Abstract: Techniques for non-invasive blood pressure monitoring are disclosed. Data corresponding to a patient may be received from a hospital information system. The data may include, for example, drug administration data, medical procedure data, medical equipment data, or a combination thereof. Whether a blood pressure monitoring system needs to be recalibrated may be determined, based at least in part on the received data. If it is determined that the blood pressure monitoring system needs to be recalibrated, the recalibration may be performed and at least one blood pressure measurement of the patient may be computed using the recalibrated blood pressure monitoring system.

Abstract: Systems and methods are provided for storing and recalling metrics associated with physiological signals. It may be determined that the value of a monitored physiological metric corresponds to a stored value. In such cases, a patient monitor may determine that a calibration is not desired. In some cases, a patient monitor may recall calibration parameters associated with the stored value if it determined that the stored value corresponds to the monitored metric value.

Abstract: According to embodiments, systems and methods are provided for filtering a signal. A first reference signal may be generated according to a signal model and a second reference signal may be generated by analyzing a continuous wavelet transform of a signal. The first and second reference signals may then both be applied to an input signal to filter the input signal according to the components of both of the reference signals.

Abstract: Systems and methods are provided for determining when to update a blood pressure measurement. The value of a physiological metric may be monitored and compared to a reference value. A patient monitoring system may compute a difference between a monitored metric and a reference value, and compare the difference to a threshold value to determine whether to update a blood pressure measurement. The threshold value may be constant or variable, and may depend on the monitored metric.

Abstract: A patient monitoring system may receive a physiological signal such as a photoplethysmograph (PPG) signal that exhibits frequency and amplitude modulation based on respiration. A phase locked loop may generate a frequency demodulated signal and an amplitude demodulated signal from the PPG signal. An autocorrelation sequence may be generated for each of the frequency demodulated signal and the amplitude demodulated signal. The autocorrelation sequences may be combined and respiration information may be determined based on the combined autocorrelation sequence.

Abstract: The present disclosure relates to monitoring a characteristic respiration rate of a patient based at least in part on a suitable time period that either precedes or follows a triggering event, such as a clinician/patient interaction, where the triggering event may negatively impact the physiological parameter. In some embodiments, physiological parameter values falling between one or more pre-set thresholds may be used to derive the characteristic physiological parameter. In some embodiments, monitoring the respiration rate may provide additional information about the patient's status. In some embodiments, confidence measures may be associated with, or may be used to analyze features of the patient signal to derive information about, the characteristic respiration rate. The patient signal used to derive a patient's respiration rate may be of an oscillatory nature or may include oscillatory features that may be analyzed to derive a characteristic respiration rate.

Abstract: A physiological measurement system is disclosed which can take a pulse oximetry signal such as a photoplethysmogram from a patient and then analyse the signal to measure physiological parameters including respiration, pulse, oxygen saturation and movement. The system comprises a pulse oximeter which includes a light emitting device and a photodetector attachable to a subject to obtain a pulse oximetry signal; analogue to digital converter means arranged to convert said pulse oximetry signal into a digital pulse oximetry signal; signal processing means suitable to receive said digital pulse oximetry signal and arranged to decompose that signal by wavelet transform means; feature extraction means arranged to derive physiological information from the decomposed signal; an analyser component arranged to collect information from the feature extraction means; and data output means arranged in communication with the analyser component.

Abstract: A physiological measurement system is disclosed which can take a pulse oximetry signal such as a photoplethysmogram from a patient and then analyze the signal to measure physiological parameters including respiration, pulse, oxygen saturation and movement. The system comprises a pulse oximeter which includes a light emitting device and a photodetector attachable to a subject to obtain a pulse oximetry signal; analogue to digital converter means arranged to convert said pulse oximetry signal into a digital pulse oximetry signal; signal processing means suitable to receive said digital pulse oximetry signal and arranged to decompose that signal by wavelet transform means; feature extraction means arranged to derive physiological information from the decomposed signal; an analyzer component arranged to collect information from the feature extraction means; and data output means arranged in communication with the analyzer component.

Abstract: A physiological measurement system is disclosed which can take a pulse oximetry signal such as a photoplethysmogram from a patient and then analyze the signal to measure physiological parameters including respiration, pulse, oxygen saturation and movement. The system comprises a pulse oximeter which includes a light emitting device and a photodetector attachable to a subject to obtain a pulse oximetry signal; analog to digital converter means arranged to convert said pulse oximetry signal into a digital pulse oximetry signal; signal processing means suitable to receive said digital pulse oximetry signal and arranged to decompose that signal by wavelet transform means; feature extraction means arranged to derive physiological information from the decomposed signal; an analyzer component arranged to collect information from the feature extraction means; and data output means arranged in communication with the analyzer component.

Abstract: The present disclosure relates to monitoring a characteristic physiological parameter of a patient during a suitable time period that either precedes or follows a triggering event, such as a clinician/patient interaction, that may negatively impact the physiological parameter. In some embodiments, physiological parameter values falling between one or more pre-set thresholds may be used to derive the characteristic physiological parameter. In some embodiments, tracking the physiological parameter may provide additional information about the patient's status. In some embodiments, confidence measures may be associated with, or may be used to analyze features of the patient signal to derive information about, the characteristic physiological parameter. The patient signal used to derive a patient's physiological parameter may be of an oscillatory nature or may include oscillatory features that may be analyzed to derive a characteristic blood pressure or a characteristic respiration rate.

Abstract: A physiological measurement system is disclosed which can take a pulse oximetry signal such as a photoplethysmogram from a patient and then analyze the signal to measure physiological parameters including respiration, pulse, oxygen saturation and movement. The system comprises a pulse oximeter which includes a light emitting device and a photodetector attachable to a subject to obtain a pulse oximetry signal; analog to digital converter means arranged to convert said pulse oximetry signal into a digital pulse oximetry signal; signal processing means suitable to receive said digital pulse oximetry signal and arranged to decompose that signal by wavelet transform means; feature extraction means arranged to derive physiological information from the decomposed signal; an analyzer component arranged to collect information from the feature extraction means; and data output means arranged in communication with the analyzer component.

Abstract: A patient monitoring system may determine portions of a PPG signal that correspond to artifacts, to a baseline shift that exceeds a threshold, or to a pulse-to-pulse variability that exceeds a threshold. The patient monitoring system may identify a contiguous portion of the PPG signal that does not include the determined portions. The contiguous portion of the PPG signal may be used to determine physiological information.

Abstract: Breathing effort of a patient, as determined (for example) from a photoplethysmograph (“PPG”) signal from the patient, can be calibrated in relation to air pressure in the patient's respiratory system. This calibration can be done by subjecting the patient to varying amounts of breathing resistance; and for each such amount, concurrently measuring (1) air pressure in the respiratory system (e.g., in the oral/nasal cavity) and (2) breathing effort (from the PPG signal). Use can be made of this calibration, e.g., during a sleep study of the patient. During such a study, breathing effort, again determined from the PPG signal and occurring, for example, during an apneic event of the patient, can be used to infer air pressure in the respiratory system by using the above calibration.

Abstract: A signal representing physiological information may include information related to respiration. A patient monitoring system may generate a plurality of autocorrelation sequences from the signal and combine the autocorrelation sequences to generate a combined autocorrelation sequence. The combined autocorrelation sequence may be analyzed to identify one or more peaks that may correspond to respiration information. Respiration information such as respiration rate may be determined based on the one or more peaks.

Abstract: According to embodiments, systems, devices, and methods for ridge selection in scalograms are disclosed. Ridges or ridge components are features within a scalogram which may be computed from a signal such as a physiological (e.g., photoplethysmographic) signal. Ridges may be identified from one or more scalograms of the signal. Parameters characterizing these ridges may be determined. Based at least in part on these parameters, a ridge density distribution function is determined. A ridge is selected from analyzing this ridge density distribution function. In some embodiments, the selected ridge is used to determine a physiological parameter such as respiration rate.

Abstract: Systems and methods for detecting and monitoring arrhythmias from a signal are provided. A signal processing system may transform a signal using a wavelet transformation and analyze changes in features of the transformed signal to detect pulse rhythm abnormalities. For example, the system may detect pulse rhythm abnormalities by analyzing energy parameters, morphology changes, and pattern changes in the scalogram of a PPG signal. Further, the system may detect pulse rhythm abnormalities by analyzing both the PPG signal and its corresponding scalogram. Physiological information, such as cardiac arrhythmia, may be derived based on the detected pulse rhythm abnormality.

Abstract: The present disclosure relates to signal processing and, more particularly, to determining the value of a physiological parameter, such as the blood oxygen saturation (SpO2) of a subject. In an embodiment, a first baseline for a first waveform and a second baseline for a second waveform are determined. In this embodiment, the first and second waveforms are indicative of the physiological parameter of the subject. The first and second waveforms are filtered to obtain a first direct-current (DC) component for the first waveform and a second DC component for the second waveform. A measured value for the physiological parameter is derived from the first and second baseline signals, and the first and second DC components. In an embodiment, the measured value for the physiological parameter is determined based on a ratio of the normalized difference between the DC component and the baseline signal for the first waveform with respect to same for the second waveform.

Abstract: According to embodiments, systems and methods for high-pass filtering a plethysmograph or photoplethysmograph (PPG) signal are disclosed. A sensor or probe may be used to obtain a plethysmograph or PPG signal from a subject. The sensor may be placed at any suitable location on the body, e.g., the forehead, finger, or toe. The PPG signal generated by the sensor may be high-pass filtered to disambiguate certain features of the PPG signal, including one or more characteristic points. The cut-off frequency for the high-pass filter may be greater than 0.75 Hz and less than 15 Hz. The cut-off frequency for the high-pass filter may be selected to be greater than the subject's computed pulse rate. These characteristic points on the filtered PPG signal may be used to compute non-invasive blood pressure measurements continuously or on a periodic basis. For example, the time difference between two or more characteristic points in a high-pass filtered version of the generated PPG signal may be computed.

Abstract: According to embodiments, systems and methods for generating reference signals are provided. A signal may be transformed using a continuous wavelet transform. Regions of interest may be selected from a transform or the resulting scalogram that may be used to generate a reference signal to use in filtering the signal or other signals. Cross-correlation techniques may be used to cancel noise components or isolate non-noise components from the signal. A physiological parameter may then be determined from the filtered signal or isolated components in the signal.

Abstract: Methods and systems are disclosed for producing a plurality of archetype signals in wavelet space at a plurality of wavelet scales. A signal is transformed using a continuous wavelet transform based at least in part on a wavelet function. A scale dependent archetype transformed signal is computed based at least in part on the transformed signal and based at least in part on a natural periodicity of the wavelet function used to transform the signal. Information may be derived about the signal from the archetype transform signal, and stored in memory.