Presented are concepts for monitoring cardio-respiratory function of a patient. One such concept comprises detecting light or sound from the sublingual vasculature using a sublingual sensor unit adapted to be positioned at a sublingual vasculature of the patient's tongue and to generate a sensor output signal based on the detected light or sound. A processing unit adapted to receive at least one of the sensor unit output signal, wherein the sensor unit and the processing unit are arranged to analyze the venous component in the sensor output signal. An output signal from the sublingual sensor may then be used to provide information on cardio-respiratory parameters like respiration rate and respiration rate variability, for example.

A system includes a processor and an output device. The processor is configured to: (a) receive electrical signals indicative of measured positions of (i) one or more chest position sensors attached externally to a chest of a patient, and (ii) one or more back position sensors attached externally to a back of the patient; (b) compare between (i) a first shift between the measured positions and respective predefined positions of the one or more chest position sensors, and (ii) a second shift between the measured positions and respective predefined positions of the one or more back position sensors; and (c) produce an alert in response to detecting a discrepancy between the first and second shifts. The output device is configured to output the alert to a user.

A wearable patch and method for automatically monitoring, screening, and/or reporting events related to one or more health conditions (e.g., sleeping or breathing disorders, physical activity, arrhythmias) of a subject and/or one or more breathing conditions (e.g., ventilatory threshold) of a subject.

A contactless cardiopulmonary signal estimation method and apparatus are provided. A cardiopulmonary signal estimation apparatus may estimate a cardiopulmonary signal of a user from a heartbeat signal and a respiratory signal of the user in response to movement of a chest based on a cardiopulmonary exercise of the user.

A contactless cardiopulmonary signal estimation method and apparatus are provided. A cardiopulmonary signal estimation apparatus may estimate a cardiopulmonary signal of a user from a heartbeat signal and a respiratory signal of the user in response to movement of a chest based on a cardiopulmonary exercise of the user.

The present disclosure provides a wakefulness determination method for accurately determining wakefulness. The wakefulness determination method uses a respiration sensor that obtains respiratory data about respiration of a seated occupant, a calculation unit that calculates the respiratory data obtained from the respiration sensor, and a controller including a determination unit that determines a state of the seated occupant. The wakefulness determination method includes: obtaining, by the respiration sensor, respiratory data of the seated occupant; calculating, by the calculation unit, a degree of change in respiration from the obtained respiratory data; and determining, by the determination unit, wakefulness of the seated occupant by using a Bayesian filter where a probability of occurrence of drowsiness in the seated occupant for the degree of change in respiration is taken as a likelihood and the likelihood is multiplied by a prior probability of occurrence of drowsiness.

The present disclosure provides a wakefulness determination method for accurately determining wakefulness. The wakefulness determination method uses a respiration sensor that obtains respiratory data about respiration of a seated occupant, a calculation unit that calculates the respiratory data obtained from the respiration sensor, and a controller including a determination unit that determines a state of the seated occupant. The wakefulness determination method includes: obtaining, by the respiration sensor, respiratory data of the seated occupant; calculating, by the calculation unit, a degree of change in respiration from the obtained respiratory data; and determining, by the determination unit, wakefulness of the seated occupant by using a Bayesian filter where a probability of occurrence of drowsiness in the seated occupant for the degree of change in respiration is taken as a likelihood and the likelihood is multiplied by a prior probability of occurrence of drowsiness.

During a focused-ultrasound or other non-invasive treatment procedure, the motion of the treatment target or other object(s) of interest can be tracked in real time based on (i) the comparison of treatment images against a reference library of images that have been acquired prior to treatment for the anticipated range of motion and have been processed to identify the location of the target or other object(s) therein and (ii) complementary information associated with the stage of the target motion during treatment.

During a focused-ultrasound or other non-invasive treatment procedure, the motion of the treatment target or other object(s) of interest can be tracked in real time based on (i) the comparison of treatment images against a reference library of images that have been acquired prior to treatment for the anticipated range of motion and have been processed to identify the location of the target or other object(s) therein and (ii) complementary information associated with the stage of the target motion during treatment.

Provided is a method for identifying a coughing event, the method including obtaining, from a sensor included in a monitoring device worn by a patient, a plurality of digital voltage values, analyzing the plurality of digital voltage values to identify a physiological event based on (i) a change in magnitude of the digital voltage values and (ii) a duration of the change in the magnitude of the digital voltage values, the change in the magnitude of the digital voltage values and the duration of the change in the magnitude of the digital voltage values representing a first point in a two-dimensional feature space, and categorizing the identified physiological event as the coughing event based on a threshold hyperplane existing in the two-dimensional feature space.