POSTURE STATE DETECTION
First Claim
1. A method comprising:
- receiving a signal indicative of a patient parameter;
receiving information identifying an occurrence of a posture state;
determining at least a first value of a characteristic of the signal that is indicative of the patient being in the posture state and at least a second value of the characteristic of the signal that is indicative of the patient not being in the posture state, wherein the first and second values are different; and
applying a supervised machine learning technique to define a classification boundary based on the first and second values of the characteristics of the signal, wherein a medical device utilizes the classification boundary to classify a subsequently sensed signal of the patient as indicative of the posture state.
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Abstract
A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. The patient state can be, for example, a patient posture state. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.
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Citations
34 Claims
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1. A method comprising:
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receiving a signal indicative of a patient parameter; receiving information identifying an occurrence of a posture state; determining at least a first value of a characteristic of the signal that is indicative of the patient being in the posture state and at least a second value of the characteristic of the signal that is indicative of the patient not being in the posture state, wherein the first and second values are different; and applying a supervised machine learning technique to define a classification boundary based on the first and second values of the characteristics of the signal, wherein a medical device utilizes the classification boundary to classify a subsequently sensed signal of the patient as indicative of the posture state. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system comprising:
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a sensing module that generates a signal indicative of a patient parameter; a processor that receives the signal indicative of the patient parameter, receives information identifying an occurrence of a posture state, determines at least a first value of a characteristic of the signal that is indicative of the patient being in the posture state and at least a second value of the characteristic of the signal that is indicative of the patient not being in the posture state, wherein the first and second values are different, and applies a supervised machine learning technique to define a classification boundary based on the first and second values of the characteristic of the signal; and a medical device that utilizes the classification boundary to classify a subsequently sensed signal of the patient as indicative of the posture state. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A method comprising:
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receiving a signal indicative of patient parameter; applying a classification algorithm determined based on a supervised machine learning technique to classify a patient posture state based on the signal, wherein the classification algorithm defines a classification boundary; and controlling therapy delivery to the patient based on the determined patient posture state. - View Dependent Claims (18, 19, 20, 21, 22)
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23. A system comprising:
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a therapy module that delivers therapy to a patient; a sensor that generates a signal indicative of patient posture; and a processor that applies a classification algorithm determined based on a supervised machine learning technique to classify a patient posture state based on the signal and controls the therapy module based on the determined patient posture state. - View Dependent Claims (24, 25, 26, 27, 28)
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29. A system comprising:
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means for receiving a signal indicative of a patient posture; means for receiving information identifying an occurrence of a posture state; means for determining at least a first value of a characteristic of the signal that is indicative of the patient being in the posture state and at least a second value of the characteristic of the signal that is indicative of the patient not being in the posture state, wherein the first and second values are different; and means for applying a supervised machine learning technique to define a classification boundary based on the first and second values of the characteristics of the signal, wherein a medical device utilizes the classification boundary to classify a subsequently sensed signal of the patient as indicative of the posture state. - View Dependent Claims (30)
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31. A system comprising:
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means for receiving a signal indicative of patient parameter; means for applying a classification algorithm determined based on a supervised machine learning technique to classify a patient posture state based on the signal, wherein the classification algorithm defines a classification boundary; and means for controlling therapy delivery to the patient based on the determined patient posture state. - View Dependent Claims (32)
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33. A computer-readable medium comprising instructions that cause a programmable processor to:
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receive a signal indicative of a patient posture; receive information identifying an occurrence of a posture state; determine at least a first value of a characteristic of the signal that is indicative of the patient being in the posture state and at least a second value of the characteristic of the signal that is indicative of the patient not being in the posture state, wherein the first and second values are different; and apply a supervised machine learning technique to define a classification boundary based on the first and second values of the characteristics of the signal, wherein a medical device utilizes the classification boundary to classify a subsequently sensed signal of the patient as indicative of the posture state.
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34. A computer-readable medium comprising instructions that cause a programmable processor to:
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receive a signal indicative of patient parameter; apply a classification algorithm determined based on a supervised machine learning technique to classify a patient posture state based on the signal, wherein the classification algorithm defines a classification boundary; and control therapy delivery to the patient based on the determined patient posture state.
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Specification