In an embodiment, PhotoPlethysmoGraphy (PPG) signals are processed by detecting peaks and valleys in the PPG signal, segmenting the PPG signal to provide a time series of PPG waveforms located between two subsequent valleys in the PPG signal, applying to the waveforms in the time series pattern recognition with respect to a reference PPG waveform pattern produced based on a mathematical model of the PPG signal by assigning to the waveforms in the time series a recognition score. A resulting PPG signal is produced by retaining the waveforms in the time series having an assigned recognition score reaching a recognition threshold, and discarding the waveforms in the time series having an assigned recognition score failing to reach the recognition threshold.

In an embodiment, PhotoPlethysmoGraphy (PPG) signals are processed by detecting peaks and valleys in the PPG signal, segmenting the PPG signal to provide a time series of PPG waveforms located between two subsequent valleys in the PPG signal, applying to the waveforms in the time series pattern recognition with respect to a reference PPG waveform pattern produced based on a mathematical model of the PPG signal by assigning to the waveforms in the time series a recognition score. A resulting PPG signal is produced by retaining the waveforms in the time series having an assigned recognition score reaching a recognition threshold, and discarding the waveforms in the time series having an assigned recognition score failing to reach the recognition threshold.

A method of detecting status changes, and a corresponding point-in-time, in monitored entities, includes receiving one or more elements of time-series data from one or more sensors, the elements of time-series data representing an operational state of the monitored entity, creating a predictive model from the time-series data in a datastore memory, applying a transduction classifier to the predictive model, the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred, and providing an identification of the change in the time-series data and the corresponding point-in-time. In some embodiments the transduction classifier can be a maximum margin classifier having a support vector machine component and a temporal transductive component. A system and a non-transitory computer readable medium are also disclosed.

A method of detecting status changes, and a corresponding point-in-time, in monitored entities, includes receiving one or more elements of time-series data from one or more sensors, the elements of time-series data representing an operational state of the monitored entity, creating a predictive model from the time-series data in a datastore memory, applying a transduction classifier to the predictive model, the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred, and providing an identification of the change in the time-series data and the corresponding point-in-time. In some embodiments the transduction classifier can be a maximum margin classifier having a support vector machine component and a temporal transductive component. A system and a non-transitory computer readable medium are also disclosed.

Example systems, methods, and apparatus, including cognitive platforms, are provided for computing performance metrics of an individual based at least in part on user interaction(s) with computerized tasks and/or interference and at least one physiological measure of the individual, where the performance metric provides an indication of the cognitive abilities of the individual. The apparatus can be coupled to at least one physiological component to perform the physiological measurement of the individual. The apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual's cognitive abilities.

A plethysmographic respiration processor is responsive to respiratory effects appearing on a blood volume waveform and the corresponding detected intensity waveform measured with an optical sensor at a blood perfused peripheral tissue site so as to provide a measurement of respiration rate. A preprocessor identifies a windowed pleth corresponding to a physiologically acceptable series of plethysmograph waveform pulses. Multiple processors derive different parameters responsive to particular respiratory effects on the windowed pleth. Decision logic determines a respiration rate based upon at least a portion of these parameters.

A plethysmographic respiration processor is responsive to respiratory effects appearing on a blood volume waveform and the corresponding detected intensity waveform measured with an optical sensor at a blood perfused peripheral tissue site so as to provide a measurement of respiration rate. A preprocessor identifies a windowed pleth corresponding to a physiologically acceptable series of plethysmograph waveform pulses. Multiple processors derive different parameters responsive to particular respiratory effects on the windowed pleth. Decision logic determines a respiration rate based upon at least a portion of these parameters.

Systems and methods are provided herein for treating a patient in cardiogenic shock. An intravascular heart pump system is inserted into vasculature of the patient. The heart pump system has a cannula, pump outlet, pump inlet, and rotor. The heart pump system is positioned within the patient such that the cannula extends across the patient's aortic valve, the pump inlet is located within the patient's left ventricle, and the pump outlet is located within the patient's aorta. Data related to time-varying parameters of the heart pump system is acquired from the heart pump system. A plurality of features are extracted from the data. A probability of survival of the patient is determined based on the plurality of features and using a prediction model. The heart pump system is operated to treat the patient.

Systems and methods are provided herein for treating a patient in cardiogenic shock. An intravascular heart pump system is inserted into vasculature of the patient. The heart pump system has a cannula, pump outlet, pump inlet, and rotor. The heart pump system is positioned within the patient such that the cannula extends across the patient's aortic valve, the pump inlet is located within the patient's left ventricle, and the pump outlet is located within the patient's aorta. Data related to time-varying parameters of the heart pump system is acquired from the heart pump system. A plurality of features are extracted from the data. A probability of survival of the patient is determined based on the plurality of features and using a prediction model. The heart pump system is operated to treat the patient.

An information processing system includes a vehicle and an information processing device that acquires information acquired by the vehicle from the vehicle. The vehicle acquires information on an occupant in a passenger compartment of the vehicle, and acquires position information of the vehicle. When it is determined based on the information on the occupant that the vehicle has traveled to the vicinity of a hidden tourist attraction, the information processing device generates tourist attraction information including the position information of the vehicle at a point at which the determination has been performed.