Devices and methods for monitoring cerebral hemodynamic conditions
Devices, and methods for monitoring cerebro-hemodynamic signals are disclosed. In one aspect, the devices and methods may include receiving at least one signal characterizing a bioimpedance measurement. The at least one signal may be correlated with the timing of a cardiac wave and analyzed to ascertain an extent of an expected characteristic within the signal. The extent of the expected characteristic may be used to provide information for predicting a physiological brain condition. The at least one signal may include two signals, obtained from opposite brain hemispheres, and may be a bioimpedance signal.
1. A cerebro-hemodynamic measurement apparatus, comprising:
at least one processor configured to:
receive at least one signal characterizing at least one cranial bioimpedance measurement;
correlate the at least one signal with timing of a cardiac wave;
analyze the at least one signal during a time period defined by the cardiac wave to ascertain an extent of at least one expected characteristic in the at least one signal during the time period, the analysis including:
decomposing the at least one signal into basis functions associated with parameters,
determining a distribution of at least one of the parameters, and
ascertaining the extent of the at least one expected characteristic in the at least one signal during the time period based on the distribution of the at least one of the parameters; and
output information for predicting a physiological brain condition based on the extent of the at least one expected characteristic in the at least one signal.
2. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the at least one signal includes at least two bioimpedance signals, the at least two bioimpedance signals each relating to differing hemispheres of a subject's brain, and wherein the at least two bioimpedance signals are synchronized to each other.
3. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the at least one expected characteristic relates to at least one of height, delay, width, and timing of a feature in a bioimpedance signal.
4. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the at least one expected characteristic relates to at least one of a first peak, a second peak, and a third peak in a bioimpedance signal.
5. The cerebro-hemodynamic measurement apparatus of claim 4 , wherein analyzing includes focusing on at least one time period around an expected characteristic based on an indicated physiological condition.
6. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the at least one signal includes a first signal relating to a first hemisphere of a subject's brain and a second signal relating to a second hemisphere of the subject's brain, and wherein the processor is configured to predict a physiological brain condition based at least in part on a comparison of portions of the first signal with portions of the second signal.
7. The cerebro-hemodynamic measurement apparatus of claim 6 , wherein the at least one processor is further configured to analyze portions of at least one of the first signal and the second signal during predetermined time periods of interest defined by the cardiac wave.
8. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the at least one expected characteristic relates to at least one of a peak location, a minima location, an anticipated peak location, and an anticipated minima location.
9. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein correlating includes synchronizing the at least one signal with timing of a simultaneous cardiac cycle.
10. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein correlating includes detecting a coincidence in timing of the at least one signal with a cardiac cycle by comparing portions of the at least one signal with a known signal previously correlated to a cardiac cycle.
11. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the cardiac wave includes any repeating portion of an electrocardiogram signal.
12. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the cardiac wave is an electrocardiogram signal R wave.
13. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the time period defined by the cardiac wave includes at least two repetitions of a cardiac cycle.
14. The cerebro-hemodynamic measurement apparatus of claim 1 , wherein the basis functions include at least one of gaussian functions and generalized extreme value distribution functions.
15. A method for providing information for predicting a physiological brain condition, comprising:
receiving at least one signal characterizing at least one cranial bioimpedance measurement obtained via at least one electrode;
correlating, by at least one processor, the at least one signal with timing of a cardiac wave;
analyzing, by at least one processor, the at least one signal during a time period defined by the cardiac wave to ascertain an extent of at least one expected characteristic in the at least one signal during the time period, the analysis including:
decomposing the at least one signal into basis functions associated with parameters,
determining a distribution of at least one of the parameters, and
ascertaining the extent of the at least one expected characteristic in the at least one signal during the time period based on the distribution of the at least one of the parameters; and
outputting information for predicting a physiological brain condition based on the extent of the at least one expected characteristic in the at least one signal.
16. The method of claim 15 , wherein the at least one signal includes at least two signals, the at least two signals each characterizing a bioimpedance measurement relating to differing hemispheres of a subject's brain, and further comprising synchronizing the at least two signals to each other.
17. The method of claim 15 , wherein the at least one expected characteristic relates to at least one of height, delay, width, and timing of a feature in a bioimpedance signal.
18. The method of claim 15 , wherein the at least one expected characteristic relates to at least one of a first peak, a second peak, and a third peak in a bioimpedance curve.
19. The method of claim 15 , wherein the at least one signal includes a first signal relating to a first hemisphere of a subject's brain and a second signal relating to a second hemisphere of the subject's brain, and wherein the at least one processor is configured to predict a physiological brain condition based at least in part on a comparison of portions of the first signal with portions of the second signal.
20. The method of claim 19 , further comprising analyzing portions of the first signal or the second signal during predetermined time periods of interest defined by the cardiac wave.
21. The method of claim 20 , wherein the at least one expected characteristic relates to at least one of a peak location, a minima location, an anticipated peak location, and an anticipated minima location.
22. The method of claim 15 , wherein analyzing further comprises focusing on at least one time period around an expected characteristic based on an indicated physiological condition.
23. The method of claim 15 , wherein correlating further comprises synchronizing the at least one signal with timing of a simultaneous cardiac cycle.
24. The method of claim 15 , wherein correlating further comprises detecting a coincidence in timing of the at least one signal with a cardiac cycle by comparing portions of the at least one signal with a known signal previously correlated to a cardiac cycle.
25. The method of claim 15 , wherein the cardiac wave includes any repeating portion of an electrocardiogram signal.
26. The method of claim 15 , wherein the cardiac wave is an electrocardiogram signal R wave.
27. The method of claim 15 , wherein the time period defined by the cardiac wave includes at least two repetitions of a cardiac cycle.
28. The method of claim 15 , wherein the basis functions include at least one of gaussian functions and generalized extreme value distribution functions.