Adaptive method and apparatus for forecasting and controlling neurological disturbances under a multi-level control

• US 8,065,011 B2
• Filed: 08/31/2006
• Issued: 11/22/2011
• Est. Priority Date: 12/12/2000
• Status: Expired due to Term

• Current Assignee: Trustees Of The University Of Pennsylvania (University of Pennsylvania)
• Original Assignee: Trustees Of The University Of Pennsylvania (University of Pennsylvania)
• Inventors: Litt, Brian, Vachtsevanos, George John, Echauz, Javier Ramón, Esteller, Rosana
• Primary Examiner(s): Patel; Niketa
• Assistant Examiner(s): Lavert; Nicole F
• Application Number:
  US11/469,029
• Publication Number:
  US 20070142873A1
• Time in Patent Office: 1,909 Days
• Field of Search: 607/6, 607/45, 607/139
• US Class Current: 607/45
• CPC Class Codes: A61B 5/076 Permanent implantations telemetry using implanted circuitry A61B5/0031; implanted stimulators for therapy A61N1/3605, A61N1/362, A61N1/372
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/37 Intracranial electroencephalography [IC-EEG], e.g. electrocorticography [ECoG]
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/375 using biofeedback
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

An adaptive method and apparatus for forecasting and controlling neurological abnormalities in humans such as seizures or other brain disturbances. The system is based on a multi-level control strategy. Using as inputs one or more types of physiological measures such as brain electrical, chemical or magnetic activity, heart rate, pupil dilation, eye movement, temperature, chemical concentration of certain substances, a feature set is selected off-line from a pre-programed feature library contained in a high level controller within a supervisory control architecture. This high level controller stores the feature library within a notebook or external PC. The supervisory control also contains a knowledge base that is continuously updated at discrete steps with the feedback information coming from an implantable device where the selected feature set (feature vector) is implemented. This high level controller also establishes the initial system settings (off-line) and subsequent settings (on-line) or tunings through an outer control loop by an intelligent procedure that incorporates knowledge as it arises. The subsequent adaptive settings for the system are determined in conjunction with a low-level controller that resides within the implantable device. The device has the capabilities of forecasting brain disturbances, controlling the disturbances, or both. Forecasting is achieved by indicating the probability of an oncoming seizure within one or more time frames, which is accomplished through an inner-loop control law and a feedback necessary to prevent or control the neurological event by either electrical, chemical, cognitive, sensory, and/or magnetic stimulation.

Claims

1. An adaptive multi-level hierarchical control system for predicting and controlling an electrographic onset of a seizure and other neurological disturbances in an individual, comprising:

a data generation component, including a plurality of sensors adapted to be attached or implanted, that acquires a plurality of physiological signals detected by at least one sensor;

an intelligent data processing device that processes the physiological signals to extract features and analyzes and classifies the extracted features to form a feedback vector that includes an estimate of a probability of seizure for at least one specific time frame;

a low level controller including a stimulation device, having a plurality of actuators, that is activated to apply a preventive measure adaptively in response to the estimate of a probability of seizure in the feedback vector exceeding an adaptive threshold to control the onset of seizure and to adjust automatically an internal parameter setting in at least one actuator in the stimulation device; and

a high level supervisory controller including a knowledge database and a processor that adapts to feedback vector changes over time and re-tunes the intelligent data processing device parameter settings dynamically.

2. The adaptive multi-level hierarchical control system of claim 1 wherein the knowledge database comprises a priori information for the individual.

3. The adaptive multi-level hierarchical control system of claim 2 wherein the a priori information for the individual comprises seizure frequency over time, seizure duration, type of seizure, and aura frequency collected before an implantation of the intelligent data processing device.

4. The adaptive multi-level hierarchical control system of claim 1 wherein the low level controller determines and adjusts the parameter settings of the actuators in the stimulation device continuously.

5. The adaptive multi-level hierarchical control system of claim 1 wherein the high level supervisory controller can operate in an automatic mode or in a semi-automatic mode.

6. The adaptive multi-level hierarchical control system of claim 5 further comprising a program module, cooperative with the low level controller and the high level controller, wherein the program module includes an applied feedback control law and a knowledge base control law, monitors a set of controlled variables, including the probability of seizure for at least one specific time frame, and updates the applied feedback control law when operating in the automatic mode.

7. The adaptive multi-level hierarchical control system of claim 5 further comprising a program module user interface, wherein the parameter settings are input directly into the intelligent data processing device through the program module user interface when operating in a semi-automatic mode.

8. The adaptive multi-level hierarchical control system of claim 1 wherein the high level supervisory controller comprises a processor external to the intelligent data processing device for providing a coordination layer control.

9. The adaptive multi-level hierarchical control system of claim 8 wherein the coordination layer control determines control parameters including parameters related to fusion of sensory data, feature extraction, feature normalization, neural network retraining, fuzzy logic adjustments, and fault diagnoses of the plurality of actuators, the plurality of sensors and the intelligent data processing device.

10. The adaptive multi-level hierarchical control system of claim 1 further comprising an external processor for providing a research layer control including a plurality of algorithms executable on the external processor for evaluation including algorithms for control of seizures or brain disturbances, for prediction and detection of the unequivocal electrographic onset of seizure, for control strategies, or for other types of parameter adjustments.

11. The adaptive multi-level hierarchical control system of claim 10 wherein the research layer control analyzes a plurality of data extracted from the physiological signals to evaluate seizure and other brain disturbances.

12. The adaptive multi-level hierarchical control system of claim 10 wherein the research layer control collects information from a plurality of individuals to form a research and development database.

13. The adaptive multi-level hierarchical control system of claim 6 wherein the multi-level hierarchical control is provided by the feedback control law updated by the low level controller and the knowledge base control law updated by the high level supervisory controller.

14. The adaptive multi-level hierarchical control system of claim 13 wherein the adaptive hierarchical control is provided by the updated knowledge base control law.

15. The adaptive multi-level hierarchical control system of claim 6 wherein the high level supervisory controller processor operates a logic module that executes optimization algorithms and determines self-evaluation metrics to establish the supervisory controller'"'"'s performance over time, to determine required adjustments in the intelligent data processing device'"'"'s set points, and to generate the updated feedback control law that is downloaded into the intelligent data processing device.

16. The adaptive multi-level hierarchical control system of claim 1 wherein the knowledge database is updated at discrete steps by downloading information from the intelligent data processing device.

17. The adaptive multi-level hierarchical control system of claim 11 further comprising an external portable module including an external communications unit that enables the transfer of the extracted physiological data from the intelligent data processing device to the external portable module for analysis and storage.

18. The adaptive multi-level hierarchical control system of claim 17 wherein the external portable module further comprises a display device that shows the probability for having a seizure in at least one specific time frame.

19. The adaptive multi-level hierarchical control system of claim 17 wherein the external portable module further comprises an alarm device, coupled to the low level controller, that is activated to alert the individual of an oncoming seizure when the probability of having a seizure in the at least one specific time frame exceeds an adaptive threshold.

20. The adaptive multi-level hierarchical control system of claim 1 wherein the intelligent data processing device is implantable.

21. The adaptive multi-level hierarchical control system of claim 20 wherein the intelligent data processing device comprises artificial intelligence tools wherein the intelligent data processing device provides a learning capability based on the artificial intelligence tools and an analysis of previously stored information for the individual that enables an adaptation of the implantable intelligent data processing device.

22. The adaptive multi-level hierarchical control system of claim 1 wherein the at least one sensor detects physiological signals that indicate a state of brain activity in the individual.