Method, system, and computer-accessible medium for classification of at least one ICTAL state
First Claim
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1. A method for classifying at least one state of a subject, comprising:
- receiving physiological data for the subject, the physiological data including a plurality of features having at least a first feature and a second feature that are based on a respective measure of a synchronization for at least one pair of channels, wherein the synchronization associated with (i) the first feature is over a first time period; and
(ii) the second feature is over a second time period;
generating an array of the features having the first and second features as entries in the array;
generating a multilayer convolutional neural network, wherein a first layer and a second layer of the multilayer convolutional neural network is based on a convolution across time, and a third layer of the multilayer convolutional neural network is based on a convolution across time, space and frequency; and
using a computer hardware arrangement, classifying the at least one state based on information provided in the array using the at least one multilayer convolutional neural network.
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Abstract
An exemplary methodology, procedure, system, method and computer-accessible medium can be provided for receiving physiological data for the subject, extracting one or more patterns of features from the physiological data, and classifying the at least one state of the subject using a spatial structure and a temporal structure of the one or more patterns of features, wherein at least one of the at least one state is an ictal state.
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Citations
49 Claims
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1. A method for classifying at least one state of a subject, comprising:
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receiving physiological data for the subject, the physiological data including a plurality of features having at least a first feature and a second feature that are based on a respective measure of a synchronization for at least one pair of channels, wherein the synchronization associated with (i) the first feature is over a first time period; and
(ii) the second feature is over a second time period;generating an array of the features having the first and second features as entries in the array; generating a multilayer convolutional neural network, wherein a first layer and a second layer of the multilayer convolutional neural network is based on a convolution across time, and a third layer of the multilayer convolutional neural network is based on a convolution across time, space and frequency; and using a computer hardware arrangement, classifying the at least one state based on information provided in the array using the at least one multilayer convolutional neural network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A non-transitory computer-accessible medium for classifying at least one state of a subject, the computer-accessible medium including instructions thereon, wherein, when a computing arrangement executes the instructions, the computing arrangement is configured to perform procedures comprising:
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receiving physiological data for the, the physiological data including a plurality of features having at least a first feature and a second feature that are based on a respective measure of a synchronization for at least one pair of channels, wherein the synchronization associated with (i) the first feature is over a first time period; and
(ii) the second feature is over a second time period;generating an array of the features having the first and second features as entries in the array; generating a multilayer convolutional neural network, wherein a first layer and a second layer of the multilayer convolutional neural network is based on a convolution across time, and a third layer of the multilayer convolutional neural network is based on a convolution across time, space and frequency; and classifying the at least one state based on the array using the at least one multilayer convolutional neural network. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A system for classifying at least one state of a subject, which comprises:
a computer hardware arrangement configured to; receive physiological data for the, the physiological data including a plurality of features having at least a first feature and a second feature that are based on a respective measure of a synchronization for at least one pair of channels, wherein the synchronization associated with (i) the first feature is over a first time period; and
(ii) the second feature is over a second time period;generate an array of the features having the first and second features as entries in the array; generate a multilayer convolutional neural network, wherein a first layer and a second layer of the multilayer convolutional neural network is based on a convolution across time, and a third layer of the multilayer convolutional neural network is based on a convolution across time, space and frequency; and classify the at least one state based on the array using a trained classifier module that uses the at least one multilayer convolutional neural network. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49)
Specification