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.

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.

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.

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.

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.

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.

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.

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.

The effect of lossy ECG compression on QRS and HRV feature extraction

This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database. Sensitivities and specificities between 99% and 85% were computed for each CR utilised. The extracted HRV features were between 99% and 82% similar to the features extracted from the annotated records. A notable accuracy drop over all features extracted was noted beyond a CR of 30, with falls of 10% accuracy beyond this compression ratio.

EEG compression using JPEG2000: how much loss is too much?

Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm is applied to Electroencephalogram (EEG) data from the Freiburg epilepsy database. By varying compression parameters, a range of reconstructions of varying signal fidelity is produced. Although lossy compression has been applied to EEG data in previous studies, it is unclear what level of signal degradation, if any, would be acceptable to a clinician before diagnostically significant information is lost. In this paper, the reconstructed EEG signals are applied to REACT, a state-of-the-art seizure detection algorithm, in order to determine the effect of lossy compression on its seizure detection ability. By using REACT in place of a clinician, many hundreds of hours of reconstructed EEG data are efficiently analysed, thereby allowing an analysis of the amount of EEG signal distortion that can be tolerated. The corresponding compression ratios that can be achieved are also presented.

Effect of a high-flow open nasal cannula system on obstructive sleep apnea in children

OBJECTIVE

Obstructive sleep apnea syndrome in children is associated with significant morbidity. Continuous positive airway pressure (CPAP) treats obstructive apnea in children, but is impeded by low adherence. We, therefore, sought to assess the effect of warm humidified air delivered through an open nasal cannula (treatment with nasal insufflation [TNI]) on obstructive sleep apnea in children with and without adenotonsillectomy.

METHODS

Twelve participants (age: 10 +/- 1 years; BMI: 35 +/- 14 kg/m(2)), with obstructive apnea-hypopnea syndrome ranging from mild to severe (2-36 events per hour) were administered 20 L/min of air through a nasal cannula. Standard sleep architecture, sleep-disordered breathing, and arousal indexes were assessed at baseline, on TNI, and on CPAP. Additional measures of the percentage of time with inspiratory flow limitation, respiratory rate, and inspiratory duty cycle were assessed at baseline and on TNI.

RESULTS

TNI reduced the amount of inspiratory flow limitation, which led to a decrease in respiratory rate and inspiratory duty cycle. TNI improved oxygen stores and decreased arousals, which decreased the occurrence of obstructive apnea from 11 +/- 3 to 5 +/- 2 events per hour (P < .01). In the majority of children, the reduction in the apnea-hypopnea index on TNI was comparable to that on CPAP.

CONCLUSIONS

TNI offers an alternative to therapy to CPAP in children with mild-to-severe sleep apnea. Additional studies will be needed to determine the efficacy of this novel form of therapy.

Polysomnographic values in adolescents with ataxia telangiectasia

Most adolescents with ataxia telangiectasia (A-T) develop progressive bulbar muscle weakness and decreased pulmonary reserve. The purpose of this study was to define the patterns of sleep and respiration during sleep, and to identify sleep-related breathing problems in subjects with A-T. To address these issues, overnight polysomnography was performed on 12 adolescents with A-T. Eleven of the 12 subjects completed overnight polysomnography. The median age was 16 years (range, 13-20 years). All subjects in the study were wheelchair-bound and the median forced vital capacity (% predicted of normal) was 44% (range, 16-82%). The mean sleep efficiency was 72.6% with a mean apnea hypopnea index (AHI) of 0.7 events/hr (range, 0-2.2). The majority of apnea/hypopneas were REM related. The mean central apnea index was 0.1 events/hr (range, 0-0.2). The mean oxygen saturation nadir was 92.7% (range, 87-96) and the mean peak end-tidal carbon dioxide ET(CO(2) ) value was 53.8 mm Hg (range, 49-60). Two of 11 subjects had ET(CO(2) ) values >or=50 mm Hg for more than 50% of total sleep time. In this study, the majority of A-T adolescents had infrequent partial or complete upper airway obstructions during sleep and minimal nighttime hypoxemia. They did, however, have decreased sleep efficiency most likely, due in part, to their underlying neurological condition. This decrease in total sleep time may underestimate hypoventilation. Based on these findings, overnight polysomnography should be considered in adolescents with A-T, particularly in those in which there is a clinical suspicion of sleep related breathing abnormalities.