Discrimination of apnea type by medical device
Disclosed herein, among other things, are methods and apparatus related to identification of apnea type. One aspect of the present subject matter provides a method for real-time apnea discrimination. The method includes sensing an impedance-based tidal volume signal to monitor a respiratory cycle of a patient, and detecting a reduction in tidal swing using the sensed impendence to detect an apnea event. When the apnea event is detected, a shape of the sensed signal is compared to a stored signal shape to determine whether the apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event, in various embodiments.
1. A system, comprising:
a sensor configured to sense an impedance-based tidal volume signal to monitor a respiratory cycle of a patient; and
an external system including a device configured to communicate with the sensor, the external system including a processor configured to detect a reduction in tidal swing using the sensed impendence to detect an apnea event and comparing a shape of the reduction in tidal swing to a stored signal shape to determine whether the detected apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event.
2. The system of claim 1 , wherein the external system is configured to compare a stored signal shape to the signal to determine whether the detected apnea event is primarily an OSA event or primarily a CSA event.
3. The system of claim 2 , wherein the stored signal shape includes a normal tidal swing for the patient.
4. The system of claim 2 , wherein the stored signal shape includes a stored prior OSA waveform template.
5. The system of claim 2 , wherein the stored signal shape includes an ideal sine wave as a surrogate for a CSA waveform template.
6. The system of claim 1 , wherein the device includes a diagnostic device.
7. The system of claim 1 , wherein the device includes a stimulator.
8. The system of claim 1 , wherein the device includes a wearable device.
9. The system of claim 8 , wherein the wearable device includes a patch.
10. The system of claim 8 , wherein the wearable device includes a vest.
11. A method for apnea discrimination, the method comprising:
sensing an impedance-based tidal volume signal using a sensor to monitor a respiratory cycle of a patient; and
using an external system to communicate with the sensor and to process the sensed parameter to detect a reduction in tidal swing using the sensed impendence to detect an apnea event and comparing a shape of the reduction in tidal swing to a stored signal shape to determine whether the detected apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event.
12. The method of claim 11 , comprising using a processor in an implantable stimulator in communication with the sensor to process the sensed parameter.
13. The method of claim 11 , comprising using a processor in a nerve stimulator in communication with the sensor to process the sensed parameter.
14. The method of claim 11 , comprising using a processor in an implantable diagnostic device in communication with the sensor to process the sensed parameter.
15. The method of claim 11 , comprising using a processor in a wearable device in communication with the sensor to process the sensed parameter.
16. The method of claim 11 , comprising using a processor in an external medical device in communication with the sensor to process the sensed parameter.
17. The method of claim 11 , further comprising using the processor to determine a mix or ratio of OSA and CSA for the patient.
18. The method of claim 17 , further comprising using a display connected to the processor to present the mix or ratio of OSA and CSA to a clinician for diagnostic assistance.
19. The method of claim 11 , further comprising using a memory connected to the processor to store historical data of CSA events and OSA events for the patient.
20. The method of claim 19 , further comprising using the processor to calculate likelihood for each apnea event based on the historical data and to use the calculated likelihood to determine whether the apnea event is primarily an OSA event or primarily a central sleep apnea CSA event.