Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics.

BACKGROUND: For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. METHODS AND RESULTS: Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH2O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH2O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). CONCLUSIONS: Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.

Surrogate modelling of heartbeat events for improved J-peak detection in BCG using deep learning.

Sleep, or the lack thereof, has far-reaching consequences on many aspects of human physiology, cognitive performance, and emotional wellbeing. To ensure undisturbed sleep monitoring, unobtrusive measurements such as ballistocardiogram (BCG) are essential for sustained, real-world data acquisition. Current analysis of BCG data during sleep remains challenging, mainly due to low signal-to-noise ratio, physical movements, as well as high inter- and intra-individual variability. To overcome these challenges, this work proposes a novel approach to improve J-peak extraction from BCG measurements using a supervised deep learning setup. The proposed method consists of the modeling of the discrete reference heartbeat events with a symmetric and continuous kernel-function, referred to as surrogate signal. Deep learning models approximate this surrogate signal from which the target heartbeats are detected. The proposed method with various surrogate signals is compared and evaluated with state-of-the-art methods from both signal processing and machine learning approaches. The BCG dataset was collected over 17 nights using inertial measurement units (IMUs) embedded in a mattress, together with an ECG for reference heartbeats, for a total of 134 h. Moreover, we apply for the first time an evaluation metric specialized for the comparison of event-based time series to assess the quality of heartbeat detection. The results show that the proposed approach demonstrates superior accuracy in heartbeat estimation compared to existing approaches, with an MAE (mean absolute error) of 1.1 s in 64-s windows and 1.38 s in 8-s windows. Furthermore, it is shown that our novel approach outperforms current methods in detecting the location of heartbeats across various evaluation metrics. To the best of our knowledge, this is the first approach to encode temporal events using kernels and the first systematic comparison of various event encodings for event detection using a regression-based sequence-to-sequence model.

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

Iterative integral parameter identification of a respiratory mechanics model.

BACKGROUND: Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual's model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. METHODS: An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS) patients. RESULTS: The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. CONCLUSION: These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.

Physiological relevance and performance of a minimal lung model: an experimental study in healthy and acute respiratory distress syndrome model piglets.

BACKGROUND: Mechanical ventilation (MV) is the primary form of support for acute respiratory distress syndrome (ARDS) patients. However, intra- and inter- patient-variability reduce the efficacy of general protocols. Model-based approaches to guide MV can be patient-specific. A physiological relevant minimal model and its patient-specific performance are tested to see if it meets this objective above. METHODS: Healthy anesthetized piglets weighing 24.0 kg [IQR: 21.0-29.6] underwent a step-wise PEEP increase manoeuvre from 5cmH2O to 20cmH2O. They were ventilated under volume control using Engström Care Station (Datex, General Electric, Finland), with pressure, flow and volume profiles recorded. ARDS was then induced using oleic acid. The data were analyzed with a Minimal Model that identifies patient-specific mean threshold opening and closing pressure (TOP and TCP), and standard deviation (SD) of these TOP and TCP distributions. The trial and use of data were approved by the Ethics Committee of the Medical Faculty of the University of Liege, Belgium. RESULTS AND DISCUSSIONS: 3 of the 9 healthy piglets developed ARDS, and these data sets were included in this study. Model fitting error during inflation and deflation, in healthy or ARDS state is less than 5.0% across all subjects, indicating that the model captures the fundamental lung mechanics during PEEP increase. Mean TOP was 42.4cmH2O [IQR: 38.2-44.6] at PEEP = 5cmH2O and decreased with PEEP to 25.0cmH2O [IQR: 21.5-27.1] at PEEP = 20cmH2O. In contrast, TCP sees a reverse trend, increasing from 10.2cmH2O [IQR: 9.0-10.4] to 19.5cmH2O [IQR: 19.0-19.7]. Mean TOP increased from average 21.2-37.4cmH2O to 30.4-55.2cmH2O between healthy and ARDS subjects, reflecting the higher pressure required to recruit collapsed alveoli. Mean TCP was effectively unchanged. CONCLUSION: The minimal model is capable of capturing physiologically relevant TOP, TCP and SD of both healthy and ARDS lungs. The model is able to track disease progression and the response to treatment.

Validation of a novel system to assess end-expiratory lung volume and alveolar recruitment in an ARDS model.

BACKGROUND: Personalizing mechanical ventilation requires the development of reliable bedside monitoring techniques. The multiple-breaths nitrogen washin-washout (MBNW) technique is currently available to measure end-expiratory lung volume (EELVMBNW), but the precision of the technique may be poor, with percentage errors ranging from 28 to 57%. The primary aim of the study was to evaluate the reliability of a novel MBNW bedside system using fast mainstream sensors to assess EELV in an experimental acute respiratory distress syndrome (ARDS) model, using computed tomography (CT) as the gold standard. The secondary aims of the study were: (1) to evaluate trending ability of the novel system to assess EELV; (2) to evaluate the reliability of estimated alveolar recruitment induced by positive end-expiratory pressure (PEEP) changes computed from EELVMBNW, using CT as the gold standard. RESULTS: Seven pigs were studied in 6 experimental conditions: at baseline, after experimental ARDS and during a decremental PEEP trial at PEEP 16, 12, 6 and 2 cmH2O. EELV was computed at each PEEP step by both the MBNW technique (EELVMBNW) and CT (EELVCT). Repeatability was assessed by performing replicate measurements. Alveolar recruitment between two consecutive PEEP levels after lung injury was measured with CT (VrecCT), and computed from EELV measurements (VrecMBNW) as ΔEELV minus the product of ΔPEEP by static compliance. EELVMBNW and EELVCT were significantly correlated (R2 = 0.97). An acceptable non-constant bias between methods was identified, slightly decreasing toward more negative values as EELV increased. The conversion equation between EELVMBNW and EELVCT was: EELVMBNW = 0.92 × EELVCT + 36. The 95% prediction interval of the bias amounted to ± 86 mL and the percentage error between both methods amounted to 13.7%. The median least significant change between repeated measurements amounted to 8% [CI95%: 4-10%]. EELVMBNW adequately tracked EELVCT changes over time (concordance rate amounting to 100% [CI95%: 87%-100%] and angular bias amounting to - 2° ± 10°). VrecMBNW and VrecCT were significantly correlated (R2 = 0.92). A non-constant bias between methods was identified, slightly increasing toward more positive values as Vrec increased. CONCLUSIONS: We report a new bedside MBNW technique that reliably assesses EELV in an experimental ARDS model with high precision and excellent trending ability.

Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: a case-study of pulmonary mechanics.

Accurate model parameter identification relies on accurate forward model simulations to guide convergence. However, some forward simulation methodologies lack the precision required to properly define the local objective surface and can cause failed parameter identification. The role of objective surface smoothness in identification of a pulmonary mechanics model was assessed using forward simulation from a novel error-stepping method and a proprietary Runge-Kutta method. The objective surfaces were compared via the identified parameter discrepancy generated in a Monte Carlo simulation and the local smoothness of the objective surfaces they generate. The error-stepping method generated significantly smoother error surfaces in each of the cases tested (p<0.0001) and more accurate model parameter estimates than the Runge-Kutta method in three of the four cases tested (p<0.0001) despite a 75% reduction in computational cost. Of note, parameter discrepancy in most cases was limited to a particular oblique plane, indicating a non-intuitive multi-parameter trade-off was occurring. The error-stepping method consistently improved or equalled the outcomes of the Runge-Kutta time-integration method for forward simulations of the pulmonary mechanics model. This study indicates that accurate parameter identification relies on accurate definition of the local objective function, and that parameter trade-off can occur on oblique planes resulting prematurely halted parameter convergence.

When the value of gold is zero.

This manuscript presents the concerns around the increasingly common problem of not having readily available or useful "gold standard" measurements. This issue is particularly important in critical care where many measurements used in decision making are surrogates of what we would truly wish to use. However, the question is broad, important and applicable in many other areas.In particular, a gold standard measurement often exists, but is not clinically (or ethically in some cases) feasible. The question is how does one even begin to develop new measurements or surrogates if one has no gold standard to compare with?We raise this issue concisely with a specific example from mechanical ventilation, a core bread and butter therapy in critical care that is also a leading cause of length of stay and cost of care. Our proposed solution centers around a hierarchical validation approach that we believe would ameliorate ethics issues around radiation exposure that make current gold standard measures clinically infeasible, and thus provide a pathway to create a (new) gold standard.

Hierarchical individualization of a recruitment model with a viscoelastic component for ARDS patients.

Patient-specific mathematical models of respiratory mechanics enable substantial insight into patient state and pulmonary dynamics that are not directly measurable. Thus they offer potential e.g. to predict the outcome of ventilator settings for Acute Respiratory Distress Syndrome (ARDS) patients. In this work, an existing static recruitment model is extended by viscoelastic components allowing model simulations in various ventilation scenarios. A hierarchical approach is used to identify the model with measured data of 12 ARDS patients under static and dynamic conditions. Identified parameter values were physiologically plausible and reproduced the measured pressure responses with a median Coefficient of Determination (CD) of 0.972 in the dynamic and 0.992 in the static maneuver. Overall, the model presented incorporates physiological mechanisms, captures ARDS dynamics and viscoelastic tissue properties and is valid under various ventilation patterns.

Structural identifiability and practical applicability of an alveolar recruitment model for ARDS patients.

Patient-specific mathematical models of respiratory mechanics can offer substantial insight into patient state and pulmonary dynamics that are not directly measurable. Thus, they offer significant potential to evaluate and guide patient-specific lung protective ventilator strategies for acute respiratory distress syndrome (ARDS) patients. To assure bedside applicability, the model must be computationally efficient and identifiable from the limited available data, while also capturing dominant dynamics and trends observed in ARDS patients. In this study, an existing static recruitment model is enhanced by considering alveolar distension and implemented in a novel time-continuous dynamic respiratory mechanics model. The model was tested for structural identifiability and a hierarchical gradient descent approach was used to fit the model to low-flow test responses of 12 ARDS patients. Finally, a comprehensive practical identifiability analysis was performed to evaluate the impact of data quality on the model parameters. Identified parameter values were physiologically plausible and very accurately reproduced the measured pressure responses. Structural identifiability of the model was proven, but practical identifiability analysis of the results showed a lack of convexity on the error surface indicating that successful parameter identification is currently not assured in all test sets. Overall, the model presented is physiologically and clinically relevant, captures ARDS dynamics, and uses clinically descriptive parameters. The patient-specific models show the ability to capture pulmonary dynamics directly relevant to patient condition and clinical guidance. These characteristics currently cannot be directly measured or established without such a validated model.

Inverse modeling supports quantification of pressure and time depending effects in ARDS patients.

The application of respiratory mechanics models combined with standardized ventilation maneuvers enable investigations of patients' lung mechanics at the bedside in order to optimize ventilation therapy. Therefore, the underlying dynamic effects of respiratory mechanics (viscoelasticity, inhomogeneity and recruitment) are uncovered by applying various ventilation maneuvers and subsequently captured by the corresponding model via parameter identification methods. Data sets of patients undergoing quasi-static and dynamic ventilation patterns are available along with a hierarchical model structure for parameter identification and simulation purposes. The applicability of the basic 1(st) order model (FOM) of respiratory mechanics for various flow rates proved to be critical and patient dependent, since distinctive time-depending effects could not be considered. To improve this, a 2(nd) order model (SOM), individualized using data of a SCASS maneuver (Static Compliance Automated Single Step), enables successful simulations of respiratory mechanics in dynamic and quasi-static conditions. Pressure dependent effects such as static recruitment, can be captured by Hickling's nonlinear compliance model. This research illustrates the applicability of various models of respiratory mechanics within the model hierarchy in various circumstances and the ability to distinguish between dynamic and static effects.