U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging.

With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high and low confidence and reducing the risk of errors. This study introduces U-PASS, a specialized human-centered machine learning pipeline tailored for clinical applications, which effectively communicates uncertainty to clinical experts and collaborates with them to improve predictions. U-PASS incorporates uncertainty estimation at every stage of the process, including data acquisition, training, and model deployment. Training is divided into a supervised pre-training step and a semi-supervised recording-wise finetuning step. We apply U-PASS to the challenging task of sleep staging and demonstrate that it systematically improves performance at every stage. By optimizing the training dataset, actively seeking feedback from domain experts for informative samples, and deferring the most uncertain samples to experts, U-PASS achieves an impressive expert-level accuracy of 85% on a challenging clinical dataset of elderly sleep apnea patients. This represents a significant improvement over the starting point at 75% accuracy. The largest improvement gain is due to the deferral of uncertain epochs to a sleep expert. U-PASS presents a promising AI approach to incorporating uncertainty estimation in machine learning pipelines, improving their reliability and unlocking their potential in clinical settings.

Obstructive Sleep Apnea Screening by Joint Saturation Signal Analysis and PPG-derived Pulse Rate Oscillations.

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p 0.001) and multiclass (r=0.91, p 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home.

Recovery from shift work.

One fifth of today's workforce is engaged in shift work and exposed to various mental and physical health risks including shift work disorder. Efficiently recovering from shift work through physical and mental interventions allows us to mitigate negative effects on health, enables a better work-life balance and enhances our overall wellbeing. The aim of this review is to provide a state-of-the-art overview of the available literature. The role of sleep timing and naps, light therapy and psychotherapy, diet and exercise in recovery from shift work is presented here. We further review the impact of shift schedules and social support on post-shift unwinding.

Catathrenia in severe obstructive sleep apnea: A novel entity never described before.

STUDY OBJECTIVES: Catathrenia, derived from the Greek κατά (kata) meaning below and θρηνώ (threnia) to lament, is characterized by expiratory groaning episodes during sleep. In a case series of nine patients with severe obstructive sleep apnea, we observed a peculiar groaning entity that has not been described before. METHODS: We described and illustrated the cases with polysomnographic tracings and additional audio recordings. RESULTS: All patients were men, obese (body mass index 39 ± 6 kg/m2) with an apnea-hypopnea index ranging from 47 to 125/h. In addition, we identified groaning events that were consistently preceded by a cortical arousal associated with a "rescue" respiration after an obstructive hypopnea or apnea. These events exhibited characteristics of "mixed apnea's", but the "central apnea-like part" was a prolonged expiratory groaning phase, with immediately after the terminal expiratory snort appearance of an obstructive apnea. In case the duration of this expiration was at least 10 s we calculated these events separately and the index was 8.4 ± 7.7/h. More rarely (index 0.6 ± 0.5/h) a "central apnea mimicking event" with groaning not followed by an obstruction, was observed. We also observed groaning episodes during expiration with a shorter duration (less than 10 s), not calculated separately. Positive airway pressure, which was well tolerated, eliminated these events. CONCLUSIONS: This novel catathrenia entity preceded by a cortical arousal and "rescue" respiration in response to obstructive events is intriguing. Possible explanations for these observations are further discussed in this article.

Acoustic stimulation as a promising technique to enhance slow-wave sleep in Alzheimer's disease: results of a pilot study.

STUDY OBJECTIVES: Sleep disturbances are common in people with Alzheimer's disease (AD), and a reduction in slow-wave activity is the most striking underlying change. Acoustic stimulation has emerged as a promising approach to enhance slow-wave activity in healthy adults and people with amnestic mild cognitive impairment. In this phase 1 study we investigated, for the first time, the feasibility of acoustic stimulation in AD and piloted the effect on slow-wave sleep (SWS). METHODS: Eleven adults with mild to moderate AD first wore the DREEM 2 headband for 2 nights to establish a baseline registration. Using machine learning, the DREEM 2 headband automatically scores sleep stages in real time. Subsequently, the participants wore the headband for 14 consecutive "stimulation nights" at home. During these nights, the device applied phase-locked acoustic stimulation of 40-dB pink noise delivered over 2 bone-conductance transducers targeted to the up-phase of the delta wave or SHAM, if it detected SWS in sufficiently high-quality data. RESULTS: Results of the DREEM 2 headband algorithm show a significant average increase in SWS (minutes) [t(3.17) = 33.57, P = .019] between the beginning and end of the intervention, almost twice as much time was spent in SWS. Consensus scoring of electroencephalography data confirmed this trend of more time spent in SWS [t(2.4) = 26.07, P = .053]. CONCLUSIONS: Our phase 1 study provided the first evidence that targeted acoustic stimuli is feasible and could increase SWS in AD significantly. Future studies should further test and optimize the effect of stimulation on SWS in AD in a large randomized controlled trial. CITATION: Van den Bulcke L, Peeters A-M, Heremans E, et al. Acoustic stimulation as a promising technique to enhance slow-wave sleep in Alzheimer's disease: results of a pilot study. J Clin Sleep Med. 2023;19(12):2107-2112.

Insights into the Use of Point-of-Care Ultrasound for Diagnosing Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) is a sleeping disorder caused by complete or partial disturbance of breathing during the night. Existing screening methods include questionnaire-based evaluations which are time-consuming, vary in specificity, and are not globally adopted. Point-of-care ultrasound (PoCUS), on the other hand, is a painless, inexpensive, portable, and useful tool that has already been introduced for the evaluation of upper airways by anesthetists. PoCUS could also serve as a potential screening tool for the diagnosis of OSA by measuring different airway parameters, including retropalatal pharynx transverse diameter, tongue base thickness, distance between lingual arteries, lateral parapharyngeal wall thickness, palatine tonsil volume, and some non-airway parameters like carotid intima-media thickness, mesenteric fat thickness, and diaphragm characteristics. This study reviewed previously reported studies to highlight the importance of PoCUS as a potential screening tool for OSA.

In search of a cut-off apnea-hypopnea index in type 3 home portable monitors to diagnose and treat obstructive sleep apnea: a mathematical simulation.

The American Academy of Sleep Medicine (AASM) uses similar apnea-hypopnea index (AHI) cut-off values to diagnose and define severity of sleep apnea independent of the technique used: in-hospital polysomnography (PSG) or type 3 portable monitoring (PM). Taking into account that PM theoretically might underestimate the AHI, we explored whether a lower cut-off would be more appropriate. We performed mathematical re-calculations on the diagnostic PSG-AHI (scored using AASM 1999 rules) of 865 consecutive patients with an AHI of ≥20 events/h who started continuous positive airway pressure (CPAP). For a PSG-AHI of ≥15 events/h re-scored using AASM 2012 rules (PSG-AHIAASM2012 ), a PM-respiratory event index (REI)AASM2012 cut-off point of ≥15 events/h resulted in a post-test probability of 100% of having the disease, but with negative tests in 57.1%. A PM-REIAASM2012 cut-off of 8 events/h, still resulted in a positive post-test probability of 100% but with negative tests in only 34.3%. Combination of the cut-off values with clinical estimation of being 'at high risk' based on Epworth Sleepiness Scale (ESS) and Berlin Questionnaire scores only resulted in a small reduction in the percentage of negative tests (respectively 52.7% and 32.7%). After 6 months, CPAP adherence was not lower using the PM-REIAASM 2012 cut-off ≥8 events/h in comparison to ≥15 events/h (median 5.7 vs. 5.8 h/night, p = 0.368) and the reduction in ESS was similar too (median -4 and -5 points, p = 0.083). Consequently, using a lower PM-REIAASM2012 cut-off could result in cost savings because of less negative studies and lesser need for a confirmatory PSG or a performance of a CPAP trial.

Short-term positive effects of a mandibular advancement device in a selected phenotype of patients with moderate obstructive sleep apnea: a prospective study.

STUDY OBJECTIVES: To evaluate (determinants of) treatment success of mandibular advancement device application in a selected phenotype of patients with obstructive sleep apnea (OSA). METHODS: Ninety nonobese patients with moderate OSA (obstructive apnea-hypopnea index [OAHI] ≥ 15 and < 30 events/h) without comorbidities were prospectively included. Polysomnography was performed at baseline and with a mandibular advancement device. A drug-induced sleep endoscopy with jaw thrust was performed in 83%. RESULTS: OAHI reduction ≥ 50% was observed in 73%, OAHI reduction ≥ 50% with OAHI < 10 events/h in 70%, and complete OSA resolution (OAHI < 5 events/h) in 40%. Patients with nonpositional OSA showed a significantly higher rate of complete OSA resolution: Posttest probability increased to 67%. In patients with total disappearance of collapse at velum level and at all levels during drug-induced sleep endoscopy with jaw thrust, the drop in OAHI was impressive with an infinitively high positive likelihood ratio. However, the proportion of patients having nonpositional OSA or the drug-induced sleep endoscopy characteristics as described above was < 20%. The change in snoring disturbance based on a visual analog scale was 76% (interquartile range 40-89%, P < .001) and a statistically significant amelioration in Epworth Sleepiness Scale (especially in somnolent subjects) was observed. High adherence was reported. CONCLUSIONS: In this predefined OSA phenotype, a mandibular advancement device was effective in reduction of OAHI and in amelioration of symptoms. Stratification by nonpositional OSA and findings on drug-induced sleep endoscopy with jaw thrust increased treatment success defined as reduction in OAHI. However, the clinical relevance can be questioned because only a small number of patients demonstrated these characteristics. CITATION: Buyse B, Nguyen PAH, Leemans J, et al. Short-term positive effects of a mandibular advancement device in a selected phenotype of patients with moderate obstructive sleep apnea: a prospective study. J Clin Sleep Med. 2023;19(1):5-16.

A late diagnosis of myasthenia gravis at the respiratory practice: back to basics.

Myasthenia gravis may affect respiratory muscles. To differentiate between other neuromuscular diseases, evaluation for muscle fatigability can be demonstrated by cardiopulmonary exercise testing and the maximal voluntary ventilation test. https://bit.ly/3qMeWFd.

From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep staging.

Objective.The recent breakthrough of wearable sleep monitoring devices has resulted in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset.Approach.In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework are examined, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients.Main results.The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%-27% in accuracy. The performance in the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%-2% compared to a non-personalized model.Significance.In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable electroencephalography applications. (Clinical trial registration number: S64190.).

High adherence to continuous positive airway pressure (CPAP) in patients with obstructive sleep apnea (OSA) in Belgium: a narrative review.

OBJECTIVES: Continuous positive airway pressure (CPAP) is the 'gold standard' treatment for moderate-to-severe obstructive sleep apnea (OSA); adherence is an important issue. The aim of this paper is to review Belgian data on CPAP users and their adherence over a period of 11 years. METHODS: Data delivered annually by the CPAP centers to the Belgian National Institute for Health Insurance (RIZIV/INAMI) were studied. Comments on these results were embedded in a narrative review. RESULTS: On 1 January 2008 27.266 Belgian patients were treated with CPAP, at the end of 2018 this number increased to 121.605. In 2018, the short-term adherence (≤3 months) to CPAP was at least twice as high compared to the United States: the CPAP termination rate in Belgium (mainly due to stop of reimbursement because adherence <4 h/night) was estimated to be 12.4%, considerably lower than the 31.1% of patients on CPAP in the United States using the device <4 h. CONCLUSION: We speculate that this good adherence might be attributed to a stringent Belgian diagnostic and treatment convention model. This model uses 'gold standard' techniques (including in-hospital polysomnography), imposes a minimum capacity of medical doctors and paramedical collaborators, a strict follow-up of the patients, multidisciplinary care and proof of competency. Taking into account the increasing number of patients, a change in the Belgian care strategy is under consideration focusing on more out-of-centre patient's management; we propose a step-by step approach with careful monitoring of the impact of changing policy on adherence.

A critical review of the pharmacological treatment of REM sleep behavior disorder in adults: time for more and larger randomized placebo-controlled trials.

Rapid Eye Movement sleep behavior disorder (RBD) is a parasomnia causing sufferers to physically act out their dreams. These behaviors can disrupt sleep and sometimes lead to injuries in patients and their bed-partners. Clonazepam and melatonin are the first-line pharmacological treatment options for RBD based on direct uncontrolled clinical observations and very limited double-blind placebo-controlled trials. Given the risk for adverse outcomes, especially in older adults, it is of great importance to assess the existing level of evidence for the use of these treatments. In this update, we therefore critically review the clinical and scientific evidence on the pharmacological management of RBD in people aged over 50. We focus on the first-line treatments, and provide an overview of all other alternative pharmacological agents trialed for RBD we could locate as supplementary materials. By amalgamating all clinical observations, our update shows that 66.7% of 1,026 RBD patients reported improvements from clonazepam and 32.9% of 137 RBD patients reported improvements from melatonin treatment on various outcome measures in published accounts. Recently, however, three relatively small randomized placebo-controlled trials did not find these agents to be superior to placebo. Given clonazepam and melatonin are clinically assumed to majorly modify or eliminate RBD in nearly all patients-there is an urgent need to test whether this magnitude of treatment effect remains intact in larger placebo-controlled trials.

Capacitively-Coupled ECG and Respiration for Sleep-Wake Prediction and Risk Detection in Sleep Apnea Patients.

Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep-wake using ccECG and ccBioZ, and detection of high-risk OSA patients. First, signal quality indicators (SQIs) determined the data coverage of ccECG and ccBioZ. Then, a multimodal convolutional neural network (CNN) for sleep-wake prediction was tested on these preprocessed ccECG and ccBioZ data. Finally, two indices derived from this prediction detected patients at risk. The data included 187 PSG recordings of suspected OSA patients, 36 (dataset "Test") of which were recorded simultaneously with PSG, ccECG, and ccBioZ. As a result, two improvements were made compared to prior studies. First, the ccBioZ signal coverage increased significantly due to adaptation of the acquisition system. Secondly, the utility of the sleep-wake classifier increased as it became a unimodal network only requiring respiratory input. This was achieved by using data augmentation during training. Sleep-wake prediction on "Test" using PSG respiration resulted in a Cohen's kappa (κ) of 0.39 and using ccBioZ in κ = 0.23. The OSA risk model identified severe OSA patients with a κ of 0.61 for PSG respiration and κ of 0.39 using ccBioZ (accuracy of 80.6% and 69.4%, respectively). This study is one of the first to perform sleep-wake staging on capacitively-coupled respiratory signals in suspected OSA patients and to detect high risk OSA patients based on ccBioZ. The technology and the proposed framework could be applied in multi-night follow-up of OSA patients.

Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients.

Objectives: Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required. However, most unobtrusive sensors suffer from data loss and sensitivity to movement artifacts. Thus, current sleep detection methods are inadequate, as these require long intervals of good quality. Moreover, sleep monitoring of OSA patients is often less reliable due to heart rate disturbances, movement and sleep fragmentation. The primary aim was to develop a sleep-wake classifier for sleep time estimation of suspected OSA patients, based on single short-term segments of their cardiac and respiratory signals. The secondary aim was to define metrics to detect OSA patients directly from their predicted sleep-wake pattern and prioritize them for clinical diagnosis. Methods: This study used a dataset of 183 suspected OSA patients, of which 36 test subjects. First, a convolutional neural network was designed for sleep-wake classification based on healthier patients (AHI < 10). It employed single 30 s epochs of electrocardiograms and respiratory inductance plethysmograms. Sleep information and Total Sleep Time (TST) was derived for all patients using the short-term segments. Next, OSA patients were detected based on the average confidence of sleep predictions and the percentage of sleep-wake transitions in the predicted sleep architecture. Results: Sleep-wake classification on healthy, mild and moderate patients resulted in moderate κ scores of 0.51, 0.49, and 0.48, respectively. However, TST estimates decreased in accuracy with increasing AHI. Nevertheless, severe patients were detected with a sensitivity of 78% and specificity of 89%, and prioritized for clinical diagnosis. As such, their inaccurate TST estimate becomes irrelevant. Excluding detected OSA patients resulted in an overall estimated TST with a mean bias error of 21.9 (± 55.7) min and Pearson correlation of 0.74 to the reference. Conclusion: The presented framework offered a realistic tool for unobtrusive sleep monitoring of suspected OSA patients. Moreover, it enabled fast prioritization of severe patients for clinical diagnosis.

Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy.

Background.Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals.Objective.To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case.Approach.Simulations were used to evaluate the model order selection when calculating the RSA estimates introduced before, as well as three different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals.Main results.It was found that using a single model order for all the calculations suffices to characterize RSA correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 min might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration.Significance.Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions.

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines.

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG.

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea.

OBJECTIVE: The performance of a novel unobtrusive system based on capacitively-coupled electrocardiography (ccECG) combined with different respiratory measurements is evaluated for the detection of sleep apnea. APPROACH: A sleep apnea detection algorithm is proposed, which can be applied to electrocardiography (ECG) and ccECG, combined with different unobtrusive respiratory measurements, including ECG derived respiration (EDR), respiratory effort measured using the thoracic belt (TB) and capacitively-coupled bioimpedance (ccBioz). Several ECG, respiratory and cardiorespiratory features were defined, of which the most relevant ones were identified using a random forest based backwards wrapper. Using this relevant feature set, a least-squares support vector machine classifier was trained to decide if a one minute segment is apneic or not, based on the annotated polysomnography (PSG) data of 218 patients suspected of having sleep apnea. The obtained classifier was then tested on the PSG and capacitively-coupled data of 28 different patients. MAIN RESULTS: On the PSG data, an AUC of 76.3% was obtained when the ECG was combined with the EDR. Replacing the EDR with the TB led to an AUC of 80.0%. Using the ccECG and ccBioz or the ccECG and TB resulted in similar performances as on the PSG data, while using the ccECG and ccECG-based EDR resulted in a drop in AUC to 67.4%. SIGNIFICANCE: This is the first study which tests an apnea detection algorithm on capacitively-coupled ECG and bioimpedance signals and shows promising results on the capacitively-coupled data set. However, it was shown that the EDR could not be accurately estimated from the ccECG signals. Further research into the effect that respiration has on the ccECG is needed to propose alternative EDR estimates.

Respiratory decline in adult patients with Becker muscular dystrophy: A longitudinal study.

Becker muscular dystrophy (BMD) is a rare hereditary neuromuscular disease, caused by a genetic defect in the Duchenne Muscular Dystrophy (DMD) gene. We studied the natural history of respiratory function and its affecting factors in 23 adult BMD patients. These important data are needed for (future) clinical trials in BMD but are largely lacking. Patients had a median age of 51 years (28-78y) and median follow-up duration of 14 years (2-25y). We analysed 190 pulmonary function measurements with a median interval of one year (1-17y) and measured a 1.00% decline of Forced Vital Capacity percent predicted (FVC%pred) per year (p = 0.004). Loss of ambulation significantly increased the annual rate of FVC decline and was dependent of patient's body mass index (BMI; p = 0.015), with increases in BMI correlating with an even more rapid deterioration of FVC. A decline in Medical Research Council (MRC) sum score was significantly correlated with a decline in FVC (p = 0.003). We conclude that adult BMD patients experience a significant but mild respiratory decline. However, this decline is significantly more rapid and clinically relevant after loss of ambulation, which warrants a more vigilant follow-up of respiratory function in this subgroup.