METHOD AND APPARATUS FOR DETECTION OF NERVOUS SYSTEM DISORDERS

• US 20070265544A1
• Filed: 12/12/2006
• Published: 11/15/2007
• Est. Priority Date: 04/21/2006
• Status: Active Grant

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Panken, Eric J., Carlson, David L.
• Granted Patent: US 7,764,989 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• 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 1/36064 Epilepsy
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
• CPC Class Codes: A61N 1/37252 Details of algorithms or data aspects of communication system, e.g. handshaking, transmitting specific data or segmenting data
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Claims

1. A method of detecting a precursor to a neurological event, the method comprising:

sampling an electroencephalograph (EEG) signal to obtain a stream of data values;

counting changes in data value amplitude between at least a first amplitude range and a second amplitude range; and

detecting a precursor to a neurological event when the change count exceeds a predetermined precursor threshold.

2. The method of claim 1 wherein one of the amplitude ranges is adapted to identify epileptiform discharges.

3. The method of claim 1 further comprising a third amplitude range, wherein

one of the amplitude ranges is adapted to identify positive epileptiform discharges, and

one of the amplitude ranges is adapted to identify negative epileptiform discharges.

4. A method of detecting a precursor to a neurological event, the method comprising:

sampling an electroencephalograph (EEG) signal to obtain a stream of data values;

applying the data values to a level transform having a level threshold defining two amplitude ranges, the level transform adapted to produce a stream of output values, each output value identifying the amplitude range corresponding to each data value;

applying the stream of output values to a change detector, the change detector adapted to produce a stream of change signal values, a change signal value having a first value if a current output value is different from an immediately preceding output value, and having a second value if the current value is the same as the immediately preceding value;

counting the first values that occur in a predefined window; and

detecting a precursor to a neurological event when the count of first values exceeds a predetermined precursor threshold.

5. The method of claim 4 wherein the first value is 1, and the second value is 0, and

wherein counting the first values comprises computing a sum of the change signal values in a predefined window.

6. The method of claim 5 wherein the change signal values have a third value greater than 1 if the current output value is different from an immediately preceding output value by more than a predetermined amount.

7. The method of claim 4 wherein counting the first values in a predefined window comprises:

computing a block sum of N change signal values;

computing a rolling sum of M block sums to form a precursor trend count; and

detecting a precursor to a neurological event when the precursor trend count exceeds a predetermined precursor threshold.

8. The method of claim 7 wherein successive block sums are determined using non-overlapping blocks of N change signal values.

9. The method of claim 8 wherein the rolling sum is formed from M consecutive block sum values.

10. The method of claim 4 wherein the neurological event is a seizure.

11. The method of claim 4 where the level threshold is a multiple of a long-term representation of the EEG signal.

12. The method of claim 11 wherein the long-term representation is a background level signal.

13. The method of claim 4 wherein the level transform has two or more level thresholds defining at least 3 amplitude ranges.

14. The method of claim 13 wherein the at least 3 amplitude ranges include a high, a medium, and a low amplitude range.

15. The method of claim 13 wherein the at least 3 amplitude ranges include a high positive, a middle, and a high negative amplitude range.

16. The method of claim 15 wherein the stream of output values produced by the level transform comprises:

a positive 1 for each data value corresponding to the high positive amplitude range, a 0 for each data value corresponding to the middle amplitude range, and

a negative 1 for each data value corresponding to the high negative amplitude range.

17. A computer-readable medium programmed with instructions for performing a method of detecting a precursor to a neurological event, the medium comprising instructions for causing a programmable processor to:

sample an electroencephalograph (EEG) signal to obtain a stream of data values;

count changes in data value amplitude between at least a first amplitude range and a second amplitude range, the amplitude ranges being chosen to differentiate between normal EEG signal amplitudes and epileptiform discharges; and

detect a precursor to a neurological event when the change count exceeds a predetermined precursor threshold.

18. A computer-readable medium programmed with instructions for performing a method of detecting a precursor to a neurological event, the medium comprising instructions for causing a programmable processor to:

sample an electroencephalograph (EEG) signal to obtain a stream of data values;

apply the data values to a level transform having a level threshold, the level threshold defining two amplitude ranges, the level transform adapted to produce a stream of output values, each output value identifying the amplitude range corresponding to each data value;

apply the stream of output values to a change detector, the change detector adapted to produce a stream of change signal values, a change signal value having a first value if a current output value is different from an immediately preceding output value, and having a second value if the current value is the same as the immediately preceding value;

count the first values that occur in a predefined window; and

detect a precursor to a neurological event when the count of first values exceeds a predetermined precursor threshold.

19. The medium of claim 18 wherein the first value is 1, and the second value is 0, and further comprising instructions to count the first values by computing a sum of the change signal values in a predefined window.

20. The medium of claim 18 further comprising instructions to

compute a block sum of N change signal values;

compute a rolling sum of M block sums to form a precursor trend count; and

detect a precursor to a neurological event when the precursor trend count exceeds a predetermined precursor threshold.

21. A system for detecting a precursor to a neurological event, comprising:

an implantable lead adapted to sense EEG signals from a brain of a patient; and

an implantable medical device (IMD) in communication with the implantable lead and adapted to receive EEG signals, the IMD having a power source, memory, and electronic circuitry adapted to

sample an electroencephalograph (EEG) signal to obtain a stream of data values;

apply the data values to a level transform having a level threshold, the level threshold defining two amplitude ranges, the level transform adapted to produce a stream of output values, each output value identifying the amplitude range corresponding to each data value;

apply the stream of output values to a change detector, the change detector adapted to produce a stream of change signal values, a change signal value having a first value if a current output value is different from an immediately preceding output value, and having a second value if the current value is the same as the immediately preceding value;

count the first values that occur in a predefined window; and

detect a precursor to a neurological event when the count of first values exceeds a predetermined precursor threshold.

22. The system claim 21 wherein the IMD is adapted to deliver a therapy when a precursor to a neurological event has been detected.

23. The system of claim 21 wherein the IMD is adapted to deliver electrical stimulation therapy via the implantable lead.

24. The system of claim 21 wherein the IMD is adapted to charge stimulation circuitry when a precursor to a neurological event has been detected.