Abstract: A patient monitoring device includes at least one physiological sensor (32) configured to acquire at least one measured value for a patient of at least one monitored physiological variable. A cardiovascular (CV), pulmonary, or cardiopulmonary (CP) modeling component (42) includes a microprocessor pro-programmed to: receive the measured values of the at least one monitored physiological variable; receive a value for at least one patient-specific medical image parameter generated from at least one medical image of the patient; compute values for the patient of unmonitored physiological variables based on the measured values for the patient of the monitored physiological variables and the patient-specific medical image parameter; and at least one of (1) display the computed values and (2) control a therapy device delivering therapy to the patient based on the computed values.

Abstract: Respiratory variables are estimated on a per-breath basis from airway pressure and flow data acquired by airway pressure and flow sensors (20, 22). A breath detector (28) detects a breath interval. A per-breath respiratory variables estimator (30) fits the airway pressure and flow data over the detected breath interval to an equation of motion of the lungs relating airway pressure, airway flow, and a single-breath parameterized respiratory muscle pressure profile (40, 42) to generate optimized parameter values for the single-breath parameterized respiratory muscle pressure profile. Respiratory muscle pressure is estimated as a function of time over the detected breath interval as the single-breath parameterized respiratory muscle pressure profile with the optimized parameter values, and may for example be displayed as a trend line on a display device (26, 36) or integrated (32) to generate Work of Breathing (WoB) for use in adjusting settings of a ventilator (10).

Abstract: A Moving Window Least Squares (MWLS) approach is applied to estimate respiratory system parameters from measured air flow and pressure. In each window, elastance Ers (or resistance Rrs) is first estimated, and a Kalman filter may be applied to the estimate. This is input to a second estimator that estimates R (or E), to which a second Kalman filter may be applied. Finally, the estimated Ers and Rrs are used to calculate muscle pressure Pmus(t) in the time window. A system comprises a ventilator (100), an airway pressure sensor (112), and an air flow sensor (114), and a respiratory system analyzer (120) that performs the MWLS estimation. Estimated results may be displayed on a display (110) of the ventilator or of a patient monitor. The estimated Pmus(t) may be used to reduce patient-ventilator dyssynchrony, or integrated to generate a Work of Breathing (WOB) signal for controlling ventilation.

Abstract: Disclosed herein are approaches for monitoring a patient in real-time for ARDS development and providing a biomarker-driven ventilation therapy recommendation tool based on the correlation of various therapy patterns and an ARDS biomarker score. When the ARDS biomarker indicates that a patient has a high ARDS risk, the recommendation tool suggests possible therapy routes based on clinical practice. In response to a high score output by the ARDS detection model, the tool outputs a recommendation to initiate a lung protective ventilation strategy (Low Tidal Volume, high Positive End-Expiratory Pressure (PEEP)). A high ARDS score is recognized to be predictive of the appropriateness of such therapy up to several hours before such intervention is typically initiated under current clinical practices.

Abstract: A patient monitor (12) includes a display (14) and sensors (20, 22, 24) reading vital signs of a human subject. In a cardiovascular early warning scoring (cEWS) method, the human subject is classified using a plurality of cardiovascular deterioration classifiers (52, 152, 54, 56, 58) each trained respective to a different type of cardiovascular deterioration. The cardiovascular deterioration classifiers operate on a set of inputs characterizing the human subject including the at least one cardiovascular parameter (42) and the at least one respiratory parameter (44), such as tidal volume read by an airflow sensor (24). The cardiovascular early warning scores for the different types of cardiovascular deterioration are outputted on the display of the patient monitor. An empirical myocardial ischemia classifier (52) may be combined with at least one additional ischemia score generated by applying a set of rules (160) or a physiological model (162).

Abstract: A medical ventilator (10) performs a method including: receiving measurements of pressure of air inspired by or expired from a ventilated patient (12) operatively connected with the medical ventilator; receiving measurements of air flow into or out of the ventilated patient operatively connected with the medical ventilator; dividing a breath time interval into a plurality of fitting regions (60); and simultaneously estimating respiratory system's resistance and compliance or elastance, and respiratory muscle pressure in each fitting region by fitting to a time series of pressure and air flow samples in that fitting region. In one approach, the fitting includes parameterizing the respiratory muscle pressure by a continuous differentiable function, such as a polynomial function, over the fitting region.

Abstract: A mechanical ventilator (10) is connected with a ventilated patient (12) to provide ventilation in accordance with ventilator settings of the mechanical ventilator. Physiological values (variables) are acquired for the ventilated patient using physiological sensors (32). A ventilated patient cardiopulmonary (CP) model (40) is fitted to the acquired physiological variables values to generate a fitted ventilated patient CP model by fine-tuning its parameters (50). Updated ventilator settings are determined by adjusting model ventilator settings of the fitted ventilated patient CP model to minimize a cost function (60). The updated ventilator settings may be displayed on a display component (22) as recommended ventilator settings for the ventilated patient, or the ventilator settings of the mechanical ventilator may be automatically changed to the updated ventilator settings so as to automatically control the mechanical ventilator.

Abstract: 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.

Abstract: A method includes obtaining a first physiological parameter indicative of a non-invasively measured airway pressure of a subject, obtaining a second physiological parameter indicative of a non-invasively measured air flow into the lungs of the subject, and estimating a third physiological parameter indicative of an intra-pleural pressure of the subject based on the first and second physiological parameters and generating a signal indicative thereof. An other method includes obtaining a first physiological parameter indicative of a non-invasively estimated intra-pleural pressure of a subject, determining a second physiological parameter indicative of a lung volume of the subject that is based on a third physiological parameter indicative of a non-invasively measured air flow into the lungs of the subject, and determining a work of breathing based on the first and second physiological parameters and generating a signal indicative thereof.

Abstract: A method includes receiving patient data for a patient in electronic format. The patent data includes information that identifies the patient. The method further includes labeling the patient data with a guaranteed unique identifier. The method further includes at least one of removing or visually obscuring any information in the patient data that identifies the patient. The method further includes conveying, electronically, the labeled patient data to a third party remote computing/storage service. The labeled patient data is stored at the remote computing/storage service without any information in the patient data that identifies the patient.

Abstract: A system, method and non-transitory computer readable storage medium for retrieving a breathing reference value, assessing a breathing value of a test subject via a ventilator, identifying a difference between the breathing reference value and the breathing value of the test subject and generating a setting adjustment value to adjust a setting on the ventilator based on the identified difference.

Abstract: When prediction onset of a medical condition for a patient, multiple sources of knowledge (112) are aggregated and modeled into a format that is usable by multiple algorithms including an inference algorithm (134), a Bayesian network (136), and a state machine (138). The outputs (116) of the multiple algorithms are then combined to more accurately predict condition onset. For instance, several knowledge sources can be input to each of the inference algorithm, the Bayesian network, and the finite state machine, and the outputs of each algorithm are combined, optionally weighted, etc., to make a final determination of the likelihood that the patient has or will imminently have the specified medical condition.

Abstract: A medical apparatus (901, 100) assists clinicians, nurses or other users in choosing an intervention for the treatment of a patent suffering from an acute dynamic disease, e.g. sepsis. The medical apparatus is based on a method where a model of the disease is adapted or personalized to the patient. To ensure that the apparatus remains capable of predicting the health of the patient, the apparatus is continuously provided with new, more recent patient values and the model is continuously adapted to the new patient values. Since the medical apparatus is configured to be continuously adapted to current state of health, the apparatus is able to assist the user by generating disease management information, e.g. suggestions for medications, to an output device (902, 104).

Abstract: A method and system for canceling noise and compressing data from a motor phase angle sensor. In this invention, phase angle data is sampled at a first sampling rate to generate a sampled phase angle. The sampled phase angle signal is sub-sampled at a second sampling rate to generate a sub-sampled phase angle signal which is transformed by subtracting each of the sub-sampled data points of the sub-sampled signal from 90 degrees to generate a transformed signal. The transformed phase angle signal is then convoluted with a wavelet signal to generate a convoluted phase angle signal. Next, a phase angle range signal is generated by calculating the range between the largest and smallest convoluted data points within each of a plurality of segments of the convoluted signal. A moving average calculation is performed on the phase angle range signal to generate a moving average signal.

Abstract: A machine monitoring method includes obtaining signals indicative of machine conditions, machine rotational speed, direction, and load conditions over a segment of time, transforming the obtained signal indicative of machine conditions into a frequency spectrum, identifying low level features of the frequency spectrum, and processing the low level features to obtain an indicator value representative of the machine conditions.

Abstract: A system for determining a maintenance schedule for a jet engine using at least remotely-gathered environmental data is provided. The system includes a remote monitor having a sensor for collecting the remotely-gathered environmental data. A data pathway is connected to the remote monitor. A processor is connected to the data pathway and processes the remotely-gathered environmental data collected by the remote monitor. An environmental database is connected to the data pathway and compiles and stores the remotely-gathered environmental data. A flight database is connected to the data pathway and compiles and stores flight data for the jet engine. The flight data includes at least thermal cycle data and time on wing data. The processor is adapted to generate the maintenance schedule for the jet engine based on the remotely-gathered environmental data and the flight data.

Abstract: A leakage sensor determines a signal representative of a neutral-to-ground leakage current or voltage from the neutral point of an electric machine of an associated electrical system. A processor uses the sensed signal to determine incipient faults in at least one component of the electrical system.

Abstract: A system and method for sensing the dryness of clothing articles in a clothes dryer. In one embodiment, the clothes dryer uses a temperature sensor and a phase angle sensor to determine the dryness of the clothing articles as a function of the heated air temperature and the motor phase angle. In another embodiment the clothes dryer uses a humidity sensor to determine the dryness of the clothing articles as a function of the humidity of the heated air temperature. In a third embodiment the clothes dryer uses a temperature sensor, a phase angle sensor, and a humidity sensor to determine the dryness of the clothing articles as a function of the heated air temperature, the motor phase angle, and the humidity of the heated air temperature.

Abstract: A clothes dryer includes a control system for determining the drying time for a partial load in a clothes dryer. The system includes a humidity sensor, a signal processor, and a fuzzy logic control system. The humidity sensor generates a humidity signal representative of the humidity of air flowing through the trap duct. The humidity sensor transmits the humidity signal to the signal processor, and the signal processor determines a drying span utilizing the humidity signal. The fuzzy logic control system then utilizes the drying span to estimate the clothes load.

Abstract: A system and method for predicting the dryness of clothing articles in a clothes dryer. In one embodiment, the clothes dryer uses a temperature sensor, a phase angle sensor, and a humidity sensor to generate signal representations of the temperature of the clothing articles, the motor phase angle, and the humidity of the heated air in the duct, respectively. A controller receives the signal representations and determines a feature vector. A neural network uses the feature vector to predict a percentage of moisture content and a degree of dryness of the clothing articles in the clothes dryer. In another embodiment, the clothes dryer uses a combination of sensors to predict a percentage of moisture content and a degree of dryness of the clothing articles.