Unified probabilistic framework for predicting and detecting seizure onsets in the brain and multitherapeutic device

• US 7,333,851 B2
• Filed: 09/12/2003
• Issued: 02/19/2008
• Est. Priority Date: 10/20/2000
• 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): ROBINSON, DANIEL LEON
• Application Number:
  US10/662,072
• Publication Number:
  US 20040068199A1
• Time in Patent Office: 1,621 Days
• Field of Search: 600/544, 600/545, 600/300, 600/378
• US Class Current: 600/544
• 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 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• 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 1/36017 with leads or electrodes penetrating the skin
• CPC Class Codes: A61N 1/36064 Epilepsy
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
• CPC Class Codes: A61N 1/36135 using physiological parameters
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06F 18/24155 Bayesian classification
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• 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
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method and an apparatus for predicting and detecting epileptic seizure onsets within a unified multiresolution probabilistic framework, enabling a portion of the device to automatically deliver a progression of multiple therapies, ranging from benign to aggressive as the probabilities of seizure warrant. Based on novel computational intelligence algorithms, a realistic posterior probability function P(S_T|x) representing the probability of one or more seizures starting within the next T minutes, given observations x derived from IEEG or other signals, is periodically synthesized for a plurality of prediction time horizons. When coupled with optimally determined thresholds for alarm or therapy activation, probabilities defined in this manner provide anticipatory time-localization of events in a synergistic logarithmic-like array of time resolutions, thus effectively circumventing the performance vs. prediction-horizon tradeoff of single-resolution systems. The longer and shorter prediction time scales are made to correspond to benign and aggressive therapies respectively. The imminence of seizure events serves to modulate the dosage and other parameters of treatment during open-loop or feedback control of seizures once activation is triggered. Fast seizure onset detection is unified within the framework as a degenerate form of prediction at the shortest, or even negative, time horizon. The device is required to learn in order to find the probabilistic prediction and control strategies that will increase the patient'"'"'s quality of life over time. A quality-of-life index (QOLI) is used as an overall guide in the optimization of patient-specific signal features, the multitherapy activation decision logic, and to document if patients are actually improving.

Claims

1. A system for automatically predicting and preventing the electrographic onset of a seizure in an individual, comprising:

a signal acquisition component to condition and digitize a plurality of raw signals received from a transducer implanted in the individual;

a preprocessor to attenuate any artifacts in the plurality of digitized signals;

a feature extraction component containing processing logic to select patient-specific seizure-predictive and seizure-indicative attributes from the preprocessed signals to form an optimal feature vector;

a probability estimator component that synthesizes a probability vector as an estimator of the likelihood of seizure for a plurality of prediction times;

a multitherapy activation component containing processing logic to determine the therapy modalities that are to be activated or deactivated at any time; and

an implanted device including a plurality of therapy actuators to automatically activate at least one associated therapy in response to an output signal from the multitherapy activation component.

2. The system for automatically predicting and preventing the electrographic onset of a seizure of claim 1 wherein the therapies associated with the plurality of therapy actuators include one or more of cognitive stimulation, sensory stimulation, biofeedback, electrical stimulation and pharmacological infusion.

3. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the preprocessor is implemented in any one or more of a microprocessor, a digital signal processor, a field programmable gate array, an application specific integrated circuit, and a hybrid analog/digital circuit.

4. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the feature extraction component is implemented in any one or more of a microprocessor, a digital signal processor, a field programmable gate array, an application specific integrated circuit, and a hybrid analog/digital circuit.

5. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the probability estimator component is implemented in any one or more of a microprocessor, a digital signal processor, a field programmable gate array, an application specific integrated circuit, and a hybrid analog/digital circuit.

6. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the multitherapy activation component is implemented in any one or more of a microprocessor, a digital signal processor, a field programmable gate array, an application specific integrated circuit, and a hybrid analog/digital circuit.

7. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the plurality of therapy actuators are programmed to operate in an open loop mode, delivering prophylactic treatment by any one of a button, a magnet, and a vibration transducer.

8. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the plurality of therapy actuators are programmed to operate in a closed loop mode, using any one of a proportional, a proportional-integral-derivative, an optimal continuous, a gain-scheduled, a multilevel, and a bang-bang feedback control strategy.

9. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 further comprising an external storage for storing the digitized signals processed by the signal acquisition component.

10. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 further comprising a wearable access device to display the probability vector of oncoming seizures and the multitherapy activation status to the individual.

11. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the implanted device further includes an electronically erasable programmable read-only memory for downloading a plurality of learned algorithms and parameters received via transceivers.

12. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1, further comprising a processor including a centralized learning and programming interface logic for simulating the actions of the feature extraction component, probability estimator component, and multitherapy activation component in order to improve the performance of each component.

13. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 12 wherein the processor is a laptop or workstation computer.

14. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 13 wherein the laptop or workstation computer is at a location different from that of the individual.

15. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 12 wherein the processor is located at a remote facility that is accessible over the Internet via a T1 line or other high speed digital link.

16. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 1 wherein the plurality of therapy actuators automatically apply the minimally required intervention measure, thereby minimizing associated side effects.

17. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 16 wherein the minimally required intervention measures starts with a benign form of therapy and continues with more aggressive therapies as the probability vector continuously changes with decreasing time to electrographic onset.

18. The system for automatically predicting and controlling the electrographic onset of a seizure of claim 16 wherein the minimally required intervention measures couples minimally invasive benign therapies to long prediction time intervals, aggressive therapies to short prediction time intervals and gradually varying moderate forms of therapy to prediction time intervals that are between the long and short prediction time intervals.