Abstract: A method for identifying cardiac bradiacardia behavior may include acquiring pulse volume wave data from a sensor associated with a patient, and calculating metrics associated with peaks detected therein. The metrics may include changes in peak amplitudes of pulse volume peaks and in the times of occurrence of pulse volume peaks. Alternative metrics may include changes in frequency domain parameters derived from the time domain pulse volume wave data. Peak amplitude values may be compared to an amplitude baseline, and differences in successive peak occurrence times may be compared to a time baseline. Cardiac bradycardia behavior may be identified by a combination of a decrease in the pulse volume peak amplitude and an increase in successive peak occurrence times. A system to implement the method may include a computing device in data communication with a photo-plethysmograph. Alternative sensors may include a blood pressure cuff and an ECG device.

Abstract: A method for identifying cardiac dysrhythmia behavior may include acquiring pulse volume wave data from a sensor associated with a patient, and calculating metrics associated with peaks detected therein. The metrics may include differences in amplitudes of successive pulse volume peaks and differences in the times of occurrence of successive pulse volume peaks. A dispersion analysis of the time differences, obtained during a defined time window, may result in one or more time difference dispersion metrics. Amplitude differences may be compared to an amplitude baseline, and time differences may be compared to a time baseline. Cardiac dysrhythmia behavior may be identified by a combination of an amplitude difference outside of the amplitude baseline, a corresponding time difference outside of the time baseline, and the values of one or more time difference dispersion metrics.

Abstract: A system and method for hemodynamic dysfunction detection may include at least one sensor configured to received one or more signals from a patient, a computing device in data communication with the at least one sensor, a computer-readable storage medium in communication with the computing device, an input device, and an output device. The system may include computer readable instructions to cause the system to receive at least one signal in the time domain from the sensor, determine at least one metric in the frequency domain from the at least one signal in the time domain, and determine the cardiovascular state of the patient from a combination of the at least one metric in the frequency domain and information contained in at least one database of cardiovascular states. The system may also notify a user of a immanent patient cardiovascular event and recommend one or more interventions to mitigate it.

Abstract: A computer-implemented method for characterizing circulatory blood volume and autoregulatory compensatory mechanisms to maintain circulatory blood volume is disclosed. A biological signal that emulates the arterial pulse wave is collected from a sensor. Three derived parameters are extrapolated from the biological signal. The first parameter, circulatory stress, reflects of the changes of the heart rate frequency. The second, circulatory blood volume, reflects the changes in the frequency strength of the heart rate frequency. The third, Pulse Volume Alteration (PVA) Index is a ratio of the sum of the strengths of the heart rate frequency harmonics to the strength of the heart rate frequency of the unprocessed biological signal. Each parameter is compared to a threshold value and assessed to determine an adequacy of circulatory blood volume and an appropriateness of the autoregulatory mechanisms used to maintain circulatory blood volume adequacy.

Abstract: A system and method for hemodynamic dysfunction detection may include at least one sensor configured to received one or more signals from a patient, a computing device in data communication with the at least one sensor, a computer-readable storage medium in communication with the computing device, an input device, and an output device. The system may include computer readable instructions to cause the system to receive at least one signal in the time domain from the sensor, determine at least one metric in the frequency domain from the at least one signal in the time domain, and determine the cardiovascular state of the patient from a combination of the at least one metric in the frequency domain and information contained in at least one database of cardiovascular states. The system may also notify a user of a immanent patient cardiovascular event and recommend one or more interventions to mitigate it.

Abstract: A method for identifying cardiac bradiacardia behavior may include acquiring pulse volume wave data from a sensor associated with a patient, and calculating metrics associated with peaks detected therein. The metrics may include changes in peak amplitudes of pulse volume peaks and in the times of occurrence of pulse volume peaks. Alternative metrics may include changes in frequency domain parameters derived from the time domain pulse volume wave data. Peak amplitude values may be compared to an amplitude baseline, and differences in successive peak occurrence times may be compared to a time baseline. Cardiac bradycardia behavior may be identified by a combination of a decrease in the pulse volume peak amplitude and an increase in successive peak occurrence times. A system to implement the method may include a computing device in data communication with a photo-plethysmograph. Alternative sensors may include a blood pressure cuff and an ECG device.

Abstract: A method for identifying cardiac dysrhythmia behavior may include acquiring pulse volume wave data from a sensor associated with a patient, and calculating metrics associated with peaks detected therein. The metrics may include differences in amplitudes of successive pulse volume peaks and differences in the times of occurrence of successive pulse volume peaks. A dispersion analysis of the time differences, obtained during a defined time window, may result in one or more time difference dispersion metrics. Amplitude differences may be compared to an amplitude baseline, and time differences may be compared to a time baseline. Cardiac dysrhythmia behavior may be identified by a combination of an amplitude difference outside of the amplitude baseline, a corresponding time difference outside of the time baseline, and the values of one or more time difference dispersion metrics.

Abstract: A computer-implemented method for characterizing circulatory blood volume and autoregulatory compensatory mechanisms to maintain circulatory blood volume is disclosed. A biological signal that emulates the arterial pulse wave is collected from a sensor. Three derived parameters are extrapolated from the biological signal. The first parameter, circulatory stress, reflects of the changes of the heart rate frequency. The second, circulatory blood volume, reflects the changes in the frequency strength of the heart rate frequency. The third, Pulse Volume Alteration (PVA) Index is a ratio of the sum of the strengths of the heart rate frequency harmonics to the strength of the heart rate frequency of the unprocessed biological signal. Each parameter is compared to a threshold value and assessed to determine an adequacy of circulatory blood volume and an appropriateness of the autoregulatory mechanisms used to maintain circulatory blood volume adequacy.