Systems for evaluating the respiratory function of an individual using forced oscillation technique (FOT) oscillometry include a blower controlled so as to apply FOT pressure oscillations on top of a low amplitude offset pressure. A controller continually adjusts the rotational speed of the blower to maintain a targeted time-varying pressure profile in the breathing air provided to the patient.

A patient monitoring system is for a patient, and may include a base and a frame extending upwardly. The patient monitoring system may include a weight sensor carried by the base, a pair of handrails carried by the frame to be grasped by the patient, and a pair of impedance sensors to be attached to the patient while the patient is on the weight sensor. The patient monitoring system may have a controller coupled to the pair of impedance sensors and the weight sensor and configured to sense a lung impedance of the patient, sense a weight of the patient, and determine whether the patient is experiencing CHF based upon the lung impedance and the weight of the patient.

A patient monitoring system is for a patient, and may include a base and a frame extending upwardly. The patient monitoring system may include a weight sensor carried by the base, a pair of handrails carried by the frame to be grasped by the patient, and a pair of impedance sensors to be attached to the patient while the patient is on the weight sensor. The patient monitoring system may have a controller coupled to the pair of impedance sensors and the weight sensor and configured to sense a lung impedance of the patient, sense a weight of the patient, and determine whether the patient is experiencing CHF based upon the lung impedance and the weight of the patient.

Various aspects of the present disclosure are directed toward apparatuses, systems, and methods for supporting components of an implantable medical device. The apparatuses, systems, and methods may include a first electrode and a second electrode and a scaffold assembly configured to support the first electrode and the second electrode.

The present invention relates to an automated stimulation device for inducing a tactile inter-stimulus onset asynchrony (ISOA) effect in a subject suffering from apnea, bradycardia and/or hypoxia, the device comprising at least two actuators configured for contacting a body portion of the subject, and interspaced for producing an apparent tactile movement in the subject upon sequential induction of actuation, wherein the duration of the actuations and the overlap in actuation time between the at least two actuators is controlled to attain an inter stimulus onset asynchrony (ISOA).

A valve housing for use in an apparatus for measuring inspiratory and expiratory lung pressure, the valve housing comprising a first body portion which defines a mouthpiece; a second body portion coupled to the first body portion and which defines a port; a filter medium disposed between the first and second body portions; a pair of spaced apart valve support bodies defined by one of the first and second body portions, wherein each valve support body defines an air flow conduit that includes a support frame disposed within the conduit, the support frame having a first side and a second side; and a pair of valves, wherein the valves are selectively secured to the respective first sides of the support frame or are secured to the respective second sides of the support frame, wherein when the valves are secured to the first sides of the support frame, they open to permit air to exit the valve housing via the air flow conduits, but close to prevent air being drawn into the valve housing from the external environment; and when the valves are secured to the second sides of the support frame, they open to permit air to enter into the valve housing via the air flow conduits from an external environment, but close to prevent air within the housing from exiting the housing.

Active auscultation may be used to determine organ (e.g., lung or heart) characteristics of users. An acoustic or piezo-electric signal (e.g., a pulse, a tone, and/or a broadband pulse) may be projected into an animal (typically human) body or thorax. The signal interacts with the body, or lungs, and in some cases may induce resonance within the body/lungs. A resultant signal may be emitted from the body which may be analyzed to determine, for example, a lung's resonant frequency or frequencies and/or how the sound is otherwise absorbed, reflected, or modified by the body. This information may be indicative of lung characteristics such as lung capacity, a volume of air trapped in the lungs, and/or the presence of COPD.

Active auscultation may be used to determine organ (e.g., lung or heart) characteristics of users. An acoustic or piezo-electric signal (e.g., a pulse, a tone, and/or a broadband pulse) may be projected into an animal (typically human) body or thorax. The signal interacts with the body, or lungs, and in some cases may induce resonance within the body/lungs. A resultant signal may be emitted from the body which may be analyzed to determine, for example, a lung's resonant frequency or frequencies and/or how the sound is otherwise absorbed, reflected, or modified by the body. This information may be indicative of lung characteristics such as lung capacity, a volume of air trapped in the lungs, and/or the presence of COPD.

In a medical ventilator system, a ventilator (10) delivers ventilation to a ventilated patient (12). Sensors (24, 26) acquire airway pressure and air flow data for the ventilated patient. A probabilistic estimator module (40) estimates respiratory parameters of the ventilated patient by fitting a respiration system model (50) to a data set comprising the acquired airway pressure and air flow data using probabilistic analysis, such as Bayesian analysis, in which the respiratory parameters are represented as random variables. A display component (22) displays the estimated respiratory parameters of the ventilated patient along with confidence or uncertainty data comprising or derived from probability density functions for the random variables representing the estimated respiratory parameters.

In a medical ventilator system, a ventilator (10) delivers ventilation to a ventilated patient (12). Sensors (24, 26) acquire airway pressure and air flow data for the ventilated patient. A probabilistic estimator module (40) estimates respiratory parameters of the ventilated patient by fitting a respiration system model (50) to a data set comprising the acquired airway pressure and air flow data using probabilistic analysis, such as Bayesian analysis, in which the respiratory parameters are represented as random variables. A display component (22) displays the estimated respiratory parameters of the ventilated patient along with confidence or uncertainty data comprising or derived from probability density functions for the random variables representing the estimated respiratory parameters.