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

• US 8,086,294 B2
• Filed: 08/13/2007
• Issued: 12/27/2011
• Est. Priority Date: 10/20/2000
• Status: Active Grant

• Current Assignee: Trustees Of The University Of Pennsylvania (University of Pennsylvania)
• Original Assignee: Trustees Of The University Of Pennsylvania (University of Pennsylvania)
• Inventors: Echauz, Javier Ramón, Litt, Brian, Esteller, Rosana, Vachtsevanos, George John
• Primary Examiner(s): NEGIN, RUSSELL SCOTT
• Application Number:
  US11/837,814
• Publication Number:
  US 20080021342A1
• Time in Patent Office: 1,597 Days
• Field of Search: None
• US Class Current: 600/378
• 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 system for assessing a quality of life index to adjust an implanted device to optimize patient-specific feature signals and treatment therapies. Accumulated energy of intracranial electroencephalogram (IEEG) signals is calculated over multiple data channels during seizures over a fixed time period. Accumulated energy of a treatment control is calculated over the multiple data channels over all times of activation of the implanted device over the fixed time period. The accumulated energy of both the IEEG signals and treatment control are weighted by seizure and treatment factors to determine a quality value for the fixed time period. A quality of life index is determined as a weighted average of current and previous quality values for a plurality of fixed time periods.

Claims

1. A method for assessing a quality of life index in an individual subject to seizures in order to adjust an implanted device to optimize patient-specific feature signals and treatment therapies, comprising the acts of:

calculating an accumulated energy of intracranial electroencephalogram (IEEG) signals for the individual over multiple data channels during seizures over a fixed time period;

calculating an accumulated energy of a treatment control over the multiple data channels over all times of activation of the implanted device over the fixed time period;

weighting the accumulated energy of the IEEG signals and the accumulated energy of the treatment control by seizure and treatment factors to determine a quality value for the fixed time period; and

determining the quality of life index as a weighted average of a current and previous quality values for a plurality of fixed time periods, wherein each of the above steps is executed on a programmed processor.

2. The method for assessing the quality of life index of claim 1 wherein the quality of life index is an exponentially-weighted moving average of a plurality of daily quality values which is based on a forgetting schedule to reduce the effect of older quality value data.

3. The method for assessing the quality of life index of claim 1 wherein the quality of life index is determined recursively using weights that are made to decay within four time constants of a natural exponential function.

4. A system for assessing a quality of life index in an individual subject to seizures in order to adjust an implanted device in order to optimize patient-specific feature signals and treatment therapies comprising:

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

a storage medium for storing (1) a calculated accumulated energy of intracranial electroencephalograms (IEEG) signals from the plurality of raw signals received on multiple data channels during seizures over a fixed time period, and (2) a calculated accumulated energy of a treatment control on the multiple data channels over all times of activation of the implanted device over a fixed time period;

a processor for executing a plurality of logic modules including;

a logic module for weighting the accumulated energy of the IEEG signals stored in the storage medium and the accumulated energy of the treatment control stored in the storage medium by seizure and treatment factors to determine a quality value for the fixed time period; and

a logic module for determining the quality of life index as a weighted average of a current and previous quality values for a plurality of fixed time periods.

5. The system for assessing the quality of life index of claim 4 wherein the logic module for determining the quality of life index uses an exponentially-weighted moving average of a plurality of daily quality values which is based on a forgetting schedule to reduce the effect of older quality value data.

6. The system for assessing the quality of life index of claim 4 wherein the quality of life index is determined recursively using weights that are made to decay within four time constants of a natural exponential function.

7. A non-transitory computer readable medium containing instructions for causing a computer system to assess a quality of life index in an individual subject to seizures in order to adjust an implanted device to optimize patient-specific feature signals and treatment therapies, the computer readable medium comprising:

program instructions that calculate an accumulated energy of intracranial electroencephalogram (IEEG) signals for the individual over multiple data channels during seizures over a fixed time period;

program instructions that calculate an accumulated energy of a treatment control over the multiple data channels over all times of activation of the implanted device over the fixed time period;

program instructions that weight the accumulated energy of the IEEG signals and the accumulated energy of the treatment control by seizure and treatment factors to determine a quality value for the fixed time period; and

program instructions that determine the quality of life index as a weighted average of a current and previous quality values for a plurality of fixed time periods.

8. The computer readable medium for causing a computer system to assess a quality of life index of claim 7 further comprising program instructions that determine the quality of life index as an exponentially-weighted moving average of a plurality of daily quality values which is based on a forgetting schedule to reduce the effect of older quality value data.

9. The computer readable medium for causing a computer system to assess a quality of life index of claim 7 wherein the program instructions that determine the quality of life index calculate the index recursively using weights that are made to decay within four time constants of an natural exponential function.