70 results about "Respiratory effort" patented technology

Photoplethysmography (PPG) is obtained using one red (e.g., 660 nm) and one infrared (e.g., 880 to 940 nm) light emitting diode with a single photo diode in combination with a pressure transducer thereby allowing both CVP and SpO2 to be measured simultaneously. The system also includes sensors capable of measuring position, angle and/or movement of the sensor or patient. Once the PPG signal is acquired, high pass adaptive and/or notch filtering can be used with one element of the filter from the red and infrared signals used to measure the arterial changes needed to compute SpO2 and the other element of the signal can be used to measure CVP changes.

A system for reducing airway obstructions of a patient may include a ventilator, a control unit, a gas delivery circuit with a proximal end in fluid communication with the ventilator and a distal end in fluid communication with a nasal interface, and a nasal interface. The nasal interface may include at least one jet nozzle, and at least one spontaneous respiration sensor in communication with the control unit for detecting a respiration effort pattern and a need for supporting airway patency. The system may be open to ambient. The control unit may determine more than one gas output velocities. The more than one gas output velocities may be synchronized with different parts of a spontaneous breath effort cycle, and a gas output velocity may be determined by a need for supporting airway patency.

The ventilator of the invention uses a respiratory effort sensor that does not rely on respiratory airflow or pressure for synchronization, such as a device that measures movement of the suprasternal notch in response to respiratory efforts. This makes the ventilator relatively immune to leaks. Furthermore, the mask pressure may be modulated so as to servo-control the respiratory effort signal to be zero. Because effort is servo-controlled to be near zero, it is not necessary for the effort signal to be either linear or calibrated, but merely monotonic on effort. Similarly, it is possible to achieve near 100% assistance without having to know or estimate the resistance and compliance of the patient's respiratory system, as in those systems that provide proportional assist ventilation.

Extraction of components of the a signal captured by an accelerometer, obtaining information about physiological data such as cardiac, respiratory and snoring activity. The extracted signal components are useful for the diagnosis of different types of abnormal respiratory phenomena during sleep (apneas, hypopneas and respiratory efforts associated to micro-arousals).

Disordered breathing events may be classified as central, obstructive or a combination of central an obstructive in origin based on patient motion associated with respiratory effort. Central disordered breathing is associated with disrupted respiration with reduced respiratory effort. Obstructive disordered breathing is associated with disrupted respiration accompanied by respiratory effort. A disordered breathing classification system includes a disordered breathing detector and a respiratory effort motion sensor. Components of the disordered breathing classification system may be fully or partially implantable.

A method of automatically controlling a respiration system for proportional assist ventilation with a control device and with a ventilator. An electrical signal is recorded by electromyography with electrodes on the chest in order to obtain a signal u_emg(t) representing the breathing activity. The respiratory muscle pressure p_mus(t) is determined by calculating it in the control unit from measured values for the airway pressure and the volume flow Flow(t) as well as the patient's lung mechanical parameters. The breathing activity signal u_emg(t) is transformed by means of a preset transformation rule into a pressure signal p_emg(u_emg)(t)) such that the mean deviation of the resulting transformed pressure signal p_emg(t) from the respiratory muscle pressure p_mus(t) is minimized. The respiratory effort pressure p_pat(t) is determined as a weighted mean according to p_pat(t)=a·p_mus(t)+(1−a)·p_emg(t), where a is a parameter selected under the boundary condition 0≦a≦1. The airway pressure p_aw(t) to be delivered is calculated as a function of preselected degrees of assist VA (Volume Assist) and FA (Flow Assist) by sliding adaptation as

wherein t_i is a current point in time and t_i−j, wherein j=1, . . . , n, are previous points in time of a periodical time-discrete sampling, and k_j and h_j, wherein j=1, . . . , n are parameters dependent on resistance (R), elastance (E), positive end-expiratory pressure (PEEP), intrinsic PEEP (iPEEP), Volume Assist (VA) and Flow Assist (FA) and the sampling time Δt, and the ventilator is set by the control unit so as to provide this airway pressure p_aw(t_i)

A method and system for detecting effort events is disclosed. Effort may be determined through feature analysis of the signal as transformed by a continuous wavelet transform, which may be compared against a reference effort measure to trigger an effort event flag that signals the onset and/or severity of an effort event. For example, a respiratory effort measure may be determined based at least in part on a wavelet transform of a photoplethysmograph (PP G) signal and features of the transformed signal. A respiratory reference effort measure may be based at least in part on past values of the respiratory effort measure, and a threshold test may be used to trigger an effort event flag, which may indicate a marked change in respiratory effort exerted by a patient.

An arrangement and method for detecting spontaneous respiratory effort of a patient receiving ventilatory support by a breathing circuit. The nasal cannula control system includes a nasal cannula assembly having two distinct lumens. A different pressure sensor is positioned to detect the pressure difference between each of the two lumens, thereby determining the differential pressure from within the patient's nostrils and within a breathing mask. The nasal cannula control system includes a gas sampling system such that the amount of a monitored gas discharged or exhaled by the patient can be monitored using the same nasal cannula assembly used to generate the differential pressure signal.

An improved ventilator which delivers ventilatory support that is synchronized with the phase of the patients respiratory efforts and guarantees a targeted minimum ventilation. Improved synchronization is achieved through an instantaneous respiratory phase determination process based upon measured respiratory airflow as well as measured respiratory effort using an effort sensor accessory, preferably a suprasternal notch sensor. The ventilator processes a respiratory airflow signal, a respiratory effort signal and their respective rates of change to determine a phase using standard fuzzy logic methods. A calculated pressure amplitude is adjusted based upon the calculated phase and a smooth pressure waveform template to deliver synchronized ventilation.

An apparatus or method can be configured to receive first information indicative of respiratory effort of a subject from a first piezoelectric film sensor and second information indicative of respiratory effort of the subject from a second piezoelectric film sensor, and to process the received first and second information to produce an electronic signal output indicative of respiratory effort of the subject, the processing including averaging the received first information using a first differential amplifier and signal integrator with resistive reset to reduce differential noise and to attenuate common-mode noise, and averaging the received second information using a second differential amplifier and signal integrator with resistive reset to reduce differential noise and to attenuate common-mode noise.

An improved sensor (102) for calorie monitoring in mobile devices, wearables, security, illumination, photography, and other devices and systems uses an optional phosphor-coated broadband white LED (103) to produce broadband light (114), which is then transmitted along with any ambient light to target (125) such as the ear, face, or wrist of a living subject. Some of the scattered light returning from the target to detector (141) is passed through narrowband spectral filter set (155) to produce multiple detector regions, each sensitive to a different narrowband wavelength range, and the detected light is spectrally analyzed to determine a measure of calories, such as calories expended, calories ingested, calorie balance, or rate of calories expended, in part based on a noninvasive measure of respiration, such as respiratory rate, respiratory effort, respiratory depth, or respiratory variability. In one example, variations in concentration in components of the bloodstream over time, such as hemoglobin and water in the arteries, are determined based on the detected light, and the measure of respiration is then determined based on the variations in concentration over time. In the absence of the LED light, ambient light may be sufficient illumination for analysis. The same sensor can provide identifying features of type or status of a tissue target, such as heart rate or heart rate variability, hydration status, sleep state, or even occupancy counting. Calorie monitoring systems incorporating the sensor as well as methods are also disclosed.

A method of creating a noninvasive predictor of both physiologic and imposed patient effort of breathing from airway pressure and flow sensors attached to the patient using an adaptive mathematical model. The patient effort is commonly measured via work of breathing, power of breathing, or pressure-time product of esophageal pressure and is important for properly adjusting ventilatory support for spontaneously breathing patients. The method of calculating this noninvasive predictor is based on linear or non-linear calculations using multiple parameters derived from the above-mentioned sensors.

A device and method for determining and/or monitoring the respiratory effort of a subject are presented. The device comprises a receiving unit for receiving a posture signal of the subject, a breathing signal of the subject, and an electromyography signal of the subject; and a processing unit for determining an electromyography signal based on the posture signal and the breathing signal and for deriving the respiratory effort based on the determined electromyography signal.

A wireless implantable system is provided that is externally powered and comprises of a closed-loop feedback for treating both patients with obstructive and central sleep apnea. A method is provided for treating sleep apnea using an implantable device. The method comprises sensing an inspiration (IN) signal representative of inspiration experienced by a patient from a respiratory surrogate signal and sensing a respiratory effort (RE) signal representative of an amount of effort exerted by the patient during respiration. The method also comprises analyzing the inspiration signal relative to an IN baseline, that corresponds to normal respiratory behavior, to identify an IN indicator, analyzing the RE signal relative to an RE baseline, that corresponds to a normal amount of respiratory exerted by the patient, to identify an RE indicator, declaring a central sleep apnea (CSA) state or an obstructive sleep apnea (OSA) state based on the IN and RE indicators, and delivering at least one of a CSA therapy when the CSA state is declared or an OSA therapy when the OSA state is declared.

The invention discloses a method and system for detecting apnea events based on BCG signals, which are used to accurately detect apnea events during sleep. This method locates the potential occurrence interval of respiratory events by identifying the arousal segment, and then divides the event potential occurrence interval into apnea segment, respiratory effort segment, and arousal segment, and then extracts fine-grained features that can describe the breathing pattern from the three segments. , and finally with the help of machine learning methods, it is judged whether the potential occurrence stage of the event contains an apnea event. The system mainly includes: a signal acquisition module, a data processing module, and a detection result output module. The method and system can automatically and accurately locate the potential occurrence interval of apnea events and automatically divide the interval into three different stages, which facilitates the description of breathing patterns from multiple aspects and in a fine-grained manner, and greatly improves the detection accuracy of apnea events Rate.

Methods and apparatus provide monitoring of a sleep disordered breathing state of a person such as for screening. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) (7304, 7006) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of sleep disordered breathing. The processor may generate a sleep disordered breathing indicator based on the measure(s) of sleep disordered breathing. Some versions may evaluate sensing signal(s) to generate indication(s) of cough event(s) and/or cough type.