A Novel Portable Real-Time Low-Cost Sleep Apnea Monitoring System based on the Global System for Mobile Communications (GSM) Network

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

A Portable Low-Cost Respiration Rate Measurement System for Sleep Apnea Detection

Continuous monitoring of breathing activity is vital in detecting respiratory-based diseases such as obstructive sleep apnea (OSA) and hypopnea. Sleep apnea (SA) is a potentially dangerous sleep problem that occurs when a person's breathing stops and begins periodically while they sleep. In addition, SA interrupts sleep, causing significant daytime sleepiness, weirdness, and irritability. This study aims to design a single inertial measurement unit (IMU) sensor-based system to analyze the respiratory rate of humans. The results of the developed system is validated with the Equivital Wireless Physiological Systems for different activities. Further, the experiment has been designed to identify the optimal sensor placement location for efficient respiration rate estimation during different activities. The performance of the developed model has been quantified using breathing rate estimation accuracy (% BREA) and mean absolute error (MAE). Among all sensor placement locations and activities combinations, a window size of 30sec resulted in the worst performance, whereas a window size ≥ 60sec resulted in a better performance (p-value<0.05). In addition, the performance of the model has been found consistent for window size ≥ 60sec (p-value>0.05). For activity 3 (lying straight), comparably similar performance, 0.52±0.24 and 0.52±0.12 (p-value>0.05) have been depicted by the sensor placement position 3 (Abdomen) and position 1 (chest), respectively. Further, for the other two activities, activity 1 (sitting and working) and activity 2 (sitting straight), the best performance has been depicted as 0.32±0.18, 0.49±0.21 respectively (p-value<0.05), by the sensor placement position 2 (left ribs). This research presents a reliable, cost-effective, portable respiration monitoring system that could detect SA during sleep.

Transformation of chronic disease management: Before and after the COVID-19 outbreak

Adults with chronic diseases often experience a decline in their quality of life along with frequent exacerbations. These diseases can cause anxiety and impose a significant economic burden. Self-management is a crucial aspect of treatment outside of the hospital and can improve quality of life and reduce the financial burden resulting from unexpected hospitalizations. With the COVID-19 pandemic, telehealth has become a vital tool for both medical professionals and patients; many in-person appointments have been canceled due to the pandemic, leading to increased reliance on online resources. This article aimed to discuss various methods of chronic disease management, both traditional self-management and modern telehealth strategies, comparing before and after the COVID-19 outbreak and highlighting challenges that have emerged.

Effect of short-term mindfulness-based stress reduction on sleep quality in male patients with alcohol use disorder

Background

Sleep disturbance is one of the most prominent complaints of patients with alcohol use disorder (AUD), with more than 70% of patients with AUD reporting an inability to resolve sleep problems during abstinence. Mindfulness-based stress reduction (MBSR) has been shown to improve sleep quality and as an alternative therapy to hypnotics for sleep disorders.

Objective

The aim of the present study was to evaluate the effect of short-term MBSR on sleep quality in male patients with AUD after withdrawal.

Methods

A total of 91 male patients with AUD after 2 weeks of routine withdrawal therapy were randomly divided into two groups using a coin toss: the treatment group (n = 50) and the control group (n = 41). The control group was received supportive therapy, and the intervention group added with MBSR for 2 weeks on the basis of supportive therapy. Objective sleep quality was measured at baseline and 2 weeks after treatment using the cardiopulmonary coupling (CPC). Indicators related to sleep quality include total sleep time, stable sleep time, unstable sleep time, rapid eye movement (REM) sleep time, wake-up time, stable sleep latency, sleep efficiency, and apnea index. These indicators were compared by an analysis of covariance (ANCOVA) between the two groups, controlling for individual differences in the respective measures at baseline.

Results

The results showed that there were no significant differences in the age [t (89) = -0.541, P = 0.590), BMI [t (89) = -0.925, P = 0.357], educational status [t (89) = 1.802, P = 0.076], years of drinking [t (89) = -0.472, P = 0.638), daily intake [t (89) = 0.892, P = 0.376], types of alcohol [χ2 (1) = 0.071, P = 0.789], scores of CIWA-AR [t (89) = 0.595, P = 0.554], scores of SDS [t (89) = -1.151, P = 0.253), or scores of SAS [t (89) = -1.209, P = 0.230] between the two groups. Moreover, compared with the control group, the total sleep time [F (1.88) = 4.788, P = 0.031) and stable sleep time [F (1.88) = 6.975, P = 0.010] were significantly increased in the treatment group. Furthermore, the average apnea index in the patients who received MBSR was significantly decreased than in the control group [F (1.88) = 5.284, P = 0.024].

Conclusion

These results suggest that short-term MBSR could improve sleep quality and may serve as an alternative treatment to hypnotics for sleep disturbance in patients with AUD after withdrawal.

Healthcare Monitoring Using Low-Cost Sensors to Supplement and Replace Human Sensation: Does It Have Potential to Increase Independent Living and Prevent Disease?

Continuous monitoring of health status has the potential to enhance the quality of life and life expectancy of people suffering from chronic illness and of the elderly. However, such systems can only come into widespread use if the cost of manufacturing is low. Advancements in material science and engineering technology have led to a significant decrease in the expense of developing healthcare monitoring devices. This review aims to investigate the progress of the use of low-cost sensors in healthcare monitoring and discusses the challenges faced when accomplishing continuous and real-time monitoring tasks. The major findings include (1) only a small number of publications (N = 50) have addressed the issue of healthcare monitoring applications using low-cost sensors over the past two decades; (2) the top three algorithms used to process sensor data include SA (Statistical Analysis, 30%), SVM (Support Vector Machine, 18%), and KNN (K-Nearest Neighbour, 12%); and (3) wireless communication techniques (Zigbee, Bluetooth, Wi-Fi, and RF) serve as the major data transmission tools (77%) followed by cable connection (13%) and SD card data storage (10%). Due to the small fraction (N = 50) of low-cost sensor-based studies among thousands of published articles about healthcare monitoring, this review not only summarises the progress of related research but calls for researchers to devote more effort to the consideration of cost reduction as well as the size of these components.