Analysis of EEG signals to detect hypoglycaemia
First Claim
1. A method for the analysis of EEG signals to detect features therein which are indicative of hypoglycaemia comprising:
- dividing EEG signals into a sequence of time segments,for each time segment determining whether a pattern of EEG signals is present which is indicative of hypoglycaemia and, where a pattern of EEG signals indicative of hypoglycaemia is determined to be present in a time segment, recording this as an event,integrating the number of events recorded during a selected number of preceding time segments which together constitute a selected time period, anddetermining that the EEG signals are indicative that hypoglycaemia is present based on said integration, wherein said integration is performed as a weighted integration in which events detected in time segments further back in time are given a lesser weighting than events detected in more recent time segments.
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Abstract
Features indicative of hypoglycaemia in EEG signals are detected by: —dividing EEG signals into a sequence of time segments, —for each time segment determining whether a pattern of EEG signals is present which is indicative of hypoglycaemia and, where a pattern of EEG signals indicative of hypoglycaemia is determined to be present in a time segment, recording this as an event, —integrating the number of events recorded during a selected number of preceding time segments which together constitute a selected time period, optionally in a time weighted manner, and—determining that the EEG signals are indicative that hypoglycaemia is present based on said integration when the said integrated number of events exceeds a preset threshold number and/or when there exists a threshold level of matching between a curve of said integration over time and a previously established ideal model of said curve indicative of hypoglycaemia.
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Citations
21 Claims
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1. A method for the analysis of EEG signals to detect features therein which are indicative of hypoglycaemia comprising:
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dividing EEG signals into a sequence of time segments, for each time segment determining whether a pattern of EEG signals is present which is indicative of hypoglycaemia and, where a pattern of EEG signals indicative of hypoglycaemia is determined to be present in a time segment, recording this as an event, integrating the number of events recorded during a selected number of preceding time segments which together constitute a selected time period, and determining that the EEG signals are indicative that hypoglycaemia is present based on said integration, wherein said integration is performed as a weighted integration in which events detected in time segments further back in time are given a lesser weighting than events detected in more recent time segments. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification