Multiparameter Monitoring with a Wearable Cardioverter Defibrillator

A wearable cardioverter-defibrillator (WCD) is a temporary treatment option for patients at high risk for sudden cardiac death (SCD) and for patients who are temporarily not candidates for an implantable cardioverter defibrillator (ICD). In addition, the need for telemedical concepts in the detection and treatment of heart failure (HF) and its arrhythmias is growing. The WCD has evolved from a shock device detecting malignant ventricular arrhythmias (VA) and treating them with shocks to a heart-failure-monitoring device that captures physical activity and cardioacoustic biomarkers as surrogate parameters for HF to help the treating physician surveil and guide the HF therapy of each individual patient. In addition to its important role in preventing SCD, the WCD could become an important tool in heart failure treatment by helping prevent HF events by detecting imminent decompensation via remote monitoring and monitoring therapy success.

Digital Health: Implications for Heart Failure Management

Digital health encompasses the use of information and communications technology and the use of advanced computing sciences in healthcare. This review covers the application of digital health in heart failure patients, focusing on teleconsultation, remote monitoring and apps and wearables, looking at how these technologies can be used to support care and improve outcomes. Interest in and use of these technologies, particularly teleconsultation, have been accelerated by the coronavirus disease 2019 pandemic. Remote monitoring of heart failure patients, to identify those patients at high risk of hospitalisation and to support clinical stability, has been studied with mixed results. Remote monitoring of pulmonary artery pressure has a consistent effect on reducing hospitalisation rates for patients with moderately severe symptoms and multiparameter monitoring shows promise for the future. Wearable devices and apps are increasingly used by patients for health and lifestyle support. Some wearable technologies have shown promise in AF detection, and others may be useful in supporting self-care and guiding prognosis, but more evidence is required to guide their optimal use. Support for patients and clinicians wishing to use these technologies is important, along with consideration of data validity and privacy and appropriate recording of decision-making.

Mud Volcanoes

The eruption of mud and magma can be influenced by earthquakes and reports date back more than 2000 years. Dozens of examples of eruptions have now been documented in response to both static and dynamic stresses from earthquakes. Already erupting systems are most sensitive to earthquakes compared to initiating new eruptions. Multiple plausible mechanisms have been proposed for triggering eruptions including disrupting particle-rich materials, mobilizing bubbles, or changing permeability—changes may occur both within and outside the reservoir hosting the materials that ultimately erupt. Using historical examples of triggered mud eruptions, we explain why it is unlikely that the Sidoarjo mud flow (sometimes nicknamed “Lusi”) was initiated by an earthquake. As multiparameter monitoring of volcanoes expands, it may eventually be possible to identify triggering mechanisms and how seismic waves influence magma and mud mobility in field settings.

Data fusion model of technical condition multiparameter monitoring

Разрабатывается модель хранения пространственно-временных событий и представления технического состояния объекта мониторинга для дальнейшего использования при прогнозной аналитике технического обслуживания на основе графовой СУБД — Neo4j.

Principles of organizing earthquake forecasting based on multiparameter sensor-WEB monitoring data

The paper describes an approach that allows, basing on the data of multiparameter monitoring of atmospheric and ionospheric parameters and using ground-based and satellite measurements, to select from the data stream a time interval indicating the beginning of the final stage of earthquake preparation, and finally using intelligent data processing to carry out a short-term forecast for a time interval of 2 weeks to 1 day before the main shock. Based on the physical model of the lithosphere-atmospheric-ionospheric coupling, the precursors are selected, the ensemble of which is observed only during the precursory periods, and their identification is based on morphological features determined by the physical mechanism of their generation, and not on amplitude selection based on statistical data processing. Basing on the developed maquette of the automatic processing service, the possibility of real-time monitoring of the situation in a seismically active region will be demonstrated using the territory of the Kamchatka region and the Kuril Islands.

Toward a New Approach in Wearable Devices in Safety Monitoring: Miniaturization and 3D Space Utilization

This commentary is focused on the requirements and general strategy of designing a multiparameter monitoring wrist-worn prototype. The solution is based on an innovative hardware approach to ensure the safety of working conditions through environmental parameter measurement. In some cases, exposure to environmental parameters for a long time can endanger an individual’s health (e.g., exposure to toxic gases or sound level beyond a certain threshold). Therefore, measuring the environmental elements can protect individuals’ health as well. In this work, we emphasize that a new approach and strategy in wearable devices, multiparameter monitoring, miniaturization, sensor integration, data fusion, and system adoption within the Internet of Things (IoT) is necessary according to today’s demands.