A New Era in Sleep Monitoring: The Application of Mobile Technologies in Insomnia Diagnosis

Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning

Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants' effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.

An Investigation of Patient Decisions to Use eHealth

eHealth service has received increasing attention. Patients can consult online doctors via the Internet, and then physically visit the doctors for further diagnosis and treatments. Although extant research has focused on the adoption of eHealth services, the decision-making process from online to offline health services still remains unclear. This study aims to examine patients’ decisions to use online and offline health services by integrating the extended valence framework and the halo effect. By analyzing 221 samples with online consultation experiences, the results show that trust significantly influences perceived benefits and perceived risks, while trust, perceived benefits, and perceived risks significantly influence the intention to consult. The intention to consult positively influences the intention to visit. Considering the moderating effects of payment types, the influence of perceived risks on the intention to consult is larger for the free group than for the paid group. The findings are useful to better understand patients’ decisions to use eHealth.

Periodic leg movement (PLM) monitoring using a distributed body sensor network

Wireless sensors networks represent the architecture of choice for distributed monitoring due to the ease of deployment and configuration. We developed a distributed sleep monitoring system which combines wireless inertial sensors SP-10C by Sensoplex controlled by a custom smartphone application as an extension of the polysomnographic (PSG) monitor SOMNOscreen plus from Somnomedics. While existing activity monitors are wired to the SOMNOscreen, our system allows the use of wireless inertial sensors to improve user's comfort during sleep. The system is intended for monitoring of periodic leg movements (PLM) and user's activity during sleep. Wireless sensors are placed on ankle and toes of the foot in a customized sock. An Android app communicates with wireless sensors over Bluetooth Smart (BTS) link and streams 3D accelerometer values, 4D unit quaternion values and timestamps. In this paper we present a novel method of synchronization of data streams from PSG and inertial sensors, and original method of detection of PLM events. The system was tested using five experiments of simulated PLM, and achieved 96.51% of PLM detection accuracy.