PATIENT STATE DETECTION BASED ON SUPPORT VECTOR MACHINE BASED ALGORITHM
First Claim
1. A method comprising:
- receiving a signal indicative of a parameter of a patient;
determining a feature vector based on the signal;
applying a support vector machine based algorithm to classify a patient state based on the feature vector, wherein the support vector machine based algorithm defines a classification boundary;
determining a distance between the feature vector and the classification boundary; and
determining an evaluation metric for the patient state based on the distance.
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Accused Products
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. 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.
25 Citations
24 Claims
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1. A method comprising:
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receiving a signal indicative of a parameter of a patient; determining a feature vector based on the signal; applying a support vector machine based algorithm to classify a patient state based on the feature vector, wherein the support vector machine based algorithm defines a classification boundary; determining a distance between the feature vector and the classification boundary; and determining an evaluation metric for the patient state based on the distance. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An implantable medical system comprising:
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a sensing module that generates a signal indicative of a parameter of a patient; and a processor that receives the signal, determines a feature vector based on the signal, applies a support vector machine-based algorithm to classify a patient state based on the feature, wherein the support vector machine-based algorithm defines a classification boundary, and determines an evaluation metric for the patient state based on a distance between the feature vector and the classification boundary. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A system comprising:
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means for receiving a signal indicative of a parameter of a patient; means for determining a feature vector based on the signal; means for applying a support vector machine based algorithm to classify a patient state based on the feature vector, wherein the support vector machine based algorithm defines a classification boundary; means for determining a distance between the feature vector and the classification boundary; and means for determining an evaluation metric for the patient state based on the distance. - View Dependent Claims (22)
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23. A computer-readable medium comprising instructions that cause a programmable processor to:
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receive a signal indicative of a parameter of a patient; determine a feature vector based on the signal; apply a support vector machine based algorithm to classify a patient state based on the feature vector, wherein the support vector machine based algorithm defines a classification boundary; determine a distance between the feature vector and the classification boundary; and determine an evaluation metric for the patient state based on the distance. - View Dependent Claims (24)
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Specification