Convolutional Neural Networks for Apnea Detection from Smartphone Audio Signals: Effect of Window Size

Although sleep apnea is one of the most prevalent sleep disorders, most patients remain undiagnosed and untreated. The gold standard for sleep apnea diagnosis, polysomnography, has important limitations such as its high cost and complexity. This leads to a growing need for novel cost-effective systems. Mobile health tools and deep learning algorithms are nowadays being proposed as innovative solutions for automatic apnea detection. In this work, a convolutional neural network (CNN) is trained for the identification of apnea events from the spectrograms of audio signals recorded with a smartphone. A systematic comparison of the effect of different window sizes on the model performance is provided. According to the results, the best models are obtained with 60 s windows (sensitivity-0.72, specilicity-0.89, AUROC = 0.88), For smaller windows, the model performance can be negatively impacted, because the windows become shorter than most apnea events, by which sound reductions can no longer be appreciated. On the other hand, longer windows tend to include multiple or mixed events, that will confound the model. This careful trade-off demonstrates the importance of selecting a proper window size to obtain models with adequate predictive power. This paper shows that CNNs applied to smartphone audio signals can facilitate sleep apnea detection in a realistic setting and is a first step towards an automated method to assist sleep technicians. Clinical Relevance- The results show the effect of the window size on the predictive power of CNNs for apnea detection. Furthermore, the potential of smartphones, audio signals, and deep neural networks for automatic sleep apnea screening is demonstrated

Computational modelling identifies impact of subtle anatomical variation on skeletal muscle local calcium dynamics

Calcium is the main regulator of skeletal muscle metabolic activity. The question has been addressed whether the highly structured spatial organization of sites of Ca2+ release, uptake and action in skeletal muscle substantially impacts the dynamics of cytosolic Ca2+ handling and thereby the physiology of the cell. Hereto, the spatiotemporal dynamics of the free calcium distribution in a fast-twitch muscle sarcomere was studied using a reaction-diffusion computational model. The model was based on the model of Baylor and Hollingworth (J Gen Physiol. 1998 112:297–316), but was adapted to handle local calcium dynamics in mouse EDL fast twitch muscle at 35C. Furthermore, the Ca2+ mass balance was closed by adding a mathematical representation of the sarcoplasmic reticulum. Experimental calcium time courses (high time resolution, but spatially averaged) obtained under physiological conditions (35C, 125 Hz stimulation frequency) were used for model validation. The model showed that subtle changes in sarcomere microstructure influenced the local calcium concentration. Furthermore, local calcium concentration sensed by mitochondria was higher than average calcium concentration and also above the activation constant of the mitochondria, whereas the local concentration was not. Furthermore, the free Ca2+ concentration was higher at the positions with troponin C than without troponin C

Prediction of fetal and neonatal outcomes after preterm manifestations of placental insufficiency:systematic review of prediction models

Objectives: To identify all prediction models for fetal and neonatal outcomes in pregnancies with preterm manifestations of placental insufficiency (gestational hypertension, pre-eclampsia, HELLP syndrome or fetal growth restriction with its onset before 37 weeks' gestation) and to assess the quality of the models and their performance on external validation. Methods: A systematic literature search was performed in PubMed, Web of Science and EMBASE. Studies describing prediction models for fetal/neonatal mortality or significant neonatal morbidity in patients with preterm placental insufficiency disorders were included. Data extraction was performed using the CHARMS checklist. Risk of bias was assessed using PROBAST. Literature selection and data extraction were performed by two researchers independently. Results: Our literature search yielded 22 491 unique publications. Fourteen were included after full-text screening of 218 articles that remained after initial exclusions. The studies derived a total of 41 prediction models, including four models in the setting of pre-eclampsia or HELLP, two models in the setting of fetal growth restriction and/or pre-eclampsia and 35 models in the setting of fetal growth restriction. None of the models was validated externally, and internal validation was performed in only two studies. The final models contained mainly ultrasound (Doppler) markers as predictors of fetal/neonatal mortality and neonatal morbidity. Discriminative properties were reported for 27/41 models (c-statistic between 0.6 and 0.9). Only two studies presented a calibration plot. The risk of bias was assessed as unclear in one model and high for all other models, mainly owing to the use of inappropriate statistical methods. Conclusions: We identified 41 prediction models for fetal and neonatal outcomes in pregnancies with preterm manifestations of placental insufficiency. All models were considered to be of low methodological quality, apart from one that had unclear methodological quality. Higher-quality models and external validation studies are needed to inform clinical decision-making based on prediction models.</p

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors

Abstract Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images

A ternary PEDOT-TiO2-reduced graphene oxide nanocomposite for supercapacitor applications

A ternary composite of PEDOT was prepared with TiO2 via emulsion polymerization method adjusting various weight ratios of TiO2 to PEDOT and synthesized rGO was then blended with this composite. The FTIR, UV–Vis and XRD analysis displayed characteristic features of PEDOT and TiO2. The morphology of the nano-hybrid structure was additionally investigated by SEM analysis. Pore size and surface area analysis of particles were characterized by BET method. The electrochemical analysis showed that the specific capacitance (Csp) for PEDOT-TiO2-15-rGO was 18.9 F.cm-2 at 0.1 mA g-1 current density

Interprofessional Consensus Regarding Design Requirements for Liquid-Based Perinatal Life Support (PLS) Technology

Liquid-based perinatal life support (PLS) technology will probably be applied in a first-in-human study within the next decade. Research and development of PLS technology should not only address technical issues, but also consider socio-ethical and legal aspects, its application area, and the corresponding design implications. This paper represents the consensus opinion of a group of healthcare professionals, designers, ethicists, researchers and patient representatives, who have expertise in tertiary obstetric and neonatal care, bio-ethics, experimental perinatal animal models for physiologic research, biomedical modeling, monitoring, and design. The aim of this paper is to provide a framework for research and development of PLS technology. These requirements are considering the possible respective user perspectives, with the aim to co-create a PLS system that facilitates physiological growth and development for extremely preterm born infants

The prognostic value of multivoxel magnetic resonance spectroscopy determined metabolite levels in white and grey matter brain tissue for adverse outcome in term newborns following perinatal asphyxia

Magnetic resonance spectroscopy can identify brain metabolic changes in perinatal asphyxia by providing ratios of metabolites, such as choline (Cho), creatine (Cr), N-acetyl aspartate (NAA) and lactate (Lact) [Cho/Cr, Lact/NAA, etc.]. The purpose of this study was to quantify the separate white and grey matter metabolites in a slab cranial to the ventricles and relate these to the outcome. A standard 2D-chemical shift imaging protocol was used for measuring a transverse volume of interest located cranial to the ventricles allowing for direct comparison of the metabolites in white and grey matter brain tissue in 24 term asphyxiated newborns aged 3 to 16 days. Cho, NAA and Lact showed significant differences between four subgroups of asphyxiated infants with more and less favourable outcomes. High levels of Cho and Lact in the grey matter differentiated non-survivors from survivors (P = 0.003 and P = 0.017, respectively). In perinatal asphyxia the levels of Cho, NAA and Lact in both white and grey matter brain tissue are affected. The levels of Cho and Lact measured in the grey matter are the most indicative of survival. It is therefore advised to include grey matter brain tissue in the region of interest examined by multivoxel MR spectroscopy. aEuro cent Magnetic resonance spectroscopy can identify brain metabolic changes in perinatal asphyxia. aEuro cent Choline and lactate levels in grey matter seem the best indicators of survival. aEuro cent Both grey and white matter should be examined during spectroscopy for perinatal asphyxia