Method and apparatus utilizing computational intelligence to diagnose neurological disorders
First Claim
1. A method of diagnosing patients suspected of having a neurological disorder, comprising the steps of:
- a) monitoring movement of a patient in order to obtain movement data that is representative of said movement of said patient;
b) processing said movement data in order to obtain an input pattern that is representative of said movement data;
c) processing said input pattern with a computational intelligence system that has been trained to classify movement based upon a predetermined group of neurological disorder classifications; and
d) generating with said computational intelligence system an output that is indicative of an appropriate neurological disorder classification for said patient.
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Abstract
A method of diagnosing patients suspected of having a neurological disorder is disclosed and includes monitoring movement of a patient in order to obtain movement data that is representative of the movement of the patient. Another step of the method includes processing the movement data in order to obtain an input pattern that is representative of the movement data. The method also includes the step of processing the input pattern with a computational intelligence system that has been trained to classify movement based upon a predetermined group of neurological disorder classifications. Furthermore, the method includes generating with the computational intelligence system an output that is indicative of an appropriate neurological disorder classification for the patient. An analysis system for diagnosing patients suspected of having a neurological disorder is also disclosed.
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Citations
30 Claims
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1. A method of diagnosing patients suspected of having a neurological disorder, comprising the steps of:
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a) monitoring movement of a patient in order to obtain movement data that is representative of said movement of said patient;
b) processing said movement data in order to obtain an input pattern that is representative of said movement data;
c) processing said input pattern with a computational intelligence system that has been trained to classify movement based upon a predetermined group of neurological disorder classifications; and
d) generating with said computational intelligence system an output that is indicative of an appropriate neurological disorder classification for said patient. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
generating an analog movement signal having an amplitude that varies with respect to time and that is representative of said movement of said patient; and
sampling said analog movement signal at a predetermined sampling rate in order to obtain a plurality of digital samples that are representative of said movement of said patient.
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3. The method of claim 1, wherein step b) comprises the steps of:
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extracting characteristics of said movement from said movement data, and generating said input pattern based upon said characteristics extracted from said movement data.
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4. The method of claim 1, wherein step b) comprises the steps of:
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extracting first characteristics of said movement from first data of said movement data associated with a first time interval;
extracting second characteristics of said movement from second data of said movement data associated with a second time interval; and
generating said input pattern based upon said first characteristics and said second characteristics of said movement.
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5. The method of claim 1, wherein step a) comprises the step of:
collecting said movement data such that said movement data comprises a plurality of postural data sets that are each representative of a different postural tremor type.
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6. The method of claim 5, wherein step b) comprises the steps of:
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extracting first characteristics of said movement from a first postural data set of said plurality of postural data sets;
extracting second characteristics of said movement from a second postural data set of said plurality of postural data sets; and
generating said input pattern based upon said first characteristics and said second characteristics.
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7. The method of claim 1, wherein step b) comprises the steps of:
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extracting frequency characteristics of said movement from said movement data, and generating said input pattern based upon said frequency characteristics of said movement data.
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8. The method of claim 1, wherein step b) comprises the steps of:
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extracting power spectral density characteristics of said movement from said movement data, and generating said input pattern based upon said power spectral density characteristics of said movement data.
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9. The method of claim 1, wherein step b) comprises the steps of:
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extracting statistical characteristics of said movement from said movement data, and generating said input pattern based upon said statistical characteristics of said movement.
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10. The method of claim 1, wherein step b) comprises the steps of:
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extracting first frequency characteristics of said movement from first data of said movement data associated with a first time interval, extracting first statistical characteristics of said movement from said first data of said movement data associated with said first time interval, extracting second frequency characteristics of said movement from second data of said movement data associated with a second time interval, extracting second statistical characteristics of said movement from said second data of said movement data associated with said second time interval, and generating said input pattern based upon said first frequency characteristics, said first statistical characteristics, said second frequency characteristics, and said second statistical characteristics of said movement.
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11. The method of claim 1, wherein step c) comprises the step of processing said input pattern with a neural network of said computational intelligence system that is trained to classify movement.
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12. The method of claim 1, wherein step c) comprises the step of:
classifying said movement of said patient with said computational intelligence system based upon said predetermined group of neurological disorder classifications which comprises a normal tremor classification and a non-normal tremor classification.
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13. The method of claim 1, wherein step c) comprises the step of:
classifying said movement of said patient with said computational intelligence system based upon said predetermined group of neurological disorder classifications which comprises a normal classification and a Parkinson'"'"'s disease classification.
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14. The method of claim 1, wherein step c) comprises the step of:
classifying said movement of said patient with said computational intelligence system based upon said predetermined group of neurological disorder classifications comprising a normal classification, a Parkinson'"'"'s disease classification, and an essential tremor classification.
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15. An analysis system for diagnosing patients suspected of having a neurological disorder, comprising:
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a movement monitoring device operable to monitor movement of a patient over a collection period in order to obtain movement data that is representative of said movement of said patient over said collection period;
a preprocessor operable to generate an input pattern that is representative of said movement data collected by said movement monitoring device over said collection period; and
a computational intelligence system comprising a neural network that has been trained to classify movement based upon a predetermined group of neurological disorder classifications, said neural network operable to (i) process said input pattern generated by said preprocessor, and (ii) generate an output that is indicative of an appropriate neurological disorder classification for said patient. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23)
generate an analog movement signal having an amplitude with respect to time that is indicative of said movement of said patient with respect to time, and sample said analog movement signal at a predetermined sampling rate in order to obtain a plurality of digital samples that are representative of said movement of said patient.
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17. The analysis system of claim 15, wherein said preprocessor is further operable to:
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extract characteristics of said movement from said movement data, and generate said input pattern based upon said characteristics extracted from said movement data.
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18. The analysis system of claim 15, wherein said preprocessor is further operable to:
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extract first characteristics of said movement from first data of said movement data associated with a first time interval;
extract second characteristics of said movement from second data of said movement data associated with a second time interval;
generate said input pattern based upon said first characteristics and said second characteristics of said movement.
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19. The analysis system of claim 15, wherein said movement monitoring device is further operable to collect said movement data such that said movement data comprises a plurality of postural data sets that are each representative of a different postural tremor type.
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20. The analysis system of claim 19, wherein said preprocessor is further operable to:
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extract first characteristics of said movement from a first postural data set of said plurality of postural data sets;
extract second characteristics of said movement from a second postural data set of said plurality of postural data sets;
generate said input pattern based upon said first characteristics and said second characteristics of said movement.
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21. The analysis system of claim 15, wherein said neural network of said computational intelligence system is further operable to:
classify said movement of said patient based upon said input pattern and said predetermined group of neurological disorder classifications comprising a normal tremor classification and a non-normal tremor classification.
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22. The analysis system of claim 15, wherein said neural network of said computational intelligence system is further operable to:
classify said movement of said patient based upon said input pattern and said predetermined group of neurological disorder classifications which comprises a normal classification and a Parkinson'"'"'s disease classification.
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23. The analysis system of claim 15, wherein said neural network of said computational intelligence system is further operable to:
classify said movement of said patient based upon said input pattern and said predetermined group of neurological disorder classifications which comprises a normal classification, a Parkinson'"'"'s disease classification, and an essential tremor classification.
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24. A computer readable medium that configures an analysis system for diagnosing patients suspected of having a neurological disorder, comprising a plurality of instructions which when executed by said analysis system causes said analysis system to:
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generate an input pattern based upon movement data that is representative of movement of a patient over a collection period;
implement a neural network trained to classify said movement of said patient based upon a predetermined group of neurological disorder classifications;
process said input pattern with said neural network to obtain an appropriate neurological disorder classification for said patient; and
display output providing an indication of said appropriate neurological disorder classification for said patient. - View Dependent Claims (25, 26, 27, 28, 29, 30)
extract characteristics of said movement from said movement data, and generate said input pattern based upon said characteristics extracted from said movement data.
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26. The computer readable medium of claim 24, wherein said plurality of instructions when executed by said analysis system, further cause said analysis system to:
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extract first characteristics of said movement from first data of said movement data associated with a first time interval;
extract second characteristics of said movement from second data of said movement data associated with a second time interval;
generate said input pattern based upon said first characteristics and said second characteristics of said movement.
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27. The computer readable medium of claim 24, wherein:
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said movement data comprises a first postural data set representative of a first postural tremor type, and a second postural data set representative of a second postural tremor type, and said plurality of instructions when executed by said analysis system, further cause said analysis system to;
extract first characteristics of said movement from said first postural data set;
extract second characteristics of said movement from said second postural data set;
generate said input pattern based upon said first characteristics and said second characteristics of said movement.
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28. The computer readable medium of claim 24, wherein said plurality of instructions when executed by said analysis system, further cause said analysis system to:
classify said movement based upon said predetermined group of neurological disorder classifications comprising a normal tremor classification and a non-normal tremor classification.
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29. The computer readable medium of claim 24, wherein said plurality of instructions when executed by said analysis system, further cause said analysis system to:
classify said movement based upon said predetermined group of neurological disorder classifications which comprises a normal classification and a Parkinson'"'"'s disease classification.
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30. The computer readable medium of claim 24, wherein said plurality of instructions when executed by said analysis system, further cause said analysis system to:
classify said movement based upon said predetermined group of neurological disorder classifications which comprises a normal classification, a Parkinson'"'"'s disease classification, and an essential tremor classification.
Specification