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 method for apnea discrimination, the method comprising:
sensing an impedance-based tidal volume signal to monitor a respiratory cycle of a patient;
detecting a reduction in tidal swing using the sensed impendence to detect an apnea event; and
when the apnea event is detected, comparing a shape of the sensed signal 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.
2. The method of claim 1 ; wherein the stored signal shape includes a normal tidal swing shape for the patient.
3. The method of claim 2 , wherein if the shape of the sensed signal is similar to the normal tidal swing shape, the apnea event is determined to be primarily a CSA event.
4. The method of claim 2 , wherein if the shape of the sensed signal is not similar to the normal tidal swing shape, the apnea event is determined to be primarily an OSA event.
5. The method of claim 1 , wherein the stored signal shape includes a stored prior OSA waveform template.
6. The method of claim 1 , wherein the stored signal shape includes a sine wave.
7. The method of claim 1 , wherein the stored signal shape includes a square wave.
8. The method of claim 1 , further comprising calculating likelihood for each apnea event based on historical prevalence of CSA events and OSA events for the patient.
9. The method of claim 8 , further comprising using the calculated likelihood to determine whether the apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event.
10. The method of claim 1 , further comprising assigning a confidence score to the determination of whether the apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event.
11. A medical device for apnea discrimination, the device comprising:
a sensor configured to sense an impedance-based tidal volume signal to monitor a respiratory cycle of a patient; and
a processor adapted to be connected to the sensor, the processor configured to detect a reduction in tidal swing using the sensed impendence to detect an apnea event and to compare a shape of the sensed signal 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 device of claim 11 , wherein the processor is configured to use variability in low frequency components of a spectrum of at least one of intervals, S1 amplitude, S2 amplitude, S3 amplitude or systolic time intervals to determine prevalence of primarily OSA events and primarily CSA events for the patient.
13. The device of claim 12 , wherein the processor is configured to use the variability to determine a likelihood of observing CSA or OSA on the next apnea event.
14. The device of claim 13 , wherein the processor is configured to adjust a discrimination threshold for the next apnea event based on the determined likelihood.
15. The device of claim 11 , wherein the processor is configured to track a set of spectral peaks on a spectrogram and use energies of tracked peaks to determine relative prevalence of primarily OSA events and primarily CSA events for the patient.
16. The device of claim 15 , wherein the processor is configured to use energy of the tracked peaks relative to total spectral energy to determine a likelihood of observing CSA or OSA on the next apnea event, and wherein the processor is configured to adjust a discrimination threshold for the next apnea event based on the determined likelihood.