Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

A novel parameter for the prediction of rupture risk of cerebral aneurysms based on morphology

Neurosurgeons often encounter dilemmas in the clinical management of cerebral aneurysms owing to an uncertainty of their rupture status and rupture risk. This study evaluates the influence of natural frequency of an aneurysm, as a novel morphological parameter to understand and analyze rupture status and risk prediction. In this work, we employ the natural frequency of 20 idealized and 50 patient specific aneurysms. The natural frequency of patient specific aneurysms is then compared against their rupture status. A strong correlation was observed between various morphological indicators and natural frequency for ideal and patient specific geometries. A statistical analysis with both Mann Whitney U test and T-test for rupture status against natural frequency has given a p-value less than 0.01 indicating a strong correlation between them. The correlation of morphological parameters with natural frequency from Pearson correlation coefficient and T-test suggests a holistic reflection of their effects on the natural frequency of an aneurysm. Thus, natural frequency could be a good indicator to discern the rupture potential of an aneurysm. The correlation between rupture status and natural frequency makes it a novel parameter that can differentiate between ruptured and unruptured patient specific aneurysms.

Contactless screening for sleep apnea with breathing vibration signals based on modified U-Net

BACKGROUND

Obstructive sleep apnea (OSA) is a chronic sleep disorder characterized by frequent cessations or reductions of breathing during sleep. Polysomnography (PSG) is a definitive diagnostic tool for OSA. The costly and obtrusive nature of PSG and poor access to sleep clinics have created a demand for accurate home-based screening devices.

METHODS

This paper proposes a novel OSA screening method based solely on breathing vibration signals with a modified U-Net, allowing patients to be tested at home. Sleep recordings over a whole night are collected in a contactless manner, and sleep apnea-hypopnea events are labeled by a deep neural network. The apnea-hypopnea index (AHI) calculated from events estimation is then used to screen for the apnea. The performance of the model is tested by event-based analysis and comparing the estimated AHI with the manually obtained values.

RESULTS

The accuracy and sensitivity of sleep apnea events detection are 97.5% and 76.4%, respectively. The mean absolute error of AHI estimation for the patients is 3.0 events/hour. The correlation between the ground truth AHI and predicted AHI shows an R² of 0.95. In addition, 88.9% of all participants are classified into correct AHI categories.

CONCLUSIONS

The proposed scheme has great potential as a simple screening tool for sleep apnea. It can accurately detect potential OSA and help the patients to be referred for differential diagnosis of home sleep apnea test (HSAT) or polysomnographic evaluation.

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

Possibilities and limits of using gyroscopic sensors in the diagnosis of progression of osteoarthritis and femoroacetabular impingement syndrome

Osteoarthritis is the most common type of degenerative joint disease and affects millions of people. In this paper, we propose a non-obtrusive and straightforward method to assess the progression of osteoarthritis. In standard medicine praxis, osteoarthritis is observed with X-rays. In this study, we use widely available wearable sensors with gyroscopes to make the observation. Two novel methods are proposed for gyroscope data processing. A small-scale study has shown that these methods can be used to monitor osteoarthritis's progression, and to differentiate between healthy subjects and subjects with femoroacetabular impingement syndrome.