Open Access

Background: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL). Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM). Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy. Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.

In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases.

Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.

BACKGROUND: Future of digital public health and smart cities is interwoven and deeply linked. Citizen's and pet's conditions in their urban environment are critical for managing urbanization challenges and digital transformation. Inter- and Intra-connectivity of humans and animals take place in a dynamic space. In this environment, each one can share feelings and news over social media, and report an event happening at any time passively or actively via sensors or multimedia channels, respectively. One Digital Health (ODH) proposes a framework for collecting, managing, analyzing data, and supporting health-oriented policy development and implementation. Accident and Emergency Informatics gives tools to identify and manage overtime hazards and disruptive events, their victims and collaterals. OBJECTIVE: We aim to show how ODH framework, through implementing dynamic point of perceptions, supports the analysis of a use case involving a human and an animal in a technological environment-based urban, i.e., smart environment. METHODS: We describe an example of One Digital Health intervention wherein Accident and Emergency Informatics mechanisms run in the background. A One Digital Health Intervention is the implementation of a set of digital functionalities designed and deployed to (1) support specific initiatives that address human, animal, and environmental systems' needs and challenges; (2) assess and study these systems' outcomes and effects, and collect related data; (3) select timely metrics for the outcomes of multi-criteria decision analyses. This example intervention is based on the daily journey of two personas: Tracy (a human) and Mego (Tracy's dog). They live in a metropolis, and their activities are monitored and analyzed with IoT sensors, devices, and tools for preventing and managing any health-related abnormality. RESULTS: We built an example of an ODH Intervention summary table showing examples of "how to" analyze activities of daily living as part of an ODH Intervention. For each activity, its relations to the ODH dimensions are scored, and the relevant technical fields are evaluated in the light of the FAIR (Findable, Accessible, Interoperable, Reusable) principles prism. CONCLUSIONS: The example showcased of ODH intervention provides the basics to build real-world data-based research in a FAIR (Findable, Accessible, Interoperable, Reusable) context to improve continuous health monitoring policies and systems, also for enhancing emergency management. One Digital Health framework provides medical and environmental informaticians, decision-makers, and citizens with tools for improving their daily actions. The additional, integrating Accident and Emergency Informatics layer allows them to better set forth their preparedness and response to potentially acute health-related events. The whole data management cycle must also be processed in a FAIRness way.