD treatment and research organizations. In this paper, we present an automated, cloud-based Tele-PTSD Monitor (TPM) system based on the fusion of multiple sources of information. The TPM system can be hosted in a cloud environment and accessed through landline or cell phones, or on the Internet through a web portal or mobile application (app)