Method for the real-time identification of seizures in an electroencephalogram (EEG) signal
First Claim
1. A method for the real-time identification of seizures in an Electroencephalogram (EEG) signal, the steps of the method comprising:
- (a) receiving an EEG signal comprising a plurality of channels of EEG data;
(b) for each of the plurality of channels of EEG data, segmenting the data into sequential epochs, each of the sequential epochs having an overlap with its neighboring sequential epochs;
and for an initial epoch of each of the plurality of channels performing the following steps comprising;
(c) extracting forty five or more features from each of the plurality of channels of EEG data;
(d) generating a feature vector from the extracted features;
(e) passing the feature vector for each of the plurality of channels of EEG data separately through a multi-patient trained generic Support Vector Machine (SVM) classifier and generating SVM channel seizure outputs for each feature vector, in which the multi-patient trained generic support vector machine classifier is trained on EEG data representing all seizure types, over all channels and over all patient types;
(f) fusing the SVM channel seizure outputs for all channels thereby generating an SVM epoch seizure output, the SVM epoch seizure output indicative of a seizure activity present in that epoch across all channels; and
(g) repeating steps (c) to (f) for each of the subsequent sequential epochs thereby generating a sequence of SVM channel seizure outputs and SVM epoch seizure outputs; and
(h) providing an SVM epoch decision to a user, the SVM epoch decision being indicative of whether the EEG data indicates the occurrence of a seizure or not.
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Abstract
The present invention relates to a method for the real-time identification of seizures in an Electroencephalogram (EEG) signal. The method provides for patient-independent seizure identification by use of a multi-patient trained generic Support Vector Machine (SVM) classifier. The SVM classifier is operates on a large feature vector combining features from a wide variety of signal processing and analysis techniques. The method operates sufficiently accurately to be suitable for use in a clinical environment. The method may also be combined with additional classifiers, such a Gaussian Mixture Model (GMM) classifier, for improved robustness, and one or more dynamic classifiers such as an SVM using sequential kernels for improved temporal analysis of the EEG signal.
13 Citations
19 Claims
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1. A method for the real-time identification of seizures in an Electroencephalogram (EEG) signal, the steps of the method comprising:
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(a) receiving an EEG signal comprising a plurality of channels of EEG data; (b) for each of the plurality of channels of EEG data, segmenting the data into sequential epochs, each of the sequential epochs having an overlap with its neighboring sequential epochs; and for an initial epoch of each of the plurality of channels performing the following steps comprising; (c) extracting forty five or more features from each of the plurality of channels of EEG data; (d) generating a feature vector from the extracted features; (e) passing the feature vector for each of the plurality of channels of EEG data separately through a multi-patient trained generic Support Vector Machine (SVM) classifier and generating SVM channel seizure outputs for each feature vector, in which the multi-patient trained generic support vector machine classifier is trained on EEG data representing all seizure types, over all channels and over all patient types; (f) fusing the SVM channel seizure outputs for all channels thereby generating an SVM epoch seizure output, the SVM epoch seizure output indicative of a seizure activity present in that epoch across all channels; and (g) repeating steps (c) to (f) for each of the subsequent sequential epochs thereby generating a sequence of SVM channel seizure outputs and SVM epoch seizure outputs; and (h) providing an SVM epoch decision to a user, the SVM epoch decision being indicative of whether the EEG data indicates the occurrence of a seizure or not. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. An apparatus for the real-time identification of seizures in an Electroencephalogram (EEG) signal, the apparatus comprising:
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means for receiving an EEG signal comprising a plurality of channels of EEG data; means for segmenting the data of each channel into a plurality of sequential epochs, each of the plurality of sequential epochs having an overlap with its neighboring plurality of sequential epochs; means for extracting forty five or more features from each of the plurality of channels of EEG data for each of the plurality of sequential epochs; means for generating a feature vector from the extracted features for each of the plurality of sequential epochs for each of the channels of EEG data; a multi-patient trained generic Support Vector Machine (SVM) classifier adapted to process the feature vector so as to generate SVM channel seizure outputs for each of the plurality of sequential epochs, wherein the multi-patient trained generic SVM classifier is trained on EEG data representing all seizure types, over all channels and over all patient types; and means for fusing the SVM channel seizure outputs for each of the channels thereby generating an SVM epoch seizure output indicative of a seizure activity present in that epoch across all channels; and means for providing an SVM epoch decision to a user, the SVM epoch decision being indicative of whether the EEG data indicates the occurrence of a seizure or not. - View Dependent Claims (16, 17, 18, 19)
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Specification