Respiratory Inductive Plethysmography Signals Research Articles

A plethysmography investigation comparing respiration rate before onset and after the end of central sleep apnoea episodes

The study explored possible changes in respiration rate immediately before onset and after end of central sleep apnoea (CSA) episodes using respiratory inductive plethysmography (RIP). RIP signals were recorded simultaneously from the chest and abdomen of 31 paediatric patients (mean age 7.12 years, standard deviation 4.40 years, 20 females, 11 males) attending a children’s hospital for overnight sleep disorder monitoring. The patients were also monitored for electrocardiogram, electroencephalogram, electrooculogram, electromyograph, CO2, body position, SpO2 and if they tolerated, their respiratory airflow using a thermistor (placed under the nose) and nasal prongs (placed in the nostrils). An experienced clinical physiologist scored the recorded data by indicating the time point of each respiratory pause, its duration and type (i.e., apnoea or hypopnoea; central or obstructive). The RIP signals from the chest and abdomen were summed and converted to a respiration rate signal by determining the time points they crossed the time axis. Events were scored using standard paediatric sleep and breathing scoring rules. Statistical analysis indicated significant differences between the skewness and kurtosis of respiratory rate values immediately before the onset and after the end of CSA episodes, however the mean respiration rate did not change significantly. This indicates possible changes in the pattern of respiration rate immediately after the end of CSA episodes. It may be possible to use these results to predict the onset of a CSA episode, however, to confirm the finding a larger study will be needed.

The effect of hyperoxia on ventilation during recovery from general anesthesia: A randomized pilot study for a parallel randomized controlled trial

Study objectiveWhile supplemental O2 inhalation corrects hypoxemia, its effect on post-anesthesia ventilation remains unknown. This pilot trial tested the hypothesis that hyperoxia increases the time spent with a transcutaneous PCO2 (TcPCO2) > 45 mmHg, compared with standard O2 supplementation. DesignSingle-blinded, parallel two-arm randomized pilot trial. SettingUniversity hospital. Patients20 patients undergoing robotic-assisted laparoscopic nephrectomy. MeasurementsAfter institutional approval and informed consent, patients were randomized to receive O2 titrated to arterial saturation (SpO2): 90–94% (Conservative O2, N =10), or to SpO2 > 96% (Liberal O2, N = 10) for up to 90 min after anesthesia. Continuous TcPCO2, respiratory inductance plethysmography (RIP), and SpO2, were recorded. We calculated the percentage of time at TcPCO2 > 45 mmHg for each patient and compared the two groups using analysis of covariance, adjusting for sex, age, and body mass index. We also estimated the sample size required to detect the between-group difference observed in this pilot trial. RIP signals were used to calculate apnea/hypopnea index (AHI), which was then compared between two groups. Main resultsThe mean percentage of time with a TcPCO2 > 45 mmHg was 80.6% for the Conservative O2 (N=9) and 61.2% for the Liberal O2 (N=10) group [between-group difference of 19.4% (95% CI: −18.7% to 57.6%), P = 0.140]. With an observed effect size of 0.73, we estimated that 30 participants per group are required, to demonstrate this difference with a power of 80% at a two-sided alpha of 5%. Means SpO2 were 94.5% and 99.9% for the Conservative O2 and the Liberal O2 groups, respectively. AHI was significantly higher in the Conservative O2, compared with the Liberal O2 group (median AHI: 16 vs. 3; P = 0.0014). ConclusionsHyperoxia in the post-anesthesia period reduced the time spent at TcPCO2 > 45 mmHg and significantly decreased AHI, while mean SpO2 ranged inside the a priori defined limits. Trial registrationClinicalTrials.gov identifier: NCT04723433

Deep learning to predict power output from respiratory inductive plethysmography data

AbstractPower output is one of the most accurate methods for measuring exercise intensity during outdoor endurance sports, since it records the actual effect of the work performed by the muscles over time. However, power meters are expensive and are limited to activity forms where it is possible to embed sensors in the propulsion system such as in cycling. We investigate using breathing to estimate power output during exercise, in order to create a portable method for tracking physical effort that is universally applicable in many activity forms. Breathing can be quantified through respiratory inductive plethysmography (RIP), which entails recording the movement of the rib cage and abdomen caused by breathing, and it enables us to have a portable, non‐invasive device for measuring breathing. RIP signals, heart rate and power output were recorded during a N‐of‐1 study of a person performing a set of workouts on a stationary bike. The recorded data were used to build predictive models through deep learning algorithms. A convolutional neural network (CNN) trained on features derived from RIP signals and heart rate obtained a mean absolute percentage error (MAPE) of 0.20 (ie, 20% average error). The model showed promising capability of estimating correct power levels and reactivity to changes in power output, but the accuracy is significantly lower than that of cycling power meters.

Stroke Volume Estimation from Respiratory Inductive Plethysmography: Double Empirical Decomposition

In this study, we have developed a “double-empirical mode decomposition algorithm” to estimate cardiac stroke volume from respiratory inductive plethysmography (RIP) signals. The algorithm consists of first an ensemble empirical mode decomposition (EEMD) to extract the cardiorespiratory components. Then, it is followed by an empirical mode decomposition (EMD) to extract only the cardiac components. This double approach permits (a) solving problems of mixing between cardiac and respiratory components (mode and scale mixing), (b) cardiogenic oscillations extraction in the respiratory inductive plethysmography signal, and (c) subsequent estimation of stroke volume. The algorithm is applied to simulated and real RIP signals. The simulated signals are generated by a cardiorespiratory model previously published by the authors. The real signals are measured via a developed inductive vest. In the real case, the values of estimated stroke volumes are compared to the values obtained by thoracocardiographic filter-based method. In the simulated case, the values are compared to the simulated cardiac activity. The results of comparison through Bland and Altman indicate an error lying in the range ±10%. In contrast to thoracocardiography, the proposed method consists of a promising tool for continuous noninvasive adaptive cardiac monitoring that does not need adjusting parameters or cut-off based on ECG. Also, in comparison to echocardiography and impedance-based methods, it does not necessitate the presence of an expert and is not too sensitive to current penetration.

Three-Stage Breathing Effort Quantification for Obstructive Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals

A level-IV home-based sleep apnea monitoring system that utilizes alternative sensors, such as dual respiratory inductance plethysmography (RIP) belts, is proposed to promote routine apnea monitoring. Notably, continued excursion may occur in RIP belt signals, owing to the imperfect relationship between thoracic and abdominal movements during obstructive events. Therefore, we propose a novel algorithm to detect obstructive apnea based on an obstructive reciprocal divergence (ORD) continued excursion model and to explore the possibility of a multistage breathing-effort evaluation model using only RIP signals. Using the developed approach, we detected obstructive sleep apnea with a high accuracy of 99.83 ± 0.71% and a slight reduction of 73.34 ± 28.35% in hypopnea performance with overall combined objective metrics of 89.38 ± 10.53%. We found that introducing many stages improves specificity (p $\text{R}^{2}=0.98$ ; p < 0.001). The proposed approach can assist sleep technologists in characterizing obstructive and nonobstructive apneic events. Moreover, ORD is competent for further quantitative and qualitative IFL analysis and will significantly benefit the automated IFL detection system studies.

Diagnosis of sleep apnea without sensors on the patient's face.

Thermistors, nasal cannulas, and respiratory inductance plethysmography (RIP) are the recommended reference sensors of the American Academy of Sleep Medicine (AASM) for the detection and characterization of apneas and hypopneas; however, these sensors are not well tolerated by patients and have poor scorability. We evaluated the performance of an alternative method using a combination of tracheal sounds (TSs) and RIP signals. Consecutive recordings of 70 adult patients from the Pays de la Loire Sleep Cohort were manually scored in random order using the AASM standard signals and the combination TS and RIP signals, without respiratory sensors placed on the patient's face. The TS-RIP scoring used the TS and RIP-flow signals for detection of apneas and hypopneas, respectively, and the suprasternal pressure and RIP belt signals for the characterization of apneas. Sensitivity and specificity of the TS-RIP combination were 96.21% and 91.34% for apnea detection and 89.94% and 93.25% for detecting hypopneas, respectively, with a kappa coefficient of 0.87. For the characterization of apneas, sensitivity and specificity were 98.67% and 96.17% for obstructive apneas, 92.66% and 99.36% for mixed apneas, and 96.14% and 98.89% for central apneas, respectively, with a kappa coefficient of 0.94. The TS-RIP scoring revealed a high agreement for classifying obstructive sleep apnea into severity classes (none, mild, moderate, and severe obstructive sleep apnea) with a Cohen's kappa coefficient of 0.96. Compared with the AASM reference sensors, the TS-RIP combination allows reliable noninvasive detection and characterization of respiratory events with a high degree of sensitivity and specificity. TS-RIP combination could be used for diagnosis of obstructive sleep apnea in adults, either as an alternative to the AASM sensors or in combination with the recommended AASM sensors.

Altered Causal Coupling Pathways within the Central-Autonomic-Network in Patients Suffering from Schizophrenia.

The multivariate analysis of coupling pathways within physiological (sub)systems focusing on identifying healthy and diseased conditions. In this study, we investigated a part of the central-autonomic-network (CAN) in 17 patients suffering from schizophrenia (SZO) compared to 17 age–gender matched healthy controls (CON) applying linear and nonlinear causal coupling approaches (normalized short time partial directed coherence, multivariate transfer entropy). Therefore, from all subjects continuous heart rate (successive beat-to-beat intervals, BBI), synchronized maximum successive systolic blood pressure amplitudes (SYS), synchronized calibrated respiratory inductive plethysmography signal (respiratory frequency, RESP), and the power PEEG of frontal EEG activity were investigated for 15 min under resting conditions. The CAN revealed a bidirectional coupling structure, with central driving towards blood pressure (SYS), and respiratory driving towards PEEG. The central-cardiac, central-vascular, and central-respiratory couplings are more dominated by linear regulatory mechanisms than nonlinear ones. The CAN showed significantly weaker nonlinear central-cardiovascular and central-cardiorespiratory coupling pathways, and significantly stronger linear central influence on the vascular system, and on the other hand significantly stronger linear respiratory and cardiac influences on central activity in SZO compared to CON, and thus, providing better understanding of the interrelationship of central and autonomic regulatory mechanisms in schizophrenia might be useful as a biomarker of this disease.

Measurement of respiratory effort in sleep by 3D camera and respiratory inductance plethysmography

Polysomnography systems used for the diagnosis of sleep respiratory disorders are comprised of multiple sensors, including abdomen and thorax respiratory inductance plethysmography (RIP) belts to record respiratory effort. However, RIP belts are known to be susceptible to signal loss. To resolve this, we utilized a contactless three-dimensional (3D) time-of-flight (TOF) camera to monitor respiratory effort. We aimed to show that respiratory effort monitoring can be achieved by 3D TOF camera recording instead of RIP belts. The use of RIP belt signals is twofold. Firstly, the signals are used to classify the apnea events into obstructive, central, and mixed. Additionally, the American Academy of Sleep Medicine (AASM) Scoring Manual recommends the scoring of apneas and hypopneas using RIPSum (the sum of the abdomen and thorax RIP signals) when the airflow sensors are unavailable. We therefore used the 3D effort signal to classify apneas and compared it to the RIP signal classification. Reduced effort events from RIP and 3D effort signals were compared to the apnea and hypopnea events. Furthermore, the changes in effort during the events were compared between the two effort signals. Classification by 3D effort signal performed well, with 80% accuracy. It worked best for central apneas, with an accuracy of 99%. There was a high correlation of r = 0.88 (r ≠ 0, p = 0.0001) between the 3D effort signal events and RIPSum events. There was also a significant correlation of 0.62 (r ≠ 0, p = 0.0001) between 3D effort signal and RIPSum in the decrease of effort during apnea and hypopnea events. We conclude that respiratory effort derived from a 3D TOF camera can be used as an alternative to RIP belts for scoring of apneas and hypopneas and classification of apneas.

Vision-Based Heart and Respiratory Rate Monitoring During Sleep - A Validation Study for the Population at Risk of Sleep Apnea.

A reliable, accessible, and non-intrusive method for tracking respiratory and heart rate is important for improving monitoring and detection of sleep apnea. In this study, an algorithm based on motion analysis of infrared video recordings was validated in 50 adults referred for clinical overnight polysomnography (PSG). The algorithm tracks the displacements of selected feature points on each sleeping participant and extracts respiratory rate using principal component analysis and heart rate using independent component analysis. For respiratory rate estimation (mean ± standard deviation), 89.89 % ± 10.95 % of the overnight estimation was accurate within 1 breath per minute compared to the PSG-derived respiratory rate from the respiratory inductive plethysmography signal, with an average root mean square error (RMSE) of 2.10 ± 1.64 breaths per minute. For heart rate estimation, 77.97 % ± 18.91 % of the overnight estimation was within 5 beats per minute of the heart rate derived from the pulse oximetry signal from PSG, with mean RMSE of 7.47 ± 4.79 beats per minute. No significant difference in estimation of RMSE of either signal was found according to differences in body position, sleep stage, or amount of the body covered by blankets. This vision-based method may prove suitable for overnight, non-contact monitoring of respiratory rate. However, at present, heart rate monitoring is less reliable and will require further work to improve accuracy.

Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition

Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal's amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects' RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm. Graphical abstract ᅟ.

New Respiratory Inductive Plethysmography (RIP) Method for Evaluating Ventilatory Adaptation during Mild Physical Activities.

The pneumotachometer is currently the most accepted device to measure tidal breathing, however, it requires the use of a mouthpiece and thus alteration of spontaneous ventilation is implied. Respiratory inductive plethysmography (RIP), which includes two belts, one thoracic and one abdominal, is able to determine spontaneous tidal breathing without the use of a facemask or mouthpiece, however, there are a number of as yet unresolved issues. In this study we aimed to describe and validate a new RIP method, relying on a combination of thoracic RIP and nasal pressure signals taking into account that exercise-induced body movements can easily contaminate RIP thoracic signals by generating tissue motion artifacts. A custom-made time domain algorithm that relies on the elimination of low amplitude artifacts was applied to the raw thoracic RIP signal. Determining this tidal ventilation allowed comparisons between the RIP signal and simultaneously-recorded airflow signals from a calibrated pneumotachometer (PT). We assessed 206 comparisons from 30 volunteers who were asked to breathe spontaneously at rest and during walking on the spot. Comparisons between RIP signals processed by our algorithm and PT showed highly significant correlations for tidal volume (Vt), inspiratory (Ti) and expiratory times (Te). Moreover, bias calculated using the Bland and Altman method were reasonably low for Vt and Ti (0.04 L and 0.02 s, respectively), and acceptable for Te (<0.1 s) and the intercept from regression relationships (0.01 L, 0.06 s, 0.17 s respectively). The Ti/Ttot and Vt/Ti ratios obtained with the two methods were also statistically correlated. We conclude that our methodology (filtering by our algorithm and calibrating with our calibration procedure) for thoracic RIP renders this technique sufficiently accurate to evaluate tidal ventilation variation at rest and during mild to moderate physical activity.

Body Mass Index-Dependent Ventilatory Parameters From Respiratory Inductive Plethysmography During 6-Minute Walk Test.

Walking is part of obesity management. Assessment of ventilatory impairments and consequences for gait induced by obesity could be clinically helpful. We aimed to develop a method to accurately monitor ventilation with respiratory inductive plethysmography (RIP) in subjects with high body mass indices (BMIs) during a 6-min walk test (6MWT). 25 volunteers were divided into 2 groups based on BMI (<25 or >30 kg/m2) and performed a 6MWT with a calibrated RIP. Ventilatory parameters (tidal volume [V(T)], inspiratory [T(I)] and expiratory [T(E)] times, V(T)/T(I) ratio, and T(I)/Ttot ratio) were determined after processing RIP signals with a custom-made algorithm designed to discriminate tissue motion artifacts and respiratory cycles in the time domain. Six-min walk distance and average speed by minute were collected. The number of artifacts removed by the algorithm used for artifact removal was higher for high-BMI subjects and was correlated to their individual values (r = 0.66, P < .001). Six-min walk distance was lower for the group with a higher BMI (P = .001). ANOVA revealed effects of exercise for V(T), T(I), and T(E) (P < .001) and also BMI effects in the course of the 6MWT for V(T), T(I), T(E), V(T)/T(I), and T(I)/Ttot (P < .001 for each of them). This respiratory monitoring method is sufficiently sensitive to point out differences between rest and exercise as well as locomotor and ventilatory differences relative to BMI during the 6MWT. Thus, this system gives useful information from the 6MWT for clinicians who want to assess respiratory patterns of patients during this commonly used test.

Hilbert-Huang Transform Yields Improved Minute Volume Estimates from Respiratory Inductance Plethysmography During Transitions to Paradoxical Breathing.

Anesthesia and sedation are associated with paradoxical breathing. Respiratory inductance plethysmography (RIP) permits measurement of respiratory motion in clinical settings not conducive to spirometry, but correlation of RIP volume changes and spirometer flow in the time domain is degraded by the development of paradoxical breathing. The Hilbert-Huang transform (HHT) is a nonlinear signal analysis method that permits the instantaneous magnitude and phase of nonstationary signals to be estimated in the frequency domain. We hypothesized that these frequency domain estimates would provide higher correlation between RIP and spirometer signals than time domain signals during the transition between normal and paradoxical breathing. From 51 patients undergoing sevoflurane anesthesia for minor procedures, a 5-minute epoch containing transitions between pressure support ventilation and spontaneous ventilation was selected for analysis. Pearson correlation for models based on HHT magnitude and phase was compared with models based on time domain signals. Bland-Altman analysis was performed to assess deviation from linearity in the models. For the 51 patients analyzed, the modulation of tidal volume over the epoch ranged from 30% to 215% of epoch mean. The coefficient of determination for time domain analysis was 0.62 ± 0.2 compared with 0.93 ± 0.07 for the HHT model incorporating phase. This improvement of 0.31 (99% confidence interval, 0.24-0.37) was significant (P < 0.0001). No trend was observed in prediction residuals. Under conditions of changing ventilation, HHT-derived magnitude and phase measures provide higher correlation with spirometry than those obtained with traditional time domain methods.

The Feasibility of Recording Speech Breathing Patterns before and after Pulmonary Rehabilitation in an Out-Patient Environment

This study aimed to determine the feasibility of recording speech breathing patterns before and after a six week Pulmonary Rehabilitation programme (PR) for patients with Chronic Obstructive Pulmonary Disease (COPD) and bronchiectasis. Fourteen patients with COPD (age 69 ± 9.64) and 6 patients with bronchiectasis (age 71 ± 6.15) were recruited from a respiratory outpatients department, and breathing patterns were recorded using Respiratory Inductive Plethysmography (RIP) during periods of quiet breathing, reading, counting and conversational speech, before and after a six week PR programme. Nine parameters were extracted from the recorded RIP signals: respiratory rate (bpm), inspiration and expiration time (s), inspiration and expiration magnitude (arbitrary units representing volume) and the regional percentage contributions of the ribcage and abdomen during inspiration and expiration. Clinical data, modified Borg scores, and Incremental Shuttle Walk Test (ISWT) data were retrieved from medical notes. Functional exercise capacity (ISWT) significantly improved following PR by 89 m for the group (t=-4.11, df=16, p=0.00). No clinically or statistically significant changes in breathlessness (Borg), or speech breathing parameters, were detected following PR during any of the speech tasks. This study has demonstrated the feasibility of collecting speech breathing pattern data outside laboratory conditions. No firm conclusions can be drawn from the findings because of the uncontrolled nature of the study. It is therefore too soon to know if speech breathing patterns will be a useful tool for the remote monitoring of respiratory health in future.

Thorax and Abdomen Motion Analysis in Patients with Obstructive Diseases

Objective: We evaluated changes in bronchoconstriction by a new approach based on respiratory inductive plethysmography (RIP) signal analysis. Methods: Thoracic and abdominal motions were recorded (5 min) by uncalibrated RIP in 44 adult subjects with a diagnosis of moderate bronchial obstruction (Obstructive group) and 50 healthy adult controls (Healthy group). In the Obstructive group, two series of measurements were performed before (Obstructive PRE) and after (Obstructive POST) a bronchodilation protocol. Airway resistance (Raw) and lung function data (forced vital capacity (FVC), forced expiratory volume in one second (FEV1 ) and FEV1 /FVC) were measured with a body plethysmograph. A breath-bybreath analysis was performed to calculate distances between normalized thorax and abdomen RIP signals and a mean distance (D) was calculated for each recording. Results: D and Raw were higher in the Obstructive group than in the Healthy group in both PRE and POST conditions. Both D and Raw significantly decreased after bronchodilation in the Obstructive group. D and Raw were also positively and significantly correlated in the Obstructive group in both PRE and POST conditions. Conclusion: D, as calculated from signals recorded by RIP, appears to be a useful non-invasive parameter for continuous monitoring of changes in bronchoconstriction.

Sleep stage classification based on respiratory signal.

One of the research tasks, which should be solved to develop a sleep monitor, is sleep stages classification. This paper presents an algorithm for wakefulness, rapid eye movement sleep (REM) and non-REM sleep detection based on a set of 33 features, extracted from respiratory inductive plethysmography signal, and bagging classifier. Furthermore, a few heuristics based on knowledge about normal sleep structure are suggested. We used the data from 29 subjects without sleep-related breathing disorders who underwent a PSG study at a sleep laboratory. Subjects were directed to the PSG study due to suspected sleep disorders. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. The accuracy of 77.85 ± 6.63 and Cohen's kappa of 0.59 ± 0.11 were achieved for the classifier. Using heuristics we increased the accuracy to 80.38 ± 8.32 and the kappa to 0.65 ± 0.13. We conclude that heuristics may improve the automated sleep structure detection based on the analysis of indirect information such as respiration signal and are useful for the development of home sleep monitoring system.

Scoring Tools for the Analysis of Infant Respiratory Inductive Plethysmography Signals.

Infants recovering from anesthesia are at risk of life threatening Postoperative Apnea (POA). POA events are rare, and so the study of POA requires the analysis of long cardiorespiratory records. Manual scoring is the preferred method of analysis for these data, but it is limited by low intra- and inter-scorer repeatability. Furthermore, recommended scoring rules do not provide a comprehensive description of the respiratory patterns. This work describes a set of manual scoring tools that address these limitations. These tools include: (i) a set of definitions and scoring rules for 6 mutually exclusive, unique patterns that fully characterize infant respiratory inductive plethysmography (RIP) signals; (ii) RIPScore, a graphical, manual scoring software to apply these rules to infant data; (iii) a library of data segments representing each of the 6 patterns; (iv) a fully automated, interactive formal training protocol to standardize the analysis and establish intra- and inter-scorer repeatability; and (v) a quality control method to monitor scorer ongoing performance over time. To evaluate these tools, three scorers from varied backgrounds were recruited and trained to reach a performance level similar to that of an expert. These scorers used RIPScore to analyze data from infants at risk of POA in two separate, independent instances. Scorers performed with high accuracy and consistency, analyzed data efficiently, had very good intra- and inter-scorer repeatability, and exhibited only minor confusion between patterns. These results indicate that our tools represent an excellent method for the analysis of respiratory patterns in long data records. Although the tools were developed for the study of POA, their use extends to any study of respiratory patterns using RIP (e.g., sleep apnea, extubation readiness). Moreover, by establishing and monitoring scorer repeatability, our tools enable the analysis of large data sets by multiple scorers, which is essential for longitudinal and multicenter studies.

Respiratory inductance plethysmography calibration for pediatric upper airway obstruction: an animal model.

BackgroundTo determine optimal methods of Respiratory Inductance Plethysmography (RIP) flow calibration for application to pediatric post-extubation upper airway obstruction.MethodsWe measured RIP, spirometry, and esophageal manometry in spontaneously breathing, intubated Rhesus monkeys with increasing inspiratory resistance. RIP calibration was based on: ΔµVao ≈ M[ΔµVRC + K(ΔµVAB)] where K establishes the relationship between the uncalibrated rib cage (ΔµVRC) and abdominal (ΔµVAB) RIP signals. We calculated K during: (1) isovolume maneuvers during a negative inspiratory force (NIF) (2) Quantitative Diagnostic Calibration (QDC) during (a) tidal breathing, (b) continuous positive airway pressure (CPAP), and (c) increasing degrees of UAO. We compared the calibrated RIP flow waveform to spirometry quantitatively and qualitatively.ResultsIsovolume calibrated RIP flow tracings were more accurate (against spirometry) both quantitatively and qualitatively than those from QDC (p<0.0001), with bigger differences as UAO worsened. Isovolume calibration yielded nearly identical clinical interpretation of inspiratory flow limitation as spirometry.ConclusionsIn an animal model of pediatric UAO, Isovolume calibrated RIP flow tracings are accurate against spirometry. QDC during tidal breathing yields poor RIP flow calibration, particularly as UAO worsens. Routine use of a NIF maneuver before extubation affords the opportunity to use RIP to study post extubation UAO in children.

Sleep parameter estimation from portable data

Introduction The advent of small and portable devices, with high storage and processing capabilities have allowed physiological data to be acquired outside clinical environments in a reliable way, often across several days. These sources of data include ECG, Respiratory Inductance Plethysmography (RIP), oxygen saturation, Actigraphy (ACT), among others. Due to the highly constrained conditions in which the Polysomnography (PSG), the golden standard for sleep disorders diagnosis, is performed, alternative methods are desired. These small and portable devices may provide an alternative for long term monitoring, thus complementing the information provided by the PSG. In this work, we present an algorithm to estimate the Sleep Efficiency (SE), REM and Non-REM sleep percentages from multi-modal data that can easily be acquired from portable devices, combining information from ECG, RIP and ACT. Materials and methods The data was acquired from fifteen healthy volunteers who performed a standard PSG+ Actigraphy. The average SE, REM and NREM percentages, computed from the hypnograms, are 87.2%, 18.87% and 81.13% respectively. An extended feature set was extracted from the RR, RIP and ACT signals. The extracted features were used to train a two Parzen classifiers with a rejection option. The computation of the 3 sleep parameters takes into account the result of each classifier and a final regularization step, which takes includes the a priori training information regarding the performance of the classifier. Results The described method resulted in an estimated average SE of 87.8% corresponding to an average estimation error of 5.62%. The estimated REM and NREM percentages were 18.91% and 81.9%, corresponding to an average estimation error of 8.22% and 5.95% respectively. Conclusion Reliable sleep parameters estimation is typically limited to PSG data, where EEG plays a fundamental role in the determination of the sleep state. The obtained results are encouraging since they were obtained with limited data yielding low estimation errors. The use of classifiers with rejection capability ensures that only non-ambiguous data is used in the estimation process. Acknowledgements This work was supported by FCT (ISR/IST plurianual funding) through the PIDDAC Program funds and FCT project ”Detection of Brain Micro-states in Fibromyalgia” (PTDC/SAU-BEB/104948/2008).

Detecting variations of blood volume shift due to heart beat from respiratory inductive plethysmography measurements in man

The simultaneous study of the cardiac and respiratory activities and their interactions is of great physiological and clinical interest. For this purpose, we want to investigate if respiratory inductive plethysmography (RIP) can be used for cardiac functional exploration. We propose a system, based on RIP technology and time-scale approaches of signal processing, for the extraction of cardiac information. This study focuses on the monitoring of blood volume shift due to heart beat, noted ▵Vtr_c and investigates RIP for the detection of ▵Vtr_c variations by comparison to stroke volume (SV) variations estimated by impedance cardiography (IMP). We proposed a specific respiratory protocol assumed to induce significant variations of the SV. Fifteen healthy volunteers in the seated and supine positions were asked to alternate rest respiration and maneuvers, consisting in blowing into a manometer. A multi-step treatment including a variant of empirical mode decomposition was applied on RIP signals to extract cardiac volume signals and estimate beat-to-beat ▵Vtr_c. These were averaged in quasi-stationary states at rest and during the respiratory maneuvers, and analysed in view of SV estimations from IMP signals simultaneously acquired. Correlation and statistical tests over the data show that RIP can be used to detect variations of the cardiac blood shift in healthy young subjects.