Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to characterize the internal structure of individual breaths. Traditional approaches treat airflow as a quasi-periodic signal and rely on global descriptors such as tidal volume or peak flow, obscuring sub-breath events that reflect neuromuscular coordination and compensatory breathing strategies. This study introduces a parametric framework for decomposing inspiratory airflow into a small number of time-localized components with explicit amplitude, onset time, and duration parameters. Unlike spectral or data-adaptive methods, the proposed approach employs physiologically grounded basis functions, Half-Sine, Gaussian, and Beta, to represent intrabreath waveform morphology through constrained nonlinear optimization. Evaluation across 8,276 breaths demonstrates high reconstruction accuracy (mean squared error < 0.001 for four-component models) and robust parameter precision under moderate noise. Component-derived features describing sub-breath timing and coordination improved classification of cognitive fatigue states arising from cognitive-respiratory competition by up to 30.7

The critical brain hypothesis suggests that the brain operates near critical points, balancing order and disorder for optimal processing via self-organized neural firing patterns. However, important gaps remain in understanding criticality in human in vivo brain dynamics using electroencephalography (EEG), particularly in translating these systems neuroscience concepts to operational applications (e.g., cognitive state detection, human-machine teaming). We address three key aspects that yet remain to be demonstrated: (1) how to decode large-scale EEG neural recordings coordination and self-organization of distributed neuronal populations as they evolve toward critical dynamics; (2) how windowed EEG neural events during cognitive tasks exhibit signatures of criticality; and (3) how decoding randomly selected or task-irrelevant null events fails to recover critical dynamics ruling out artifacts of noise that mimic scale invariance. To address these issues, we introduce EEG techniques that identify multiple interacting spectral oscillatory groups' self-organization through unique hierarchical arrangements utilizing an optimized filter bank-based approach while performing cognitive tasks. This EEG neuronal decoding method characterizes signals across 4-dimensions of intensity, phase, time, and frequency that are represented as a hierarchical rank. This allows us to observe how the brain's self-organization across numerous neuronal oscillatory bands during cognitive tasks, confirming criticality by fitting a power law estimate over a range of k≈[102,105]. Supplementing these findings, we demonstrate that during non-task periods, the power law fails to fit, indicating the absence of scale-invariant criticality (k≈[102,102]), showing that criticality emerges during cognitive tasks when the neuronal firing is sent to the outer cortex of the brain.

This study introduces a novel entropy-based methodology to quantitatively characterize non-linear transient breathing dynamics under respiratory stress. Environmental and pathophysiological stressors can disrupt the respiratory system's gas exchange, leading to compromise and compensatory mechanisms. We present a data-driven approach that systematically evaluates classical respiratory features alongside novel entropic features as key indicators under respiratory stress. We demonstrate that conventional metrics like breathing rate ( B R ), time of inspiration ( T I ), and expiration ( T E ) fail to capture discriminating features needed to detect early ventilatory instability and predict intervention needs. An exhaustive analysis of key respiratory fiducial points using entropic methods led to novel features for understanding respiratory mechanics and classifying respiratory states. We found that the non-linear dynamics of the transition times between inspiratory and expiratory phases (interphases) are crucial for assessing adaptability to respiratory challenges. This metric quantifies the complexity of transition duration (acceleration and deceleration between phases) and is essential for predicting declining breathing states. Our predictive model incorporating these novel approaches showed superior discriminating ability over models using classical features, achieving a 50.76% increase in predictive power as measured by the Area Under the Curve (AUC). These findings underscore the effectiveness of this entropy-based approach for early detection of respiratory compromise, with the best model achieving an AUC of 0.784. The results have significant implications for improving clinical monitoring of acute respiratory failure and managing chronic respiratory conditions.

Whole-brain EEG connectivity offers insights into how attention is modulated under respiratory load, aiming to improve our understanding of inter-regional brain communication in extreme environments such as aviation or deep-sea diving. These conditions impose significant respiratory and interoceptive challenges that strain cognitive control systems and elevate the risk of attention lapses. Particularly during voluntary breathing control, traditional human performance studies attempt to understand cognitive states such as attention by isolating activity in specific brain regions and frequency bands, overlooking the distributed dynamics of brain-body integration. Building on recent theories of global neural coordination, we propose a spectral connectivity framework that captures neuronal oscillatory power, temporal stability, and interregional synchrony across frequency bands and compare it to traditional network methods. This network analysis was validated through a predictive modeling framework, which identified the critical brain regions and connectivity patterns that sustain attention during respiratory stress. Such a predictive network analysis is first of its kind for address how brain networks are modulated and impact attention lapses which has allowed us to extend the understanding of neural control of breathing by framing it as a dynamic network system in the brain, in which inhibitory (weakening) and excitatory (strengthening) connections between brain regions modulate in response to respiratory load and cause adaptations in breathing patterns. These attention lapses involve widespread breakdowns in dynamic spectral coordination across the brain, not just in the frontal cortex, providing a more holistic neural marker for tracking cognitive state in demanding environments. Furthermore, our developed novel signal processing techniques have demonstrated an enhanced ability to characterize the neuronal connectivity patterns within the brain, providing a 7.11% increase in predictive power from traditional coherence metrics.

Breathing relies on unrestricted movement of the chest wall to maintain O2 and CO2 balance. Understanding the effects of chest and abdominal restrictions on respiratory function is critical for studying conditions such as respiratory diseases, extreme environments, and load-induced impairments. However, existing methods to simulate these restrictions are limited, lacking the ability to provide both static and dynamic conditions or precise load control. To address these gaps, we developed a novel chest wall and abdomen restriction device capable of independently applying and measuring static and dynamic loads with adjustable and reproducible force levels. Separate bands for the chest and abdomen enable targeted restrictions. In static conditions, the bands are immobilized, preventing any movement of the chest and abdomen. In dynamic conditions, constant force springs provide resistance, allowing movement when sufficient force is applied. Integrated sensors quantify applied loads and respiratory mechanics. To validate the device, healthy participants underwent pulmonary function testing under baseline, static, and dynamic restriction conditions. Significant reductions in forced expiratory volume (FEV1) and forced vital capacity (FVC) were observed under restrictions compared to baseline. Other respiratory metrics also differed significantly, highlighting distinct effects of static and dynamic restrictions. Pressure variability tests confirmed reproducibility and adjustability of loads, while displacement data from linear variable differential transducers (LVDTs) validated the device’s ability to distinguish static and dynamic effects. This device addresses prior limitations by enabling precise, reproducible loading and independent control of chest and abdominal restrictions, supporting research into respiratory diseases, extreme environments, and respiratory mechanics. Our results demonstrate its potential to advance respiratory function research and expand clinical and experimental applications.

Apolipoprotein E (ApoE) gene variations are involved in lipid metabolism and cholesterol transport, with the ApoE4 allele being a known risk factor associated with neurodegenerative conditions later in life. Emerging evidence suggests these genetic variations may also influence respiratory function and vitality. However, the specific impact of different ApoE genotypes on breathing patterns remains largely unexplored. This work investigates differences in breathing waveform characteristics and entropy statistics derived from plethysmography (PLETH) data between rat models possessing two distinct ApoE genotypes (referred to herein as gene59 and gene95). Findings reveal significant distributional differences in common plethysmography metrics and approximate entropy between the two genotypes, observed during both active and resting states. Additionally, the study examines the transient impact of sighs (deep breaths) on these breathing metrics, demonstrating that entropy and other measures are altered in the breaths immediately following a sigh.

BackgroundRespiratory impairment is a major concern in amyotrophic lateral sclerosis (ALS), shortening survival and lowering quality of life. One therapy with promise to delay respiratory decline in ALS is acute intermittent hypoxia (AIH), consisting of alternating periods of breathing mildly hypoxic (9%-12% O2) and normoxic (21% O2) gas. AIH stimulates spinal, serotonin-dependent neuroplasticity in rodent models, conferring functional benefits in diverse physiological systems without detectable pathology. However, in rodent models, AIH-induced neuroplasticity is constrained by distinct signaling cascades initiated by spinal adenosine. ObjectiveWe propose to investigate a therapeutic strategy to delay breathing compromise in those living with ALS by combining a selective adenosine 2A (A2A) receptor inhibitor (istradefylline) with AIH. The fundamental hypothesis guiding this proposal is that a single AIH trial after pretreatment with istradefylline enhances respiratory neuroplasticity versus AIH or sham intervention. MethodsWe propose to evaluate resting breathing, respiratory strength, and participant-reported symptoms in adults living with ALS after combined istradefylline plus AIH. A mixed within- and between-participant study design incorporates 4 test sessions, separated by approximately 2 weeks (±5 days). Testing conditions include single sessions of AIH + istradefylline, AIH + placebo, sham AIH (ie, normoxia) + placebo, and sham AIH + istradefylline. Safety and feasibility will be characterized using the rate of adverse events, changes in vital signs, and participant-reported breathing sensations (Aim 1). Neuroplasticity of breathing and motor function will be evaluated as changes in resting breathing, voluntary respiratory strength, respiratory control, and maximal pinch force (Aim 2). ResultsAs of January 2025, with a target sample of 16 participants in each group, 10 participants with ALS and 5 control participants completed study procedures. Recruiting is ongoing, and the final participant will complete the study by December 2025. Publication of results is expected by the end of 2026. ConclusionsThese aims will provide crucial data regarding the preliminary safety and feasibility of this paired intervention and help optimize therapeutic AIH as a rehabilitation strategy, thereby guiding further research concerning this novel treatment for ALS. Trial RegistrationClinicalTrials.gov NCT05377424; https://clinicaltrials.gov/study/NCT05377424 International Registered Report Identifier (IRRID)DERR1-10.2196/76105

Detecting cognitive states and impairments through EEG signals is crucial for applications in aviation and medicine and has broad applications in the field of human-machine interaction. However, existing methods often fail to capture the fine-grained neural dynamics of critical brain processes due to limited temporal resolution and inadequate signal decomposition techniques. To address this, we introduce the Spectral Intensity Stability (SIS) algorithm, a novel technique that analyzes the stability and competition of dominant brain frequency oscillations across granular timescales (≈4 ms). Unlike traditional spectral methods, SIS captures rapid neural transitions and hierarchical frequency dynamics, enabling more accurate characterization of task-specific cognitive processes. Our study focuses on EEG data from pilots performing multitasking simulations under hypoxic and non-hypoxic conditions, a high-stakes scenario where cognitive performance is crucial. We divided this multitasking scenario into specific cognitive states, such as task precursor, interruption, execution, and recovery. Our algorithm SIS achieved a 29.8% improvement in cognitive state classification compared to conventional methods, demonstrating superior accuracy in distinguishing both task states and hypoxic impairments. This work is novel because it bridges gaps left by traditional methods by revealing the role of hierarchical spectral dynamics in maintaining cognitive performance. Through the Granular Analysis Informing Neural Stability (GAINS) framework, we reveal how neuronal groups self-organize across fine-grained time scales, providing new understanding of task-switching, neural communication, and criticality. The findings highlight the potential for developing real-time cognitive monitoring systems to enhance safety and performance in environments where cognitive impairments can have serious consequences. Future research should extend these insights by incorporating transient behaviors and spatial dynamics to achieve a more comprehensive framework for characterizing cognitive states.

Cognitive impairment due to hypoxia significantly affects human performance, particularly during tasks that require sustained mental effort and engagement. Electroencephalography (EEG) has emerged as a valuable non-invasive tool for analyzing brain activity and cognitive performance. However, traditional EEG-based methods for human performance modeling, such as the Engagement Index and wavelet entropy, are limited in their ability to detect rapid and localized changes in brain dynamics that are critical for real-time modeling. This paper investigates Spectral Stability, an entropy-based method that captures fast, localized changes in brain activity by analyzing the hierarchical rankings of spectral intensities across EEG frequency bands. Using a data set collected under normoxic and hypoxic dual-task scenarios, we evaluate the ability of Spectral Stability to provide granular insight into oscillatory cognitive states in different regions of the brain. Our findings demonstrate that Spectral Stability reliably distinguishes between normoxic and hypoxic conditions across multiple brain regions, outperforming the Engagement Index in spatial specificity, while also correlating significantly with the Engagement Index, indicating shared underlying neural dynamics. These results highlight that Spectral Stability may provide additional key information within neuronal dynamics as a tool to advance cognitive state modeling and understanding real-time engagement variability under impairment.

Late Onset Pompe Disease (LOPD) is an incurable, rare progressive genetic disease. It is characterized by glycogen build-up, due to mutations in the gene encoding the lysosomal enzyme acid alpha glucosidase, throughout muscle and neural regions. This accumulation is associated with cell death and muscle fiber swelling, eventually leading to respiratory insuffciency and airway clearance (i.e. cough) deficiency. Impairments in effective airway clearance result from the inability to clear normal pulmonary volume. Emerging evidence indicates significant respiratory neuron dysfunction, altering pulmonary function in humans and animal models. It has been suggested that respiratory and airway clearance insuffciency observed in LOPD may be attributed to medullary motoneurons potentially associated with the respiratory and cough pattern generators (Fuller et al., 2013; DeRuisseau et al., 2009). However, the precise neural mechanisms that suppress breathing and cough are not fully understood. One possible target for LOPD respiratory insuffciencies may be reduced medullary inspiratory neuron activity within this network, which could result in decreased phrenic motoneuron output. Using a stochastic neural network simulator that consisted of discrete “integrate and fire” populations, we simulated a systematic decrease in three medullary inspiratory neuron populations: inspiratory driver (I-Driver), inspiratory decrementing (I-Dec), or inspiratory augmenting (I-Aug) neurons. We simulated breathing and three cough trials. By targeting these populations, we expected global decreased neural bursts, suppressed inspiratory motor drive, and/or suppressed expiratory motoneuron output. Simulations indicated that when the I-Dec motoneuron population decreased by 25%, it resulted in apnea and abolished cough. Reducing the I-Aug motoneuron population by 20% resulted in longer inspiratory phase durations (TI) and a decreased respiratory rate. During cough, there was a decrease in the duration and amplitudes of the expiratory motoneuronal activity associated with decreased cough receptor and pulmonary stretch receptor afferent feedback. When the I-Driver motoneuron population was reduced by 25%, TI decreased and I amplitude increased during respiration. During cough, lumbar and phrenic motoneuron discharge duration and amplitude decreased. These results are consistent with previously reported in vivo data. Overall, our simulation data support the plausibility of glycogen storage dysfunction impairing medullary neuronal and premotoneuronal excitability within the inspiratory network. Our results also suggest that some neurons may be more sensitive to glycogen storage than others within the brainstem inspiratory network. Supported by NHLBI L30HL165496, NIH T32 HL 134621 and 3OT2OD023854. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

In this study, we explore the work of breathing (WoB) experienced by aviators during the Anti-G Straining Maneuver (AGSM) to improve pilot safety and performance. Traditional airflow models of WoB fail to adequately distinguish between breathing rate and inspiratory frequency, leading to potentially inaccurate assessments. This mismatch can have serious implications, particularly in critical flight situations where understanding the true respiratory workload is essential for maintaining performance. To address these limitations, we used a non-sinusoidal model that captures the complexities of WoB under high inspiratory frequencies and varying dead space conditions. Our findings indicate that the classical airflow model tends to underestimate WoB, particularly at elevated inspiratory frequencies ranging from 0.5 to 2 Hz, where resistive forces play a significant role and elastic forces become negligible. Additionally, we show that an increase in dead space, coupled with high-frequency breathing, elevates WoB, heightening the risk of dyspnea among pilots. Interestingly, our analysis reveals that higher breathing rates lead to a decrease in total WoB, an unexpected finding suggesting that refining breathing patterns could help pilots optimize their energy expenditure. This research highlights the importance of examining the relationship between alveolar ventilation, breathing rate, and inspiratory frequency in greater depth within realistic flight scenarios. These insights indicate the need for targeted training programs and adaptive life-support systems to better equip pilots for managing respiratory challenges in high-stress situations. Ultimately, our research lays the groundwork for enhancing respiratory support for aviators, contributing to safer and more efficient flight operations.

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over a majority voting approach. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusion enables us to obtain a holistic picture of the users’ context and monitor their activity state with increased accuracy. Human activity recognition using noninvasive sensors allows us to capture the natural behavior of users in their day-to-day lives. This in-the-wild activity recognition, however, poses several key challenges that must be addressed to create effective classification models. The main challenges are class imbalance, uncertainty in classifier decisions, and large feature spaces. To address them, this study further explores a probabilistic sensor fusion method called Naive Adaptive Probabilistic Sensor (NAPS) Fusion. In doing so, we establish the viability of NAPS Fusion for natural human activity recognition using noninvasive sensing modalities. NAPS Fusion handles dimensionality reduction by creating reduced feature sets and mitigates the class imbalance issue through the use of Synthetic Minority Oversampling Technique (SMOTE). Moreover, NAPS Fusion addresses uncertainty in the decisions of classifiers using a Dempster-Shafer theoretic late fusion framework. Our empirical evaluation demonstrates that NAPS Fusion has broad applications beyond its original design for cognitive state detection. It outperforms similar decision level sensor fusion methods (late fusion using averaging, LFA, and late fusion using learned weights, LFL) in the detection of exercise and sedentary activities such as walking, running, lying down, and sitting. We observe improvements of up to 56% in F1 score and up to 59% in precision with NAPS Fusion over the compared methods.

In the area of human performance and cognitive research, machine learning (ML) problems become increas-ingly complex due to limitations in the experimental design, resulting in the development of poor predictive models. More specifically, experimental study designs produce very few data instances, have large class imbalances and conflicting ground truth labels, and generate wide data sets due to the diverse amount of sensors. From an ML perspective these problems are further exacerbated in anomaly detection cases where class imbalances occur and there are almost always more features than samples. Typically, dimensionality reduction methods (e.g., PCA, autoencoders) are utilized to handle these issues from wide data sets. However, these dimensionality reduction methods do not always map to a lower dimensional space appropriately, and they capture noise or irrelevant information. In addition, when new sensor modalities are incorporated, the entire ML paradigm has to be remodeled because of new dependencies introduced by the new information. Remodeling these ML paradigms is time-consuming and costly due to lack of modularity in the paradigm design, which is not ideal. Furthermore, human performance research experiments, at times, creates ambiguous class labels because the ground truth data cannot be agreed upon by subject-matter experts annotations, making ML paradigm nearly impossible to model.This work pulls insights from Dempster-Shafer theory (DST), stacking of ML models, and bagging to address uncertainty and ignorance for multi-classification ML problems caused by ambiguous ground truth, low samples, subject-to-subject variability, class imbalances, and wide data sets. Based on these insights, we propose a probabilistic model fusion approach, Naive Adaptive Probabilistic Sensor (NAPS), which combines ML paradigms built around bagging algorithms to overcome these experimental data concerns while maintaining a modular design for future sensor (new feature integration) and conflicting ground truth data. We demonstrate significant overall performance improvements using NAPS (an accuracy of 95.29%) in detecting human task errors (a four class problem) caused by impaired cognitive states and a negligible drop in performance with the case of ambiguous ground truth labels (an accuracy of 93.93%), when compared to other methodologies (an accuracy of 64.91%). This work potentially sets the foundation for other human-centric modeling systems that rely on human state prediction modeling.

With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be used to gain insight on future autonomous systems and make model translation to different airframes less difficult. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead-of-time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

The characterization of breathing dynamics provides researchers and clinicians the ability to differentiate respiratory compensation, impairment, disease progression, ventilator assistance, and the onset of respiratory failure. However, within many sub-fields of respiratory physiology, we still have challenges identifying changes within the breathing dynamics and critical respiratory states. We discuss one fundamental modeling of breathing and how modeling imprecise assumptions decades ago regarding breathing are still propagating into our quantitative analysis today, limiting our characterization and modeling of breathing. The assumption that breathing is a continuous sinusoidal wave that can consist of a single frequency which is composed of a stationary time-invariant process has limited our expanded discussion of breathing dynamics, modeling, functional testings, and metrics. Therefore, we address major misnomers regarding breathing dynamics, specifically rate, rhythm, frequency, and period. We demonstrate how these misnomers impact the characterization and modeling through the force equations that are linked to the Work of Breathing (WoB) and our interpretation of breathing dynamics through the fundamental models and create possible erroneous evaluations of work of breathing. This discussion and simplified non-periodic WoB models ultimately sets the foundation for improved quantitative approaches needed to further our understanding of breathing dynamics, compensation, and adaptation.

Extreme temperature or physiologically demanding environments often present in aerospace missions pose a high risk of heat stress to pilots and astronauts which can lead to heat illnesses and human performance decrements. This is particularly prevalent within military aircraft, where many flight research installations and airfields are located in hot arid desert or high-humidity tropical climates. The intense heat in these environments can exacerbate the severity of the heat stress already present in the pilot due other physiological and environmental stressors. To measure a key biological indicator of the level of heat stress, core body temperature, we propose a noninvasive method for subjects under extreme high thermal stress using heart rate and skin temperature measurements from mobile biosensors in real open-world environments. As an analog for pilots operating in extreme thermal environments, we utilize observations of professional race car drivers exposed to high thermal stress inside vehicle cockpits for several consecutive hours. The conditions experienced by the drivers not only incorporate the heat stress produced by the layered protective equipment but also the thermal stress from the operational environment and vehicle. A Kalman filter is designed to predict core body temperature utilizing linear models generated by the driver’s heart rate and skin temperature sensors. The data obtained at 15 races from 4 different drivers was used to train the linear models and validate the Kalman filter. Ground truth core body temperature measurements obtained from ingestible core temperature capsules were used to measure the filter’s performance. Despite the number of factors that negatively affected the quality of the biosensor data, such as stresssors due to the operational environment and vehicle (e.g., extreme ambient temperatures, g-forces, heat dissipation impairment), sensor dropouts and anomalies, and numerous physiological stressors imposed on the drivers (e.g., elevated heart rate, dehydration), our method was robust enough to be comparable to studies done in less complex environments. maneuvers, poor heat dissipation, regulated breathing, chest-wall constriction, and high cognitive workloads while being confined to their seating position for prolonged periods.