Scope of linear estimators of tidal and occluded volumes using thoracoabdominal indications of breathing movement coordination

A flow-leak correction algorithm for pneumotachographic work-of-breathing measurement during high-flow nasal cannula oxygen therapy

Measuring work of breathing (WOB) is an intricate task during high-flow nasal cannula (HFNC) therapy because the continuous unidirectional flow toward the patient makes pneumotachography technically difficult to use. We implemented a new method for measuring WOB based on a differential pneumotachography (DP) system, equipped with one pneumotachograph inserted in the HFNC circuit and another connected to a monitoring facemask, combined with a leak correction algorithm (LCA) that corrects flow measurement errors arising from leakage around the monitoring facemask. To test this system, we used a mechanical lung model that provided data to compare LCA-corrected respiratory flow, volume and time values with effective values obtained with a third pneumotachograph used instead of the LCA to measure mask flow leaks directly. Effective and corrected volume and time data showed high agreement (Bland-Altman plots) even at the highest leak. Studies on two healthy adult volunteers confirmed that corrected respiratory flow combined with esophageal pressure measurements can accurately determine WOB (relative error < 1%). We conclude that during HFNC therapy, a DP system combined with a facemask and an algorithm that corrects errors due to flow leakages allows pneumotachography to measure reliably the respiratory flow and volume data needed for calculating WOB.

Nonlinear model for estimating respiratory volume based on thoracoabdominal breathing movements

BACKGROUND AND OBJECTIVE

Respiratory inductive plethysmography is a non-invasive technique for measuring respiratory function. However, there are challenges associated with using linear methods for calibration of respiratory inductive plethysmography. In this study, we developed two nonlinear models, artificial neural network and adaptive neuro-fuzzy inference system, to estimate respiratory volume based on thoracoabdominal movements, and compared these models with routine linear approaches, including qualitative diagnostic calibration and multiple linear regression.

METHODS

Recordings of spirometry volume and respiratory inductive plethysmography were obtained for 10 normal subjects and 10 asthmatic patients, during asynchronous breathing for 7 min. The first 5 min of recording were used to develop the models; the remaining data were used for subsequent validation of the results.

RESULTS

The results from the nonlinear models fitted the spirometry volume curve significantly better than those obtained by linear methods, particularly during asynchrony (P < 0.05). On a breath-by-breath analysis, estimates of tidal volume, total cycle time and sigh values using the artificial neural network model were accurate by comparison with qualitative diagnostic calibration. In contrast to the artificial neural network model, there was a significant correlation between values for thoracoabdominal asynchrony and increased error of qualitative diagnostic calibration (P < 0.05).

CONCLUSIONS

These results indicate that the nonlinear methods can be adapted to closely simulate variable conditions and used to study the patterns of volume changes during normal and asynchronous breathing.

Correlation between perceived asthma control and thoraco-abdominal asynchrony in primary care patients diagnosed with asthma

OBJECTIVE

Thoraco-abdominal asynchrony (TAA), the discordant movement of the abdomen and thorax, may impact upon health-related variables. Here, we investigated the extent to which TAA is associated with health-related variables, particularly perceived asthma control and quality of life.

METHODS

Ambulatory respiratory data from 43 patients diagnosed with asthma and 43 healthy age and sex-matched controls were recorded over 4 hours. Phase relation (Ph Rel Total), the percentage of time that the effects of rib cage (RC) and diaphragmatic movement result in opposite effects on intra-thoracic volume, quantified TAA. Subjects completed the Mini Asthma Quality of Life Questionnaire (AQLQ), Asthma Control Questionnaire (ACQ), Nijmegen questionnaire (NQ), Hospital Anxiety and Depression Scale (HADS), Spielberger State-Trait Anxiety Inventory (STAI), and General Health Perception (GHP) subscale of the short form 36 questionnaire'. Capnography profiling, breath-hold time (BHT), and standard spirometry were performed.

RESULTS

The time in asynchrony was significantly greater in the asthma than in the healthy control group (Ph Rel Total = 14% (interquartile range (IQR) 8.5-20.7%) versus 10.4% (IQR 7.1-14.5%), p = .012). In patients with asthma, Ph Rel Total was weakly associated with poorer ACQ scores (r = 0.33, p = .03), and in the healthy control group with GHP (r = 0.319, p = .037). Post-hoc exploratory analysis revealed a moderate relationship in the female asthma subgroup between Ph Rel Total and AQLQ (r = -0.56, p = .003).

CONCLUSIONS

TAA may be associated with decreased perceived asthma control. In healthy individuals, asynchrony may be associated with low perception of general health. Further studies are required to investigate if the reduction of TAA improves these health-related variables.

Nasal airflow and thoracoabdominal motion in children using infrared thermographic video processing

The assessment of apnea and asynchronous breathing requires the application of a facemask connected to a pneumotachograph and inductive transducer bands placed around the chest wall. These contact devices may alter the breathing pattern and are difficult to implement, especially in infants and children. This study validates a contactless image-processing system that simultaneously retrieves breath-related thermal variations from nasal, ribcage, and abdomen regions of interest (ROI) from infrared thermographic video recordings of children. Thermographic videos were obtained in 17 supine, spontaneously breathing unsedated children (0.33-13.75 years), including 8 patients with respiratory pathology. Representative thermographic signals were obtained from each ROI on a frame-by-frame basis. Cronbach's Alpha reliability coefficient assessed the correlation between control nasal pressure period, the visually scored respiratory rate and the fundamental period in the frequency domain of thermographic signals. A cross-correlation function calculated the time delay and the phase angle between ribcage and abdomen variability. A Cronbach's Alpha value of 0.976 (0.992-0.944 95% CI) suggests a small-scale measurement error between thermographic and control periods. The ribcage-abdomen time delay in children with respiratory disease (-0.42 ± 0.707 sec) significantly differed from healthy children (0.22 ± 0.426 sec, P = 0.0125). This novel system reliably acquired time-aligned nasal airflow and thoracoabdominal motion estimates without relying on attached sensor performance and detected asynchronous breathing in pediatric patients.

Weaning From Mechanical Ventilation: A Retrospective Analysis Leading to a Multimodal Perspective

Practitioners' decision for mechanical aid discontinuation is a challenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Recently, an increasing interest on respiratory pattern variability as an extubation readiness indicator has appeared. Reliable assessment of this variability involves a set of signal processing and pattern recognition techniques. This paper presents a suitability analysis of different methods used for breathing pattern complexity assessment. The contribution of this analysis is threefold: 1) to serve as a review of the state of the art on the so-called weaning problem from a signal processing point of view; 2) to provide insight into the applied processing techniques and how they fit into the problem; 3) to propose additional methods and further processing in order to improve breathing pattern regularity assessment and weaning readiness decision. Results on experimental data show that sample entropy outperforms other complexity assessment methods and that multidimensional classification does improve weaning prediction. However, the obtained performance may be objectionable for real clinical practice, a fact that paves the way for a multimodal signal processing framework, including additional high-quality signals and more reliable statistical methods.