Detecting, assessing and managing epilepsy using a multi-variate, metric-based classification analysis
First Claim
1. A method comprising:
- detecting a plurality of seizure events based upon body data of the patient;
determining, for each seizure event, at least one seizure metric value characterizing the seizure event, wherein each of said at least one seizure metric values comprises one of an autonomic index, a neurologic index, a metabolic index, an endocrine index, a tissue index, or a tissue stress index;
performing a first classification analysis of a first portion of the plurality of seizure events, the first classification analysis comprising assigning each seizure event in the first portion to at least one seizure class based upon the proximity of the seizure metric values to each other;
performing a second classification analysis of a second portion of the plurality of seizure events, the second classification analysis comprising assigning each seizure event in the second portion to at least one seizure class based upon the proximity of the seizure metric values, wherein said second portion comprises at least one seizure event not present in the first portion;
comparing the results of the first classification analysis and the second classification analysis;
andperforming a further action selected from;
a. reporting a change from the first classification to the second classification;
b. reporting the absence of a change from the first classification to the second classification;
c. displaying a result of at least one of the first classification analysis, the second classification analysis, and the comparing;
d. identifying the emergence of a new class based on the comparing;
e. identifying the disappearance of a prior class based on the comparing;
f. identifying one or more outlier seizure events not part of any class;
g. identifying an effect of a therapy;
h. providing a therapy to the patient in response to the comparing;
i. identifying a proposed change in therapy;
j. identifying a proposed additional therapy;
k. identifying an extreme seizure event.l. identifying a worsening trend in the patient'"'"'s seizures;
m. identifying an improvement trend in the patient'"'"'s seizures;
n. downgrading the patient'"'"'s condition in response to a worsening in the patient'"'"'s seizures; and
o. upgrading the patient'"'"'s condition in response to an improvement in the patient'"'"'s seizures.
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Accused Products
Abstract
A method for identifying changes in an epilepsy patient'"'"'s disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.
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Citations
19 Claims
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1. A method comprising:
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detecting a plurality of seizure events based upon body data of the patient; determining, for each seizure event, at least one seizure metric value characterizing the seizure event, wherein each of said at least one seizure metric values comprises one of an autonomic index, a neurologic index, a metabolic index, an endocrine index, a tissue index, or a tissue stress index; performing a first classification analysis of a first portion of the plurality of seizure events, the first classification analysis comprising assigning each seizure event in the first portion to at least one seizure class based upon the proximity of the seizure metric values to each other; performing a second classification analysis of a second portion of the plurality of seizure events, the second classification analysis comprising assigning each seizure event in the second portion to at least one seizure class based upon the proximity of the seizure metric values, wherein said second portion comprises at least one seizure event not present in the first portion; comparing the results of the first classification analysis and the second classification analysis; and performing a further action selected from; a. reporting a change from the first classification to the second classification; b. reporting the absence of a change from the first classification to the second classification; c. displaying a result of at least one of the first classification analysis, the second classification analysis, and the comparing; d. identifying the emergence of a new class based on the comparing; e. identifying the disappearance of a prior class based on the comparing; f. identifying one or more outlier seizure events not part of any class; g. identifying an effect of a therapy; h. providing a therapy to the patient in response to the comparing; i. identifying a proposed change in therapy; j. identifying a proposed additional therapy; k. identifying an extreme seizure event. l. identifying a worsening trend in the patient'"'"'s seizures; m. identifying an improvement trend in the patient'"'"'s seizures; n. downgrading the patient'"'"'s condition in response to a worsening in the patient'"'"'s seizures; and o. upgrading the patient'"'"'s condition in response to an improvement in the patient'"'"'s seizures. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method comprising:
- detecting a plurality of seizure events in a first time period, wherein each of the seizure events is detected based upon body data of the patient;
determining at least one seizure metric value for each seizure event of the plurality of seizure events; performing a first classification analysis of a first portion of the plurality of seizure events, wherein the detection of each seizure in the first portion occurred within a second time period within said first time period, wherein said first classification analysis comprises identifying at least a first seizure class and a second seizure class based on the at least one seizure metric value, wherein the second seizure class comprises seizures that are more severe than seizures in the first seizure class; performing a second classification analysis of a second portion of the plurality of seizure events, wherein the detection of each seizure in the second portion occurred within a third time period, wherein said third time period is a period within said first time period and wherein at least a portion of said third time period is not within the second time period, wherein said second classification analysis comprises determining, for each seizure event in said third time period, whether the seizure event is within the first seizure class and within the second seizure class, based on the at least one seizure metric value; identifying at least one of a change in the first seizure class and the second seizure class between the first classification analysis and the second classification analysis; and
performing a responsive action based on the identifying.
- detecting a plurality of seizure events in a first time period, wherein each of the seizure events is detected based upon body data of the patient;
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14. A medical device, comprising:
- a seizure determination unit configured to detect a plurality of seizure events based upon body data of the patient;
a seizure metric determination unit configured to determine, for each seizure event, at least one seizure metric value characterizing the seizure event, wherein each of said at least one seizure metric values comprises one of an autonomic index, a neurologic index, a metabolic index, an endocrine index, a tissue index, or a tissue stress index; a seizure classification analysis unit configured to perform a first classification analysis of a first portion of the plurality of seizure events, the first classification analysis comprising assigning each seizure event in the first portion to at least one seizure class based upon the proximity of the seizure metric values to each other, and configured to perform a second classification analysis of a second portion of the plurality of seizure events, the second classification analysis comprising assigning each seizure event in the second portion to at least one seizure class based upon the proximity of the seizure metric values, wherein said second portion comprises at least one seizure event not present in the first portion; a classification analysis comparator configured to compare the results of the first classification analysis and the second classification analysis; and
a responsive action unit configured to perform at least one further action selected from;a. reporting a change from the first classification to the second classification; b. reporting the absence of a change from the first classification to the second classification; c. displaying a result of at least one of the first classification analysis, the second classification analysis, and the comparing; d. identifying the emergence of a new class based on the comparing; e. identifying the disappearance of a prior class based on the comparing; f. identifying one or more outlier seizure events not part of any class; g. identifying an effect of a therapy; h. providing a therapy to the patient in response to the comparing; i. identifying a proposed change in therapy; j. identifying a proposed additional therapy; k. identifying an extreme seizure event. l. identifying a worsening trend in the patient'"'"'s seizures; m. identifying an improvement trend in the patient'"'"'s seizures; n. downgrading the patient'"'"'s condition in response to a worsening in the patient'"'"'s seizures; and o. upgrading the patient'"'"'s condition in response to an improvement in the patient'"'"'s seizures. - View Dependent Claims (15, 16, 17, 18, 19)
- a seizure determination unit configured to detect a plurality of seizure events based upon body data of the patient;
Specification