Neural analysis and treatment system
A neural analysis and treatment system includes a computing device with a memory for storing an application that is executable on a processor to receive amplitude-integrated electroencephalography (aEEG) and range-EEG (rEEG) measurements associated with a patient. The systems determine a spectral edge frequency (SEF) measurement from the received EEG measurements, and determine one or more neural characteristics of the patient according to the determined SEF, aEEG, and rEEG measurements. These neural characteristics may then be used to identify and implement an appropriate therapeutic treatment.
1. A neural analysis and treatment system comprising:
a computing device having at least one non-transitory local memory and at least one processor, the at least one non-transitory local memory comprising computer executable instructions executed by the at least one processor to:
receive, by the at least one processor, electroencephalography (EEG) data comprising amplitude-integrated electroencephalography (aEEG) and range-EEG (rEEG) measurements from one or more electrodes associated with a patient exposed to somatosensory stimulation;
generate spectral edge frequency (SEF) information from the received EEG data;
determine one or more neural characteristic of the patient using the SEF information, wherein the one or more neural characteristic comprises variance in voltages of the received EEG data, continuity of the received EEG data, or sleep-wake cycling of the patient; and
develop one or more therapeutic plans based upon the one or more neural characteristic of the patient, wherein the one or more therapeutic plans entrain a brain activity for controlling respiration and mastication in a patient, wherein the patient is a preterm infant or a post-conceptional age infant.
2. The system of claim 1 , wherein the SEF information comprises at least one of an SEF modulation of an EEG spectra of the received EEG data or an SEF asymmetry of the EEG spectra.
3. The system of claim 2 , wherein the SEF information is derived from an rEEG modulation of the patient.
4. The system of claim 2 , wherein the SEF information is derived from an rEEG asymmetry of the patient.
5. The system of claim 2 , wherein the SEF information is derived from an aEEG modulation of the patient.
6. The system of claim 2 , wherein the SEF information is derived from an aEEG asymmetry of the patient.
7. The system of claim 1 , wherein the one or more neural characteristic is determined using an SEF asymmetry of the patient.
8. The system of claim 1 , wherein the one or more neural characteristic comprises a cortical activity of the patient.
9. The system of claim 1 , further comprising the processor being configured to:
generate a signal, received at an entrainment device to perform the one or more therapeutic plans on the patient;
receive, by the processor, other EEG data from one or more electrodes associated with the patient, after the one or more therapeutic plans have been performed;
generate additional SEF information from the other received EEG data;
determine at least one other neural characteristic of the patient according to the additional SEF information; and
determine a brain development level of the patient according to the one or more neural characteristic and the at least one other neural characteristic.
10. The system of claim 9 , wherein the brain development level comprises a quantified cortical adaptation of the patient.
11. The system of claim 9 , wherein the additional SEF information comprises at least one of an SEF modulation of the other received EEG or an SEF asymmetry of the other received EEG data.
12. The system of claim 11 , wherein the additional SEF information is derived from an rEEG modulation of the patient.
13. The system of claim 11 , wherein the additional SEF information is derived from an rEEG asymmetry of the patient.
14. The system of claim 11 , the additional SEF information is derived from an aEEG modulation of the patient.
15. The system of claim 11 , the additional SEF information is derived from an aEEG asymmetry of the patient.
16. The system of claim 9 , wherein the at least one other neural characteristic comprises a cortical activity of the patient.
17. The system of claim 9 , further comprising the processor being configured to generate one or more reports that include at least one of the one or more neural characteristics, the at least one other neural characteristic, and the brain development level of the patient.
18. A method for neural analysis and treatment plan development for providing neurological entrainment to a patient, using a computing device having at least one processor and at least one non-transitory local memory further comprising computer executable instructions executed by the at least one processor, the method comprising:
at the computing device:
receiving electroencephalography (EEG) data comprising amplitude-integrated electroencephalography (aEEG) and range-EEG (rEEG) measurements from one or more electrodes associated with the patient exposed to somatosensory stimulation;
generating spectral edge frequency (SEF) information from the received EEG data;
determining one or more neural characteristic of the patient using the SEF information wherein the one or more neural characteristic comprises variance in voltages of the received EEG data, continuity of the received EEG data, or sleep-wake cycling of the patient;
developing one or more therapeutic plans based upon the one or more neural characteristic of the patient, wherein the one or more therapeutic plans entrain a brain activity for controlling respiration and mastication in the patient, wherein the patient is a preterm infant or a post-conceptional age infant.
19. The method of claim 18 further comprising:
using an entrainment device to perform the one or more therapeutic plans on the patient;
receiving, at the computing device, other EEG data from one or more electrodes associated with the patient, after the one or more therapeutic plans have been performed;
generating, at the computing device, other SEF information from the other received EEG data;
determining, at the computing device, another neural characteristic of the patient according to the other SEF information; and
determining, at the computing device, a brain development level of the patient according to the one or more neural characteristic and the other neural characteristic.
20. The method of claim 19 , wherein the brain development level comprises a quantified cortical adaptation of the patient.
21. The method of claim 19 , wherein at least one of the one or more neural characteristic or other neural characteristic comprises an SEF modulation of the patient.
22. The method of claim 19 , wherein at least one of the one or more neural characteristic or other neural characteristic comprises a rEEG modulation of the patient.
23. The method of claim 19 , wherein at least one of the one or more neural characteristic or other neural characteristic comprises a rEEG asymmetry of the patient.
24. The method of claim 19 , wherein at least one of the one or more neural characteristic or other neural characteristic comprises an aEEG modulation of the patient.
25. The method of claim 19 , wherein at least one of the one or more neural characteristic or other neural characteristic comprises an aEEG asymmetry of the patient.
26. The method of claim 19 , wherein at least one of the one or more neural characteristic or other neural characteristic comprises an SEF asymmetry of the patient.
27. The method of claim 19 , wherein at least one of the one or more neural characteristic or other neural characteristic comprises a cortical activity of the patient.