Discrimination of cheyne stokes breathing patterns
A method of a processor for detecting a presence of Cheyne-Stokes respiration from a respiration signal includes accessing data representative of a respiration signal. Data is assessed to detect apnea and/or hypopnea events. A cycle length histogram is determined based on the events and an incident of Cheyne-Stokes respiration is detected based on the cycle length histogram.
1. A method implemented by a processor for detecting a presence of Cheyne-Stokes respiration from a respiratory signal generated by a respiration sensor, the method comprising:
accessing respiratory data representative of the respiratory signal;
assessing the accessed respiratory data to detect apnea and/or hypopnea events;
evaluating respiratory data directly following the detected events to estimate a jump feature representing a change in the respiratory data, wherein the jump feature is calculated by selecting a first peak of the respiratory data, selecting a second peak at a predetermined ratio of the first peak, and calculating a gradient between the first peak and the second peak;
detecting an incident of Cheyne-Stokes respiration based on the jump feature; and
responding to the detection of an incident of Cheyne-Stokes respiration, the responding comprising controlling an adjustment of a therapeutic pressure delivered by a respiratory treatment apparatus.
2. The method of claim 1 , wherein evaluating the respiratory data comprises calculating inspiratory tidal volumes during at least a portion of time between two adjacent apnea and/or hypopnea events.
3. The method of claim 1 , wherein evaluating the respiratory data comprises calculating a product of peak inspiratory flow data and inspiratory tidal volume and storing them in a morphology vector.
4. The method of claim 3 , wherein evaluating the respiratory data further comprises computing a mean squared error between the morphology vector and an approximating function.
5. The method of claim 3 , wherein evaluating the respiratory data comprises integrating the peak inspiratory flow data between apnea and/or hypopnea events and storing them in the morphology vector.
6. The method of claim 5 , wherein evaluating the respiratory data further comprises computing a mean squared error between the morphology vector and an approximating function.
7. The method of claim 1 , wherein the jump feature represents at least one of a rise and a fall of a breathing drive of a patient.
8. The method of claim 1 , further comprising scaling the gradient between the first peak and the second peak.
9. The method of claim 1 , wherein detecting the incident of Cheyne Stokes respiration comprises determining a Cheyne Stokes respiration probability using the jump feature.
10. An apparatus for detecting a presence of Cheyne-Stokes respiration from a respiratory signal generated by a respiration sensor, the apparatus comprising:
a memory for storing respiratory data associated with the respiratory signal; and
a processor, coupled with the memory, the processor being configured to assess the respiratory data to detect apnea and/or hypopnea events;
evaluate peaks in the respiratory data directly following the detected events to estimate a jump feature representing a change in the respiratory data, wherein the jump feature is calculated by selecting a first peak of the respiratory data, selecting a second peak at a predetermined ratio of the first peak, and calculating a gradient between the first peak and the second peak;
detect an incident of Cheyne-Stokes respiration based on the jump feature; and
generate a response to the detection of an incident of Cheyne-Stokes respiration, the response comprising an adjustment to therapeutic pressure delivered by a respiratory treatment apparatus.
11. The apparatus of claim 10 , wherein the data associated with the respiratory signal comprises flow data and tidal volume data and the processor is further configured to:
calculate peak inspiratory flow data and inspiratory tidal volume; and
calculate a product of the peak inspiratory flow data and inspiratory tidal volume and storing them in a morphology vector.
12. The apparatus of claim 11 , wherein the processor is configured to normalize the morphology vector by converting it into 0 to 1 probability space.
13. The apparatus of claim 11 , wherein the processor is configured to evaluate the peaks in the flow data by computing a mean squared error between the morphology vector and an approximating function.
14. The apparatus of claim 11 , wherein the processor is configured to evaluate the peaks in the flow data by integrating the flow data between apnea and/or hypopnea events and storing them in the morphology vector.
15. The apparatus of claim 14 , wherein the processor is configured to evaluate the peaks in the flow data by computing a mean squared error between the morphology vector and an approximating function.
16. The apparatus of claim 10 , wherein the jump feature represents at least one of a rise and a fall of a breathing drive of a patient.
17. The apparatus of claim 10 , wherein the processor is configured to scale the gradient between the first peak and the second peak.
18. The method of claim 1 , wherein the accessed respiratory data comprises values of respiratory flow.
19. The method of claim 1 , wherein the accessed respiratory data comprises values of ventilation.
20. The method of claim 1 , wherein the accessed respiratory data comprises values of tidal volume.