Abstract: A parameter translation system configured to determine respiratory therapy control parameters is provided. When a subject is prescribed respiratory therapy, his or her regimen will likely include receiving therapy from several different respiratory therapy devices. Respiratory therapy devices are often designed and/or approved for a niche depending on acuity of respiratory distress and/or insufficiency in the subject, the physical location of the subject, and/or other factors. These respiratory devices are made by a variety of manufacturers and deliver therapy with a variety of therapy regimes, settings, alarm parameters, and/or other control parameters. It is a laborious process to transfer such control parameters between two or more respiratory therapy devices.

Abstract: A patient monitoring apparatus and method is provided. A plurality of moving average filters each receive a signal sensed from a patient and generates an output signal including a respective cutoff frequency. An extraction processor is coupled to receive the sensed signal and output signals from each moving average filters. The extraction processor determines at least one feature associated with the sensed signal based on at least one of the sensed signal and the output signals from each moving average filter and generates a feature signal including data representative of the determined at least one feature. A classification processor identifies a position of artifacts within the sensed signal based on the feature signal and generates a signal identifying the position of the artifacts.

Abstract: An apparatus and method is provided that identifies the presence or absence of a P-wave within a set of ECG data. A computation processor identifies the R-wave and then analyses a section of the waveform within a predetermined time window preceding the detected R-wave peak. The waveform within the window is analysed to identify a candidate P-wave, and in response to identifying the candidate P-wave a first and second feature associated therewith is measured. A composite feature value is calculated from the first and second measures, and compared to a classification threshold value. In an exemplary embodiment, the first feature represents a height between a highest peak of the candidate P-wave and a trough of the Q-wave, and the second feature represents a time between the peak of the candidate P-wave and a peak of the R-wave.

Abstract: The present disclosure pertains to a parameter translation system configured to determine respiratory therapy control parameters. When a subject is prescribed respiratory therapy, his or her regimen will likely include receiving therapy from several different respiratory therapy devices. Respiratory therapy devices are often designed and/or approved for a niche depending on acuity of respiratory distress and/or insufficiency in the subject, the physical location of the subject, and/or other factors. These respiratory devices are made by a variety of manufacturers and deliver therapy with a variety of therapy regimes, settings, alarm parameters, and/or other control parameters. It is a laborious process to transfer such control parameters between two or more respiratory therapy devices.

Abstract: A patient monitoring apparatus and method is provided. A plurality of moving average filters each receive a signal sensed from a patient and generates an output signal including a respective cutoff frequency. An extraction processor is coupled to receive the sensed signal and output signals from each moving average filters. The extraction processor determines at least one feature associated with the sensed signal based on at least one of the sensed signal and the output signals from each moving average filter and generates a feature signal including data representative of the determined at least one feature. A classification processor identifies a position of artifacts within the sensed signal based on the feature signal and generates a signal identifying the position of the artifacts.

Abstract: An apparatus and method is provided that identifies the presence or absence of a P-wave within a set of ECG data. A computation processor identifies the R-wave and then analyses a section of the waveform within a predetermined time window preceding the detected R-wave peak. The waveform within the window is analysed to identify a candidate P-wave, and in response to identifying the candidate P-wave a first and second feature associated therewith is measured. A composite feature value is calculated from the first and second measures, and compared to a classification threshold value. In an exemplary embodiment, the first feature represents a height between a highest peak of the candidate P-wave and a trough of the Q-wave, and the second feature represents a time between the peak of the candidate P-wave and a peak of the R-wave.