High flow nasal cannula treatment for obstructive sleep apnea in infants and young children.

BACKGROUND: Continuous positive airway pressure (CPAP) is the nonsurgical treatment of choice for children with obstructive sleep apnea (OSA). However, CPAP limitations include difficulty with adherence and midface hypoplasia risk. We, therefore, sought to assess the effect of warm humidified air delivered via open nasal cannula (HFNC) on OSA in children in the sleep laboratory and at home. METHODS: A retrospective review was performed among children recommended treatment of OSA with HFNC. Reasons for HFNC recommendation included poor surgical candidacy, residual OSA following surgery, and CPAP intolerance. Children underwent both diagnostic and HFNC titration sleep studies and were prescribed HFNC for home use. Standard sleep architecture, arousals, and apnea-hypopnea indices (AHI) were assessed with the evaluation of reported adherence and complications over 12 months of treatment. RESULTS: Twenty-two children (average 12.8 months, 95% confidence interval [95% CI: 7.0, 18.6]) with OSA (obstructive AHI [OAHI] range: 4.8-89.2 events/h) underwent HFNC titration with significant reduction in OAHI (28.9 events/h [17.6, 40.2] vs 2.6 [1.1, 4.0]; P < .001) (mean [95% CI]). Nineteen patients received home HFNC treatment. By 12 months, four patients were lost to follow-up and OSA resolved in three patients (16%). Of 12 remaining patients, 7 (58%) continued therapy while 5 (42%) discontinued due to intolerance. The most common treatment complication was cannula dislodgement. Additional complications included skin irritation, dry mucus membranes, restlessness, oxygen desaturation, and increased central apneas. CONCLUSION: HFNC offers a treatment alternative to CPAP in infants and young children with OSA and was well tolerated at home in our study.

Image-based classification of bladder state using electrical impedance tomography.

OBJECTIVE: In this study, we examine the potential of using machine learning classification to determine the bladder state ('not full', 'full') with electrical impedance tomography (EIT) images of the pelvic region. Accurate classification of these states would enable urinary incontinence (UI) monitoring to alert the patient, before involuntary voiding occurs, in a low-cost and discrete manner. APPROACH: Using both numerical and experimental data, we form datasets that contain diverse observations with varying clinical parameters such as bladder volume, urine conductivity, and the reference used for time-difference imaging. We then classify the bladder state using both pixel-wise and feature extraction-based classification techniques. We employ principal component analysis, wavelets, and image segmentation to help create features. MAIN RESULTS: The performance was compared across several classifier algorithms. The minimum accuracy was 77.50%. The highest accuracy observed was 100%, and was found by combining principal component analysis and the Gaussian radial based function kernel support vector machine. This combination also offered the best trade-off between classification performance and the costs of training time and memory space. The biggest challenge in bladder state classification is classifying volumes near the separation volume of not full and full, in which choosing the most suitable classifier combination can minimize this error. SIGNIFICANCE: We performed the first machine learning classification of bladder EIT images, achieving high classification accuracies with both numerical and experimental data. This work highlights the potential of using image-based machine learning with an EIT device to support bladder monitoring for those suffering from UI.

Web-Based Interfaces for Virtual C. elegans Neuron Model Definition, Network Configuration, Behavioral Experiment Definition and Experiment Results Visualization.

The Si elegans platform targets the complete virtualization of the nematode Caenorhabditis elegans, and its environment. This paper presents a suite of unified web-based Graphical User Interfaces (GUIs) as the main user interaction point, and discusses their underlying technologies and methods. The user-friendly features of this tool suite enable users to graphically create neuron and network models, and behavioral experiments, without requiring knowledge of domain-specific computer-science tools. The framework furthermore allows the graphical visualization of all simulation results using a worm locomotion and neural activity viewer. Models, experiment definitions and results can be exported in a machine-readable format, thereby facilitating reproducible and cross-platform execution of in silico C. elegans experiments in other simulation environments. This is made possible by a novel XML-based behavioral experiment definition encoding format, a NeuroML XML-based model generation and network configuration description language, and their associated GUIs. User survey data confirms the platform usability and functionality, and provides insights into future directions for web-based simulation GUIs of C. elegans and other living organisms. The tool suite is available online to the scientific community and its source code has been made available.

Accuracy assessment of a novel image-free handheld robot for Total Knee Arthroplasty in a cadaveric study.

Surgical navigation has been shown to improve the accuracy of bone preparation and limb alignment in total knee arthroplasty (TKA). Previous work has shown the effectiveness of various types of navigation systems. Here, for the first time, we assessed the accuracy of a novel imageless semiautonomous handheld robotic sculpting system in performing bone resection and preparation in TKA using cadaveric specimens. In this study, we compared the planned and final implant placement in 18 cadaveric specimens undergoing TKA using the new tool. Eight surgeons carried out the procedures using three types of implant designs. A quantitative analysis was performed to determine the translational, angular, and rotational differences between the planned and achieved positions of the implants. The mean femoral flexion, varus/valgus, and rotational error was -2.0°, -0.1°, and -0.5°, respectively. The mean tibial posterior slope, and varus/valgus error was -0.2°, and -0.2°, respectively. We obtained higher flexion errors for the femoral implant when using cut-guides as compared to using a bur for cutting the bones. The image-free robotic sculpting tool achieved accurate implementation of the surgical plan with small errors in implant placement. Future studies will focus on determining how well the accurate implant placement translates into a clinical and functional benefit for the patient.

Gamma Band Neural Stimulation in Humans and the Promise of a New Modality to Prevent and Treat Alzheimer's Disease.

Existing treatments for Alzheimer's disease (AD) have questionable efficacy with a need for research into new and more effective therapies to both treat and possibly prevent the condition. This review examines a novel therapeutic modality that shows promise for treating AD based on modulating neuronal activity in the gamma frequency band through external brain stimulation. The gamma frequency band is roughly defined as being between 30 Hz-100 Hz, with the 40 Hz point being of particular significance. The epidemiology, diagnostics, existing pathological models, and related current treatment targets are initially briefly reviewed. Next, the concept of external simulation triggering brain activity in the gamma band with potential demonstration of benefit in AD is introduced with reference to a recent important study using a mouse model of the disease. The review then presents a selection of relevant studies that describe the neurophysiology involved in brain stimulation by external sources, followed by studies involving application of the modality to clinical scenarios. A table summarizing the results of clinical studies applied to AD patients is also reported and may aid future development of the modality. The use of a therapy based on modulation of gamma neuronal activity represents a novel non-invasive, non-pharmacological approach to AD. Although use in clinical scenarios is still a relatively recent area of research, the technique shows good signs of efficacy and may represent an important option for treating AD in the future.

Supervised Learning Classifiers for Electrical Impedance-based Bladder State Detection.

Urinary Incontinence affects over 200 million people worldwide, severely impacting the quality of life of individuals. Bladder state detection technology has the potential to improve the lives of people with urinary incontinence by alerting the user before voiding occurs. To this end, the objective of this study is to investigate the feasibility of using supervised machine learning classifiers to determine the bladder state of 'full' or 'not full' from electrical impedance measurements. Electrical impedance data was obtained from computational models and a realistic experimental pelvic phantom. Multiple datasets with increasing complexity were formed for varying noise levels in simulation. 10-Fold testing was performed on each dataset to classify 'full' and 'not full' bladder states, including phantom measurement data. Support vector machines and k-Nearest-Neighbours classifiers were compared in terms of accuracy, sensitivity, and specificity. The minimum and maximum accuracies across all datasets were 73.16% and 100%, respectively. Factors that contributed most to misclassification were the noise level and bladder volumes near the threshold of 'full' or 'not full'. This paper represents the first study to use machine learning for bladder state detection with electrical impedance measurements. The results show promise for impedance-based bladder state detection to support those living with urinary incontinence.

Symmetry difference electrical impedance tomography-a novel modality for anomaly detection.

OBJECTIVE: The theoretical basis, experimental implementation, and proof of concept of a novel electrical impedance tomography (EIT) imaging technique, called symmetry difference EIT, is described. This technique is applicable in situations where there is inherent symmetry in the region being imaged. METHODS: The sample scenario of the human head is used to describe the technique. The head is largely symmetrical across the sagittal plane. A unilateral lesion such as a haemorrhage or region of ischaemia distorts that symmetry. This distortion may be visualised using EIT. Measurement sets from a ring of electrodes placed on the boundary in both clockwise and counter-clockwise orientations are compared to detect the anomaly. Computer simulations featuring a hemispherical model of the head and brain are used initially to demonstrate the theory. Then, a more complex numerical model with anatomically accurate finite element models (FEMs) is used to expand on the concept with a more realistic scenario. Finally, tank experiments are performed with phantom lesions to validate the technique in the real world. RESULTS: Deviations from normal symmetry, due to the presence of lesions, are detectable using this new modality. The ease of detection improves with larger lesions and those far from the plane of symmetry. Quantitative metrics, as well as an image, help to robustly detect and identify both the presence of an abnormality and the cause (haemorrhagic or ischaemic lesion in the scenarios tested) or indeed indicate where no detection is possible. CONCLUSION: Symmetry difference EIT is a valuable new modality that is applicable to cases where the 'normal' features symmetry across a plane. Significantly, a change in the region of interest is not required and hence this technique may be suited to static or quasi-static cases where time difference EIT cannot be used.

A realistic pelvic phantom for electrical impedance measurement.

OBJECTIVE: To design and fabricate an anatomically and conductively accurate phantom for electrical impedance studies of non-invasive bladder volume monitoring. APPROACH: A modular pelvic phantom was designed and fabricated, consisting of a mechanically and conductively stable boundary wall, a background medium, and bladder phantoms. The wall and bladders are made of conductive polyurethane. The background material is an ultrasound gel-based mixture, with conductivity matched to a weighted average of the pelvic cavity organs, bone, muscle and fat. The phantom boundary is developed using a computer tomography model of a male human pelvis. The bladder phantoms were designed to correlate with human bladder dimensions. Electrical impedance measurements of the phantom were recorded, and images produced using six different bladder phantoms and a realistic finite element model. MAIN RESULTS: Five different bladder volumes were successfully imaged using an empty bladder as a reference. The average conductivity index from the reconstructed images showed a strong positive correlation with the bladder phantom volumes. SIGNIFICANCE: A conductively and anatomically accurate pelvic phantom was developed for non-invasive bladder volume monitoring using electrical impedance measurements. Several bladders were designed to correlate with actual human bladder volumes, allowing for accurate volume estimation. The conductivity of the phantom is accurate over 50-250 kHz. This phantom can allow changeable electrode location, contact and size; multi-layer electrodes configurations; increased complexity by addition of other organ or bone phantoms; and electrode movement and deformation. Overall, the pelvic phantom enables greater scope for experimentation and system refinement as a precursor to in-man clinical studies.

Adaptive Dictionary Reconstruction for Compressed Sensing of ECG Signals.

This paper proposes a novel adaptive dictionary (AD) reconstruction scheme to improve the performance of compressed sensing (CS) with electrocardiogram signals (ECG). The method is based on the use of multiple dictionaries, created using dictionary learning (DL) techniques for CS signal reconstruction. The modified reconstruction framework is a two-stage process that leverages information about the signal from an initial signal reconstruction stage. By identifying whether a QRS complex is present and if so, determining a location estimate of the QRS, the most appropriate dictionary is selected and a second stage more refined signal reconstruction can be obtained. The performance of the proposed algorithm is compared with state-of-the-art CS implementations in the literature, as well as the set partitioning in hierarchical trees (SPIHT) wavelet-based lossy compression algorithm. The results indicate that the proposed reconstruction scheme outperforms all existing CS implementations in terms of signal fidelity at each compression ratio tested. The performance of the proposed approach also compares favorably with SPIHT in terms of signal reconstruction quality. Furthermore, an analysis of the overall power consumption of the proposed ECG compression framework as would be used in a body area network (BAN) demonstrates positive results for the proposed CS approach when compared with existing CS techniques and SPIHT.

Energy-efficient Compressed Sensing for ambulatory ECG monitoring.

Advances in Compressed Sensing (CS) are enabling promising low-energy implementation solutions for wireless Body Area Networks (BAN). While studies demonstrate the potential of CS in terms of overall energy efficiency compared to state-of-the-art lossy compression techniques, the performance of CS remains limited. The aim of this study is to improve the performance of CS-based compression for electrocardiogram (ECG) signals. This paper proposes a CS architecture that combines a novel redundancy removal scheme with quantization and Huffman entropy coding to effectively extend the Compression Ratio (CR). Reconstruction is performed using overcomplete sparse dictionaries created with Dictionary Learning (DL) techniques to exploit the highly structured nature of ECG signals. Performance of the proposed CS implementation is evaluated by analyzing energy-based distortion metrics and diagnostic metrics including QRS beat-detection accuracy across a range of CRs. The proposed CS approach offers superior performance to the most recent state-of-the-art CS implementations in terms of signal reconstruction quality across all CRs tested. Furthermore, QRS detection accuracy of the technique is compared with the well-known lossy Set Partitioning in Hierarchical Trees (SPIHT) compression technique. The proposed CS approach outperforms SPIHT in terms of achievable CR, using the area under the receiver operator characteristic (ROC) curve (AUC). For an application where a minimum AUC performance threshold of 0.9 is required, the proposed technique extends the CR from 64.6 to 90.45 compared with SPIHT, ensuring a 40% saving on wireless transmission costs. Therefore, the results highlight the potential of the proposed technique for ECG computer-aided diagnostic systems.

Utilizing inspiratory airflows during standard polysomnography to assess pharyngeal function in children during sleep.

OBJECTIVES: Obstructive sleep apnea (OSA) is the result of pharyngeal obstruction that occurs predominantly during REM in children. Pathophysiologic mechanisms responsible for upper airway obstruction, however, are poorly understood. Thus, we sought to characterize upper airway obstruction in apneic compared to snoring children during sleep. We hypothesized that apneic compared to snoring children would exhibit an increased prevalence and severity of upper airway obstruction, that would be greater in REM compared to non-REM, and would improve following adenotonsillectomy. STUDY DESIGN: Apneic children were assessed with routine polysomnography before and after adenotonsillectomy, and compared to snoring children matched for gender, age, and BMI z-score. In addition to traditional scoring metrics, the following were used to characterize upper airway obstruction: maximal inspiratory airflow (%VI max) and percent of time with inspiratory flow-limited breathing (%IFL). RESULTS: OSA compared to snoring children had similar degrees of upper airway obstruction in non-REM; however, during REM, children with sleep apnea exhibited a higher %IFL (98 ± 2% vs.73 ± 8%, P < 0.01) and lower %VI max (56 ± 6 vs.93 ± 10%, P < 0.01). In children with OSA, CO2 levels were elevated during both wake and sleep. Following adenotonsillectomy, upper airway obstruction improved during REM manifest by decreased %IFL (98 ± 2 to 63 ± 9%, P = 0.04), increased %VI max (56 ± 6 to 95 ± 5%, P = 0.01) and decreased CO2 levels. CONCLUSIONS: Differences in the prevalence and severity upper airway obstruction suggest impaired compensatory responses during REM in children with OSA, which improved following adenotonsillectomy.

Oral Alimentation in Neonatal and Adult Populations Requiring High-Flow Oxygen via Nasal Cannula.

Use of high-flow oxygen via nasal cannula (HFO2-NC) is increasingly common in intensive care unit (ICU) settings. Despite the critical interface between respiration and swallowing, and the high acuity of patients in ICUs, the impact of HFO2-NC on feeding and swallowing is unknown. The present prospective, single-center, cohort study investigated the impact of HFO2-NC use on oral alimentation in neonatal and adult ICU patients. Oral alimentation status was evaluated in 100 consecutive ICU inpatients (50 neonatal and 50 adult) requiring HFO2-NC. Participant characteristics, respiratory support, successful initiation of oral feeding in neonates, and successful resumption of oral feeding in adults were recorded. Seventeen of 50 (34 %) neonates requiring HFO2-NC were deemed developmentally and medically appropriate by the neonatologist and nursing to begin oral alimentation. All 17 (100 %) were successful with initiation of oral feedings. Thirty-three of 50 (66 %) continued nil per os due to prematurity or medical conditions precluding oral alimentation at time of data collection. Thirty-nine of 50 (78 %) adults requiring HFO2-NC were deemed medically appropriate by the intensivist and nursing to resume oral alimentation (n = 34) or with a functional swallow without aspiration on FEES (n = 5). All 39 (100 %) resumed oral alimentation successfully. Eleven of 50 (22 %) continued nil per os due to severe respiratory issues precluding both swallow testing and oral alimentation at time of data collection. All developmentally and medically appropriate neonatal and adult patients requiring HFO2-NC were successful with either the introduction or resumption of oral alimentation. Patients requiring HFO2-NC who are identified as having feeding or swallowing issues should be referred for swallowing evaluations using the same criteria as patients who do not require HFO2-NC, as it is not the use of HFO2-NC but rather patient-specific determinants of feeding and swallowing readiness and their underlying medical conditions that impact readiness for oral alimentation status.

Polysomnographic Markers in Children With Cystic Fibrosis Lung Disease.

BACKGROUND AND OBJECTIVES: Children with cystic fibrosis (CF) often report poor sleep, increased daytime sleepiness, and fatigue. The purpose of this study was to identify respiratory patterns over the spectrum of disease severity in children with CF. The overall hypothesis for the current study is that children with CF compared with snoring control subjects demonstrate gas exchange abnormalities and increased respiratory loads during sleep that are not reported or recognized by conventional polysomnography (PSG). METHODS: Analysis of breathing patterns and gas exchange on PSG was performed in children with CF and healthy controls matched by age and BMI. For all CF and control subjects, the indication for PSG was evaluation for obstructive sleep apnea based on a history of snoring. RESULTS: Children with CF, compared with age- and BMI-matched snoring controls, demonstrated lower oxyhemoglobin saturation (95% ± 1.6% vs 98% ± 0.6%, P = .005), higher respiratory rate (19.5 ± 4.9 vs 16.5 ± 1.2 breaths per minute, P = .03), and a higher proportion of inspiratory flow limitation (44.1% ± 24.7% vs 12.1% ± 13.5%, P = .007) during non-rapid eye movement sleep. The respiratory disturbance index did not differ between CF and snoring control groups (1.5 ± 2.7 vs 0.6 ± 0.6 events per hour, P = .11). CONCLUSIONS: Children with CF exhibited abnormalities in gas exchange and increased respiratory load during sleep compared with normal age- and BMI-matched snoring controls. Because these abnormalities were independent of weight and lung function, sleep state may serve as an opportunity for early detection of breathing abnormalities and possibly CF lung disease progression.

Nasal insufflation treatment adherence in obstructive sleep apnea.

BACKGROUND: Nasal insufflation (NI) is a novel treatment method that has been introduced for improving respiration during sleep. NI's warmed and humidified nasal airflow provides ventilatory assistance delivered as a rapidly dispersed pressure head, with minimal side wall pressures, that may affect treatment tolerability. The aim of the current study was to investigate objective and subjective adherence rates for NI therapy in mild to moderate obstructive sleep apnea (OSA). METHODS: Ten patients (three men and seven women; age, 51.3 ± 9.6 years; BMI, 32.2 ± 7.7 kg/m2 [mean ± sd]) with recently diagnosed mild to moderate OSA (10.9 ± 5.8 events/h) were investigated. A crossover design was used to compare adherence to NI and continuous positive airway pressure (CPAP) therapy using a range of objective and subjective measurements. Objective (sleep efficiency (%) and arousal indices (arousal/h)) and subjective evaluations of sleep quality were carried out each night in the laboratory. During in-home treatment, adherence for both therapies was assessed objectively (time on therapy) and subjectively (self-reported sleep diary). RESULTS: Objectively derived adherence values were comparable for CPAP and NI, with both treatment devices sharing similar usage per night (3.5 ± 2.5 vs. 3.6 ± 1.6 h/night; respectively) and the number of nights with at least 4 h of treatment (5.5 ± 4.3 vs. 6.8 ± 3.3 nights/trial, respectively). Self-reported adherence was significantly higher than objectively assessed adherence (p < 0.03). CONCLUSIONS: This study showed similar adherence to NI and CPAP over a short period of usage. A randomized clinical trial is now essential for determining the comparative effectiveness of NI therapy in relation to treatment with CPAP.

Compressed sensing for bioelectric signals: a review.

This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.

Leptin and the control of pharyngeal patency during sleep in severe obesity.

RATIONALE: Obesity imposes mechanical loads on the upper airway, resulting in flow limitation and obstructive sleep apnea (OSA). In previous animal models, leptin has been considered to serve as a stimulant of ventilation and may prevent respiratory depression during sleep. We hypothesized that variations in leptin concentration among similarly obese individuals will predict differences in compensatory responses to upper airway obstruction during sleep. METHODS: An observational study was conducted in 23 obese women [body mass index (BMI): 46 ± 3 kg/m(2), age: 41 ± 12 yr] and 3 obese men (BMI: 46 ± 3 kg/m(2), age: 43 ± 4 yr). Subjects who were candidates for bariatric surgery were recruited to determine upper airway collapsibility under hypotonic conditions [pharyngeal critical pressure (passive PCRIT)], active neuromuscular responses to upper airway obstruction during sleep, and overnight fasting serum leptin levels. Compensatory responses were defined as the differences in peak inspiratory airflow (ΔVImax), inspired minute ventilation (ΔVI), and pharyngeal critical pressure (ΔPCRIT) between the active and passive conditions. RESULTS: Leptin concentration was not associated with sleep disordered breathing severity, passive PCRIT, or baseline ventilation. In the women, increases in serum leptin concentrations were significantly associated with increases in ΔVImax (r(2) = 0.44, P < 0.001), ΔVI (r(2) = 0.40, P < 0.001), and ΔPCRIT (r(2) = 0.19, P < 0.04). These responses were independent of BMI, waist-to-hip ratio, neck circumference, or sagittal girth. CONCLUSION: Leptin may augment neural compensatory mechanisms in response to upper airway obstruction, minimizing upper airway collapse, and/or mitigating potential OSA severity. Variability in leptin concentration among similarly obese individuals may contribute to differences in OSA susceptibility.

Compressive sampling for time critical microwave imaging applications.

Across all biomedical imaging applications, there is a growing emphasis placed on reducing data acquisition and imaging times. This research explores the use of a technique, known as compressive sampling or compressed sensing (CS), as an efficient technique to minimise the data acquisition time for time critical microwave imaging (MWI) applications. Where a signal exhibits sparsity in the time domain, the proposed CS implementation allows for sub-sampling acquisition in the frequency domain and consequently shorter imaging times, albeit at the expense of a slight degradation in reconstruction quality of the signals as the compression increases. This Letter focuses on ultra wideband (UWB) radar MWI applications where reducing acquisition is of critical importance therefore a slight degradation in reconstruction quality may be acceptable. The analysis demonstrates the effectiveness and suitability of CS with UWB applications.

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression.

In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications. Data compression is an important factor in these applications as a means of reducing the amount of data required for transmission. Allowing for a carefully controlled level of loss in the compression method can provide significant gains in data compression. Quantisation is easy to implement method of data reduction that requires little power expenditure. However, it is a relatively simple, non-invertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity. Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant. SPIHT is a state of the art signal compression method based on the Discrete Wavelet Transform (DWT), originally designed for images but highly regarded as a general means of data compression. This paper compares the approaches of compression by changing the quantisation level of the DWT coefficients in SPIHT, with the standard thresholding method used in SPIHT, to evaluate the effects of each on EEG signals. The combination of increasing quantisation and the use of SPIHT as an entropy encoder has been shown to provide significantly improved results over using the standard SPIHT algorithm alone.

Approach for streamlining measurement of complex physiological phenotypes of upper airway collapsibility.

UNLABELLED: The critical closing pressure (PCRIT), a quantitative assessment of upper airway collapsibility, is derived from pressure flow relationship during sleep. The analytic generation of the pressure flow relationships are non-standardized due to various regression models (linear, spline, median), breath characteristics (flow limited, non-flow limited) or known covariates (sleep stage, body position). We propose a GUI based PCRIT Analysis Software (PAS) to streamline PCRIT analysis and validate its reliability and accuracy compared to current analysis procedures. METHODS: Seventeen subjects underwent a physiology sleep study in which the PCRIT was determined during NREM sleep. Data analysis was performed independently using three paradigms: (1) PAS (Igor Pro; median regression), (2) non-graphic statistics application (SAS; spline regression), and (3) manual spreadsheet calculations (Excel; linear regression). The reliability and accuracy of the PAS was examined through the agreement between each approach using Bland-Altman plots of the mean difference and within-individual variation using intra-class correlation (ICC). RESULTS: There was no difference in the group mean values for PCRIT using the PAS (-1.7 ± 0.7 cm H2O) compared to spline regression (-1.6 ± 0.7 cm H2O; p=0.69) or linear regression (-2.1 ± 0.7 cm H2O; p=0.92). The Bland-Altman analysis did not demonstrate a systematic bias between the PAS and either approach. There was a mean difference of 0.39 ± 0.2 cm H2O between the PAS and linear regression approaches, with upper and lower limits of agreement of 1.81 and -1.02 cm H2O, respectively. The PAS and spline analyses demonstrated an even smaller mean difference of -0.10 ± 0.1cm H2O, with upper and lower limits of 0.90 and -1.08 cm H2O, respectively. CONCLUSION: PAS preserves the reliability and accuracy of the original PCRIT analysis methods while vastly improving their efficiency through graphic user interface and automation of analytic processes. Providing a standardized platform for physiologic data processing offers the ability to implement quality assurance and control procedures for multicenter studies as well as cost saving by improving the efficiency of complex repetitive tasks.