The invention provides a system and method for determining a respiratory effort for a subject. The method comprises obtaining a relaxed signal representing a subject breathing in a relaxed manner and a forced signal representing a subject breathing in a forced manner. The relaxed signal is then smoothed over a first averaging window and the forced signal is smoothed over a second averaging window, wherein the first averaging window is longer than the second averaging window. Based on the smoothed relaxed signal and the smoothed forced signal, a respiratory effort can thus be determined.

A device for characterizing particles of exhaled air. The device comprises an inlet line directed towards an outer environment with a filter for filtering particles. The inlet line is fluidly connected to a breathing line which comprises an interface through which air is breathable. A measurement line is fluidly connected to the breathing line and is fluidly connected to a particle measurement device for determining a parameter corresponding to the particles of the exhaled air. An inventive method comprises the following steps: Directing ex- haled air to a particle measurement device and determining a parameter corresponding to the particles of exhaled air, the parameter being at least one of the following parameters: Particle number, particle concentration (density), particle diameter, particle mass, particle size distribution, particle mass distribution, particle mass concentration, particle number concentration.

A respiration promoting apparatus and a method for promoting respiration for coordinately stimulating a phrenic nerve of a patient for activating a diaphragm of the patient by a pulsating field that is produced by interference stimulation principles.

An electrochemical sensor arrangement (10) for a breath alcohol measuring device (100), to a corresponding breath alcohol measuring device (100) as well as to a process for determining a vitality of electrodes of an electrochemical sensor. The electrochemical sensor arrangement comprises an electrochemical sensor with at least two electrodes (12, 14). The electrochemical sensor arrangement further comprises a heat source (16). The heat source is arranged such that it, upon activation, selectively heats one of the electrodes (12) of the electrochemical sensor.

Vision-based stimulus monitoring and response systems and methods are presented, wherein detection, via image(s) of a patient through an external stimulus, such as a caregiver, prompts analysis of the response of the patient, via secondary patient sensors or via analysis of patient image(s), to determine an autonomic nervous system (ANS) state.

A self-adaptive multi-scale respiratory monitoring method based on a camera, relates to the technical field of video image signal identification processing, in order to solve the defect that the local optimal respiratory signal and the global optimal respiratory signal cannot be acquired by single image scale, a method was provided: (1) acquiring a respiratory monitoring object in real time;(2) performing multi-scale regular pre-segmentation on a video image, performing local respiratory signal identification and extraction on each unit area pre-segmented under each scale respectively, and defining the unit area with local respiratory signal output as a target area; and (3) comparing local respiratory signals extracted from the target area pre-segmented under each scale, determining an optimal segmentation scale, and taking a local respiratory signal extracted from the target area under the optimal segmentation scale as a monitoring respiratory signal output. The reliability is improved, and intelligent monitoring is realized.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing splinting activity detection. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform splinting activity detection using at least one of splinting activity detection machine learning models, observed inspiration-expiration waveform pattern, and expected inspiration-expiration waveform patterns.

Embodiments herein relate to devices and methods for assessing pulmonary congestion using optical sensing techniques. In an embodiment, a pulmonary congestion monitoring device can be included having a first optical emitter, wherein the first optical emitter can be configured to emit light at a first wavelength, such as at a near-infrared wavelength or an ultraviolet wavelength. The monitoring device can also include a first optical detector configured to detect incident light. The first optical emitter and the first optical detector can be separated by a distance of 1 centimeters (cm) to 10 cm. The monitoring device can be configured so that the light from the first optical emitter propagates through at least one of a lung tissue and an airway tissue. The monitoring device can also be configured to use detected incident light to determine a congestion status of the lung tissue. Other embodiments are also included herein.

Embodiments herein relate to devices and methods for assessing pulmonary congestion using optical sensing techniques. In an embodiment, a pulmonary congestion monitoring device can be included having a first optical emitter, wherein the first optical emitter can be configured to emit light at a first wavelength, such as at a near-infrared wavelength or an ultraviolet wavelength. The monitoring device can also include a first optical detector configured to detect incident light. The first optical emitter and the first optical detector can be separated by a distance of 1 centimeters (cm) to 10 cm. The monitoring device can be configured so that the light from the first optical emitter propagates through at least one of a lung tissue and an airway tissue. The monitoring device can also be configured to use detected incident light to determine a congestion status of the lung tissue. Other embodiments are also included herein.

An example method includes receiving one or more physiological signals; detecting an apnea event based on the one or more physiological signals; determining that the apnea event cannot be characterized as one of a normal, OSA (obstructive sleep apnea), CSA (central sleep apnea), or combination OSA/CSA event; and outputting an electrical stimulation as a default based on determining that the apnea event cannot be characterized as a normal event, an OSA event, a CSA event, or combination OSA/CSA events.