Nocturnal vestibular stimulation using a rocking bed improves a severe sleep disorder in a patient with mitochondrial disease.

Mitochondrial diseases are rare genetic disorders often accompanied by severe sleep disorders. We present the case of a 12-year-old boy diagnosed with a severe primary mitochondrial disease, exhibiting ataxia, spasticity, progressive external ophthalmoplegia, cardiomyopathy and severely disrupted sleep, but no cognitive impairment. Interestingly, his parents reported improved sleep during night train rides. Based on this observation, we installed a rocking bed in the patient's bedroom and performed different interventions, including immersive multimodal vestibular, kinesthetic and auditory stimuli, reminiscent of the sensory experiences encountered during train rides. Over a 5-month period, we conducted four 2-week nocturnal interventions, separated by 1-week washout phases, to determine the subjectively best-perceived stimulation parameters, followed by a final 4-week intervention using the optimal parameters. We assessed sleep duration and quality using the Mini Sleep Questionnaire, monitored pulse rate changes and used videography to document nocturnal interactions between the patient and caregivers. Patient-reported outcome measures, clinical examinations and personal outcomes of specific interests were used to document daytime sleepiness, restlessness, anxiety, fatigue, cognitive performance and physical posture. In the final 4-week intervention, sleep duration increased by 25%, required caregiver interactions reduced by 75%, and caregiving time decreased by 40%. Subjective fatigue, assessed by the Checklist Individual Strength, decreased by 40%, falling below the threshold of severe fatigue. Our study suggests that rocking beds could provide a promising treatment regime for selected patients with persistent severe sleep disorders. Further research is required to validate these findings in larger patient populations with sleep disorders and other conditions.

Pleural effusion in severe aortic stenosis: marker of an adverse haemodynamic constellation and poor prognosis.

AIM: Pleural effusion (PE) is a common chest radiography (CXR) finding in patients with advanced cardiac disease. The pathophysiology and clinical value of PE in this setting are incompletely defined. We aimed to assess the haemodynamic correlates and prognostic impact of PE in patients with severe aortic stenosis (AS). METHODS AND RESULTS: We studied 471 patients (mean age 74 ± 10 years) with severe AS (indexed aortic valve area 0.42 ± 0.12 cm2/m2, left ventricular ejection fraction 58 ± 12%) undergoing right heart catheterization and upright CXR prior to aortic valve replacement (AVR). Two radiologist independently evaluated all CXR for the presence of bilateral PE, unilateral, or no PE, blinded to any other data. There were 49 (10%) patients with bilateral PE, 32 (7%) patients with unilateral PE, and 390 (83%) patients with no PE. Patients with bilateral PE had the highest mean right atrial pressure, mean pulmonary artery wedge pressure (mPAWP), and pulmonary vascular resistance, and had the lowest stroke volume index while those with unilateral PE had intermediate values. In the multivariate analysis, mPAWP was an independent predictor of any PE and bilateral PE. After a median (interquartile range) post-AVR follow-up of 1361 (957-1878) days mortality was highest in patients with bilateral PE (2.7 times higher than in patients without PE), whereas patients with unilateral PE had similar mortality as those without PE. CONCLUSIONS: In severe AS patients, the presence of PE, particularly bilateral PE, is a marker of a poor haemodynamic constellation. Bilateral PE is associated with a substantially increased post-AVR mortality.

ECG left ventricular hypertrophy in aortic stenosis: Relationship with cardiac structure, invasive hemodynamics, and long-term mortality.

BACKGROUND: In aortic stenosis (AS), left ventricular hypertrophy (LVH) is the response to pressure overload and represents the substrate for a maladaptive cascade, the so-called AS-related cardiac damage. We hypothesized that in AS patients electrocardiogram (ECG) LVH not only predicts echocardiography LVH but also other noninvasive and invasive markers of cardiac damage and prognosis after aortic valve replacement (AVR). METHODS: In 279 patients with severe AS undergoing ECG, echocardiography, and cardiac catheterization before AVR, the Sokolow-Lyon index, the Cornell product, the Romhilt-Estes score, and the Peguero-Lo Presti score were assessed. RESULTS: The mean left ventricular mass index was 109 ± 34 g/m2 , and 131 (47%) patients had echocardiography LVH. The areas under the receiver operator characteristics curve (AUC) for the Sokolow-Lyon index, the Cornell product, the Romhilt-Estes score, and the Peguero-Lo Presti score for the prediction of echocardiography LVH were 0.59, 0.70, 0.63, and 0.65. The Peguero-Lo Presti score had the numerically greatest AUC for the prediction of left ventricular end-diastolic pressure >15 mmHg, mean pulmonary artery wedge pressure >15 mmHg, pulmonary vascular resistance >3 Wood units, mean right atrial pressure >14 mmHg, and stroke volume index <31 mL/m2 . After a median follow-up of 1365 (interquartile range: 931-1851) days after AVR only the Peguero-Lo Presti score was significantly associated with all-cause mortality [hazard ratio: 1.24 (95% confidence interval: 1.01-1.54); per 1 mV increase; p = .045]. CONCLUSIONS: Among severe AS patients, the Peguero-Lo Presti score is associated with abnormalities in cardiac structure including LVH, invasive measures of cardiac damage, and long-term mortality after AVR.

Unobtrusive Sleep Position Classification Using a Novel Optical Tactile Sensor.

Unobtrusive sleep position classification is essential for sleep monitoring and closed-loop intervention systems that initiate position changes. In this paper, we present a novel unobtrusive under-mattress optical tactile sensor for sleep position classification. The sensor uses a camera to track particles embedded in a soft silicone layer, inferring the deformation of the silicone and therefore providing information about the pressure and shear distributions applied to its surface.We characterized the sensitivity of the sensor after placing it under a conventional mattress and applying different weights (258 g, 500 g, 5000 g) on top of the mattress in various predefined locations. Moreover, we collected multiple recordings from a person lying in supine, lateral left, lateral right, and prone positions. As a proof-of-concept, we trained a neural network based on convolutional layers and residual blocks that classified the lying positions based on the images from the tactile sensor.We observed a high sensitivity of the optical tactile sensor: Even after placing the sensor below a conventional mattress, we were able to detect our lowest test weight of 258 g. Using the neural network, we were able to classify the four sleep positions, lateral left, lateral right, prone, and supine with a classification accuracy of 91.2 %.The high sensitivity of the sensor, as well as the good performance in the classification task, demonstrate the feasibility of using such a sensor in a robotic bed setup.Clinical Relevance- Positional Obstructive Sleep Apnea is highly prevalent across the general population. Today's gold standard treatment of using CPAP ventilation is often not accepted, leading to unwanted treatment cessations. Alternative treatments, such as positional interventions through robotic beds are highly promising. However, these beds require reliable detection of the lying position. In this paper, we present a novel, scalable and completely unobtrusive sensor that is concealed under the mattress while classifying sleeping positions with high accuracy.

Enhancing Sleep Quality with Closed-Loop Autotuning of a Robotic Bed.

Sleep is essential to boost the rehabilitation outcome as it facilitates motor learning, enhances cognitive performance, and improves mood and well-being. Rocking beds that provide vestibular stimulation may be a promising and non-invasive alternative to conventional pharmaceutical treatments for individuals with sleep problems, offering regenerative sleep without unwanted side effects. Previous research has shown that the effectiveness of the interventions is related to the chosen rocking acceleration. Moreover, the movement of the bed must be comfortable and smooth to avoid disturbing the user's sleep. Previously, the motor control parameters were tuned manually, which was time-consuming, subjective, and did not guarantee minimum deviation from the desired acceleration profile. In this work, we present an efficient and effective method using Gaussian processes to automatically tune the PI control parameters of a rocking bed moving along the longitudinal axis. We first simulated the kinematics of a rocking bed and optimized the control parameters for a chosen objective function that included the desired and the actual accelerations in the movement direction. We then compared the number of iterations needed to reach this objective for a model based on Gaussian processes and for a model based on a naive random exploration of the parameter space. Finally, we implemented the Gaussian process on the rocking bed to automatically tune the control parameters and subjectively compared them to the control parameters that were previously obtained after manual tuning. Our simulation showed that we can reach the control objective after a constant number of iterations using Gaussian processes, independent of the search space size. For the random search, the number of iterations increased quadratically with the size of the search space. The Gaussian process was found to be well transferable to the rocking bed. After less than one hour, control parameters were discovered that outperformed the previous parameters in terms of smoothness. However, despite the smoother motion, the noise emission from the motor, which was not part of the optimization, increased considerably. Our presented technique based on Gaussian processes significantly reduced the time and effort required to optimize the bed's control parameters compared to manual tuning. In future work, the control objective has to be refined to include noise emission as an optimization metric as low noise is an important aspect in sleep-related applications.

Transferability of a Sensing Mattress for Posture Classification from Research into Clinics.

Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

Estimated glomerular filtration rate, haemodynamics, and mortality in patients with aortic stenosis.

BACKGROUND: In aortic stenosis (AS), estimated glomerular filtration rate (eGFR) is an important prognostic marker but its haemodynamic determinants are unknown. We investigated the correlation between eGFR and invasive haemodynamics and long-term mortality in AS patients undergoing aortic valve replacement (AVR). METHODS: We studied 503 patients [median (interquartile range) age 76 (69-81) years] with AS [indexed aortic valve area .42 (.33-.49) cm2 /m2 ] undergoing cardiac catheterization prior to surgical (72%) or transcatheter (28%) AVR. Serum creatinine was measured on the day before cardiac catheterization for eGFR calculation (CKD-EPI formula). RESULTS: The median eGFR was 67 (53-82) mL/min/1.73 m2 . There were statistically significant correlations between eGFR and mean right atrial pressure (r = -.13; p = .004), mean pulmonary artery pressure (mPAP; r = -.25; p < .001), mean pulmonary artery wedge pressure (r = -.19; p < .001), pulmonary vascular resistance (r = -.21; p < .001), stroke volume index (r = .16; p < .001), extent of coronary artery disease, and mean transvalvular gradient but not indexed aortic valve area. In multivariate linear regression, higher age, lower haemoglobin, lower mean transvalvular gradient (i.e. lower flow), lower diastolic blood pressure, and higher mPAP were independent predictors of lower eGFR. After a median post-AVR follow-up of 1348 (948-1885) days mortality was more than two-fold higher in patients in the first eGFR quartile compared to those in the other three quartiles [hazard ratio 2.18 (95% confidence interval 1.21-3.94); p = .01]. CONCLUSION: In patients with AS, low eGFR is a marker of an unfavourable haemodynamic constellation as well as important co-morbidities. This may in part explain the association between low eGFR and increased post-AVR mortality.

Pulmonary Congestion by Conventional Chest Radiography: Relationship With Hemodynamics and Mortality in Patients With Severe Aortic Stenosis.

Background: The relationship between chest radiograph (CXR) findings of pulmonary congestion and invasive hemodynamics and clinical outcomes in patients with cardiac diseases is unclear. We assessed the correlation between a CXR-based congestion score (RxCS) and the mean pulmonary artery wedge pressure (mPAWP) and the prognostic impact of RxCS and mPAWP in severe aortic stenosis (AS). Methods: In 471 patients with severe AS undergoing right heart catheterization and upright CXR, the RxCS was calculated (6 items, maximum score: 10 points) independently by 2 radiologists (average value taken) blinded to clinical data. Congestion was defined as an RxCS > 1. Four patterns were defined based on the presence or absence of congestion (C+ or C-) and elevated (> 15 mm Hg) or normal mPAWP (P+ or P-). Results: The median (interquartile range) RxCS was 1 (0-2). Patients with an RxCS > 1 (n = 207) had a higher mean right atrial pressure, mean pulmonary artery pressure, mPAWP, and pulmonary vascular resistance than patients with an RxCS ≤ 1 (n = 264). However, the correlation between the RxCS and the mPAWP was moderate only (r = 0.45). Patients with a C+/P+ pattern had the worst hemodynamics, whereas C-/P- patients had the most favourable constellation. After a median post-valve replacement follow-up of 1361 days, mortality was higher in patients with RxCs > 1 vs ≤ 1 as well as mPAWP > 15 mm Hg vs ≤15 mm Hg. Mortality was highest in C+/P+ patients and lowest in C-/P- patients, whereas it was intermediate in C-/P+ and C+/P- patients. Conclusions: In AS patients, RxCS and mPAWP have a significant but moderate correlation. Both RxCS and mPAWP provide prognostic information.

Contexte: Des zones floues persistent quant au lien entre les signes de congestion pulmonaire à la radiographie thoracique, les examens hémodynamiques invasifs et les résultats cliniques chez les patients atteints de maladies cardiaques. Nous avons donc évalué, d'une part, la corrélation entre le score radiologique de congestion pulmonaire et la pression capillaire pulmonaire moyenne et, d'autre part, la valeur pronostique du score radiologique de congestion pulmonaire et de la pression capillaire pulmonaire moyenne dans les cas de sténose aortique sévère. Méthodologie: Chez 471 patients atteints d'une sténose aortique sévère soumis à un cathétérisme du cœur droit et à une radiographie thoracique en position debout, un score radiologique de congestion pulmonaire a été calculé (6 items, score maximal de 10 points) de façon indépendante par deux radiologistes (la valeur retenue étant la moyenne) qui ne connaissaient pas les données cliniques des patients. La congestion correspondait à un score radiologique de congestion pulmonaire > 1. Quatre types ont été définis en fonction de la présence ou de l'absence de congestion (C+ ou C­) et d'une valeur de pression capillaire pulmonaire moyenne élevée (>15 mmHg) ou normale (P+ ou P­). Résultats: La médiane (écart interquartile) du score radiologique de congestion a été de 1 (0-2). Les patients dont le score radiologique de congestion était > 1 (n = 207) présentaient des valeurs moyennes plus élevées pour la pression auriculaire droite, la pression artérielle pulmonaire, la pression capillaire pulmonaire et la résistance vasculaire pulmonaire que les patients dont le score radiologique de congestion était ≤ 1 (n = 264). Cependant, la corrélation entre le score radiologique de congestion et la pression capillaire pulmonaire moyenne n'était que modérée (r = 0,45). Les patients de type C+/P+ avaient le profil hémodynamique le plus défavorable, tandis que les patients de type C­/P­ avaient le profil le plus favorable. À l'issue d'un suivi médian de 1361 jours après un remplacement valvulaire, la mortalité était plus élevée chez les patients dont le score radiologique de congestion était > 1 vs un score ≤ 1, de même que chez les patients dont la pression capillaire pulmonaire moyenne était > 15 mmHg vs une valeur ≤ 15 mmHg. La mortalité la plus élevée a été observée chez les patients de type C+/P+, et la plus faible, chez les patients de type C­/P­, tandis qu'elle était intermédiaire chez les patients de types C­/P+ et C+/P­. Conclusions: Chez les patients atteints d'une sténose aortique, on constate une corrélation significative mais modérée entre le score radiologique de congestion pulmonaire et la pression capillaire pulmonaire moyenne. Ces paramètres revêtent tous deux une valeur pronostique.

Label noise and self-learning label correction in cardiac abnormalities classification.

Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise due to inter- and intra-observer variability and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of electrocardiogram (ECG) signals.Approach.A state-of-the-art self-learning multi-class label correction method for image classification is adapted to learn a multi-label classifier for electrocardiogram signals. We evaluated our performance using 5-fold cross-validation on the publicly available PhysioNet/Computing in Cardiology (CinC) 2021 Challenge data, with full and reduced sets of leads. Due to the unknown label noise in the testing set, we tested our approach on the MNIST dataset. We investigated the performance under different levels of structured label noise for both datasets.Main results.Under high levels of noise, the cross-validation results of self-learning label correction show an improvement of approximately 3% in the challenge score for the PhysioNet/CinC 2021 Challenge dataset and an improvement in accuracy of 5% and reduction of the expected calibration error of 0.03 for the MNIST dataset. We demonstrate that self-learning label correction can be used to effectively deal with the presence of unknown label noise, also when using a reduced number of ECG leads.