Measuring High-Resolution Sleep Position in Adolescents over 4 Nights with Smartphone Accelerometers.

Sleep position affects sleep quality and the severity of different diseases. Classical methods to measure sleep position are complex, expensive, and difficult to use outside the laboratory. Wearables and smartphones can help to address these issues to track sleep position at home over several nights. In this study, we monitor high-resolution sleep position in 13 adolescents over 4 nights using smartphone accelerometer data. We aim to investigate the distribution of sleep positions and position changes in adolescents, study their variability across nights, and propose new measures related to nocturnal body movements. We developed a new index, the mean sleep angle change per hour, and calculated three other measures: position shifts per hour, mean time at each position, and periods of immobility. Our results indicate that participants spent 56% of the time on the side (32% right and 24% left), 32% in supine, and 12% in prone position, similar to what happens in adults. However, adolescents moved more than adults during sleep according to all measures. There was some variability between nights, but lower than the inter-subject variability. In conclusion, this work systematically analyzes sleep position over several nights in adolescents, a largely unstudied population, and offers innovative solutions and measures for high-resolution sleep position monitoring in a simple and cost-effective way.Clinical Relevance- Our study characterizes sleep position in adolescents and provides novel unobtrusive methods and quantitative indices to monitor high-resolution sleep position at home during multiple nights.

Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study.

Background: Acute respiratory syndrome due to coronavirus 2 (SARS-CoV-2) is characterised by heterogeneous levels of disease severity. It is not necessarily apparent whether a patient will develop severe disease or not. This cross-sectional study explores whether acoustic properties of the cough sound of patients with coronavirus disease 2019 (COVID-19), the illness caused by SARS-CoV-2, correlate with their disease and pneumonia severity, with the aim of identifying patients with severe disease. Methods: Voluntary cough sounds were recorded using a smartphone in 70 COVID-19 patients within the first 24 h of their hospital arrival, between April 2020 and May 2021. Based on gas exchange abnormalities, patients were classified as mild, moderate or severe. Time- and frequency-based variables were obtained from each cough effort and analysed using a linear mixed-effects modelling approach. Results: Records from 62 patients (37% female) were eligible for inclusion in the analysis, with mild, moderate and severe groups consisting of 31, 14 and 17 patients respectively. Five of the parameters examined were found to be significantly different in the cough of patients at different disease levels of severity, with a further two parameters found to be affected differently by the disease severity in men and women. Conclusions: We suggest that all these differences reflect the progressive pathophysiological alterations occurring in the respiratory system of COVID-19 patients, and potentially would provide an easy and cost-effective way to initially stratify patients, identifying those with more severe disease, and thereby most effectively allocate healthcare resources.

Transcutaneous Cervical Spinal Cord Stimulation Combined with Robotic Exoskeleton Rehabilitation for the Upper Limbs in Subjects with Cervical SCI: Clinical Trial.

(1) Background: Restoring arm and hand function is a priority for individuals with cervical spinal cord injury (cSCI) for independence and quality of life. Transcutaneous spinal cord stimulation (tSCS) promotes the upper extremity (UE) motor function when applied at the cervical region. The aim of the study was to determine the effects of cervical tSCS, combined with an exoskeleton, on motor strength and functionality of UE in subjects with cSCI. (2) Methods: twenty-two subjects participated in the randomized mix of parallel-group and crossover clinical trial, consisting of an intervention group (n = 15; tSCS exoskeleton) and a control group (n = 14; exoskeleton). The assessment was carried out at baseline, after the last session, and two weeks after the last session. We assessed graded redefined assessment of strength, sensibility, and prehension (GRASSP), box and block test (BBT), spinal cord independence measure III (SCIM-III), maximal voluntary contraction (MVC), ASIA impairment scale (AIS), and WhoQol-Bref; (3) Results: GRASSP, BBT, SCIM III, cylindrical grip force and AIS motor score showed significant improvement in both groups (p ≤ 0.05), however, it was significantly higher in the intervention group than the control group for GRASSP strength, and GRASSP prehension ability (p ≤ 0.05); (4) Conclusion: our findings show potential advantages of the combination of cervical tSCS with an exoskeleton to optimize the outcome for UE.

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.

Sleep Apnea & Chronic Obstructive Pulmonary Disease: Overlap Syndrome Dynamics in Patients from an Epidemiological Study.

Obstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. Chronic obstructive pulmonary disease (COPD) is a disorder which induces a persistent inflammation of the lungs. This condition produces hypoventilation, affecting the blood oxygenation, and leads to an increased risk of developing lung cancer and heart disease. In this study, we evaluated how COPD affects the severity and characteristics of OSA in a multivariate demographic database including polysomnographic signals. Results showed SpO2 subtle variations, such as more non-recovered desaturations and increased time below a 90% SpO2 level, which, in the long term, could worsen the risk to suffer cardiovascular and cerebrovascular diseases.Clinical Relevance- COPD increases the OSA risk due to hypoventilation and altered SpO2 behavior.

Detection of Sleep-Disordered Breathing in Patients with Spinal Cord Injury Using a Smartphone.

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients' recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea-hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI ≥ 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients.

Quantitative evaluation of trunk function and the StartReact effect during reaching in patients with cervical and thoracic spinal cord injury.

Objective.Impaired trunk stability is frequent in spinal cord injury (SCI), but there is a lack of quantitative measures for assessing trunk function. Our objectives were to: (a) evaluate trunk muscle activity and movement patterns during a reaching task in SCI patients, (b) compare the impact of cervical (cSCI) and thoracic (tSCI) injuries in trunk function, and (c) investigate the effects of a startling acoustic stimulus (SAS) in these patients.Approach.Electromyographic (EMG) and smartphone accelerometer data were recorded from 15 cSCI patients, nine tSCI patients, and 24 healthy controls, during a reaching task requiring trunk tilting. We calculated the response time (RespT) until pressing a target button, EMG onset latencies and amplitudes, and trunk tilt, lateral deviation, and other movement features from accelerometry. Statistical analysis was applied to analyze the effects of group (cSCI, tSCI, control) and condition (SAS, non-SAS) in each outcome measure.Main results.SCI patients, especially those with cSCI, presented significantly longer RespT and EMG onset latencies than controls. Moreover, in SCI patients, forward trunk tilt was accompanied by significant lateral deviation. RespT and EMG latencies were remarkably shortened by the SAS (the so-called StartReact effect) in tSCI patients and controls, but not in cSCI patients, who also showed higher variability.Significance. The combination of EMG and smartphone accelerometer data can provide quantitative measures for the assessment of trunk function in SCI. Our results show deficits in postural control and compensatory strategies employed by SCI patients, including delayed responses and higher lateral deviations, possibly to improve sitting balance. This is the first study investigating the StartReact responses in trunk muscles in SCI patients and shows that the SAS significantly accelerates RespT in tSCI, but not in cSCI, suggesting an increased cortical control exerted by these patients.

SleepPos App: An Automated Smartphone Application for Angle Based High Resolution Sleep Position Monitoring and Treatment.

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is unreliable, or determined by using specific devices, such as polysomnography, polygraphy or cameras, that can be expensive and difficult to employ at home. The aim of this study is to determine how smartphones could be used to monitor and treat sleep position at home. We divided our research into three tasks: (1) develop an Android smartphone application ('SleepPos' app) which monitors angle-based high-resolution sleep position and allows to simultaneously apply positional treatment; (2) test the smartphone application at home coupled with a pulse oximeter; and (3) explore the potential of this tool to detect the positional occurrence of desaturation events. The results show how the 'SleepPos' app successfully determined the sleep position and revealed positional patterns of occurrence of desaturation events. The 'SleepPos' app also succeeded in applying positional therapy and preventing the subjects from sleeping in the supine sleep position. This study demonstrates how smartphones are capable of reliably monitoring high-resolution sleep position and provide useful clinical information about the positional occurrence of desaturation events.

Enhanced Monitoring of Sleep Position in Sleep Apnea Patients: Smartphone Triaxial Accelerometry Compared with Video-Validated Position from Polysomnography.

Poor sleep quality is a risk factor for multiple mental, cardiovascular, and cerebrovascular diseases. Certain sleep positions or excessive position changes can be related to some diseases and poor sleep quality. Nevertheless, sleep position is usually classified into four discrete values: supine, prone, left and right. An increase in sleep position resolution is necessary to better assess sleep position dynamics and to interpret more accurately intermediate sleep positions. This research aims to study the feasibility of smartphones as sleep position monitors by (1) developing algorithms to retrieve the sleep position angle from smartphone accelerometry; (2) monitoring the sleep position angle in patients with obstructive sleep apnea (OSA); (3) comparing the discretized sleep angle versus the four classic sleep positions obtained by the video-validated polysomnography (PSG); and (4) analyzing the presence of positional OSA (pOSA) related to its sleep angle of occurrence. Results from 19 OSA patients reveal that a higher resolution sleep position would help to better diagnose and treat patients with position-dependent diseases such as pOSA. They also show that smartphones are promising mHealth tools for enhanced position monitoring at hospitals and home, as they can provide sleep position with higher resolution than the gold-standard video-validated PSG.

Assessment of trunk flexion in arm reaching tasks with electromyography and smartphone accelerometry in healthy human subjects.

Trunk stability is essential to maintain upright posture and support functional movements. In this study, we aimed to characterize the muscle activity and movement patterns of trunk flexion during an arm reaching task in sitting healthy subjects and investigate whether trunk stability is affected by a startling acoustic stimulus (SAS). For these purposes, we calculated the electromyographic (EMG) onset latencies and amplitude parameters in 8 trunk, neck, and shoulder muscles, and the tilt angle and movement features from smartphone accelerometer signals recorded during trunk bending in 33 healthy volunteers. Two-way repeated measures ANOVAs were applied to examine the effects of SAS and target distance (15 cm vs 30 cm). We found that SAS markedly reduced the response time and EMG onset latencies of all muscles, without changing neither movement duration nor muscle recruitment pattern. Longer durations, higher tilt angles, and higher EMG amplitudes were observed at 30 cm compared to 15 cm. The accelerometer signals had a higher frequency content in SAS trials, suggesting reduced movement control. The proposed measures have helped to establish the trunk flexion pattern in arm reaching in healthy subjects, which could be useful for future objective assessment of trunk stability in patients with neurological affections.

Automatic Silence Events Detector from Smartphone Audio Signals: A Pilot mHealth System for Sleep Apnea Monitoring at Home.

Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Recently, mHealth tools are being proposed to screen OSA patients at home. In this work, we analyzed full-night audio signals recorded with a smartphone microphone. Our objective was to develop an automatic detector to identify silence events (apneas or hypopneas) and compare its performance to a commercial portable system for OSA diagnosis (ApneaLink™, ResMed). To do that, we acquired signals from three subjects with both systems simultaneously. A sleep specialist marked the events on smartphone and ApneaLink signals. The automatic detector we developed, based on the sample entropy, identified silence events similarly than manual annotation. Compared to ApneaLink, it was very sensitive to apneas (detecting 86.2%) and presented an 83.4% positive predictive value, but it missed about half the hypopnea episodes. This suggests that during some hypopneas the flow reduction is not reflected in sound. Nevertheless, our detector accurately recognizes silence events, which can provide valuable respiratory information related to the disease. These preliminary results show that mHealth devices and simple microphones are promising non-invasive tools for personalized sleep disorders management at home.

Automatic Event Detector from Smartphone Accelerometry: Pilot mHealth Study for Obstructive Sleep Apnea Monitoring at Home.

Obstructive sleep apnea (OSA) is a common disorder with a low diagnosis ratio, leaving many patients undiagnosed and untreated. In the last decades, accelerometry has been found to be a feasible solution to obtain respiratory activity and a potential tool to monitor OSA. On the other hand, many smartphone-based systems have already been developed to propose solutions for OSA monitoring and treatment. The objective of this work was to develop an automatic event detector based on smartphone accelerometry and pulse oximetry, and to assess its ability to detect thoracic movements. It was validated with a commercial OSA monitoring system at home. Results of this preliminary pilot study showed that the proposed event detector for accelerometry signals is a feasible tool to detect abnormal respiratory events, such as apneas and hypopneas, and has potential to be included in smartphone-based systems for OSA assessment.

Onset Detection to Study Muscle Activity in Reaching and Grasping Movements in Rats.

EMG signals reflect the neuromuscular activation patterns related to the execution of a certain movement or task. In this work, we focus on reaching and grasping (R&G) movements in rats. Our objective is to develop an automatic algorithm to detect the onsets and offsets of muscle activity and use it to study muscle latencies in R&G maneuvers. We had a dataset of intramuscular EMG signals containing 51 R&G attempts from 2 different animals. Simultaneous video recordings were used for segmentation and comparison. We developed an automatic onset/offset detector based on the ratio of local maxima of Teager-Kaiser Energy (TKE). Then, we applied it to compute muscle latencies and other features related to the muscle activation pattern during R&G cycles. The automatic onsets that we found were consistent with visual inspection and video labels. Despite the variability between attempts and animals, the two rats shared a sequential pattern of muscle activations. Statistical tests confirmed the differences between the latencies of the studied muscles during R&G tasks. This work provides an automatic tool to detect EMG onsets and offsets and conducts a preliminary characterization of muscle activation during R&G movements in rats. This kind of approaches and data processing algorithms can facilitate the studies on upper limb motor control and motor impairment after spinal cord injury or stroke.