EP-4734848-A1 - SEIZURE DETECTION AI

EP4734848A1EP 4734848 A1EP4734848 A1EP 4734848A1EP-4734848-A1

Abstract

A method (100) for training an artificial intelligence to detect seizures using a first EEG sensor is provided. The method comprises steps of: receiving (S110) seizure-specific data (20) sensed using a second EEG sensor of a different type than the first EEG sensor; manipulating (S120) the seizure-specific data (20) into primary data (21), which corresponds to the seizure-specific data (20) having been sensed using the first EEG sensor; receiving (S130) secondary data (10) sensed using the first EEG sensor; detecting (S140) seizures in the secondary data (10); separating (S150) the secondary data (10) into active data (11), which comprises the detected seizures, and inactive data (12), which does not comprise the detected seizures; generating (S160) a training set (30) for the artificial intelligence by combining the inactive data (12) and the primary data (21); and training (S170) the artificial intelligence using the training set (30). A method (200) for detecting seizures using a first EEG sensor is further provided.

Inventors

MADSEN, RASMUS ELSBORG
GANGSTAD, Sirin Wilhelmsen
VEDEL-LARSEN, Esben
MOELLER, Jakob Skadkaer
ØLUND, Thomas
MARIBOE, Michael Feveile

Assignees

UNEEG Medical A/S

Dates

Publication Date: 20260506
Application Date: 20240620

Claims (1)

CLAIMS 1 . A method (100) for training an artificial intelligence to detect seizures using a first EEG sensor, the method comprising steps of: receiving (S110) seizure-specific data (20) sensed using a second EEG sensor of a different type than the first EEG sensor; manipulating (S120) the seizure-specific data (20) into primary data (21 ), which corresponds to the seizure-specific data (20) having been sensed using the first EEG sensor; receiving (S130) secondary data (10) sensed using the first EEG sensor; detecting (S140) seizures in the secondary data (10); separating (S150) the secondary data (10) into active data (11 ), which comprises the detected seizures, and inactive data (12), which does not comprise the detected seizures; generating (S160) a training set (30) for the artificial intelligence by combining the inactive data (12) and the primary data (21 ); and training (S170) the artificial intelligence using the training set (30). 2. The method according to claim 1 , wherein the seizure-specific data (20) comprises at least two different types of seizures. 3. The method according to claim 1 or 2, wherein the secondary data (10) comprises data collected during at least one continuous week. 4. The method according to any one of the preceding claims, wherein the inactive data (12) is at least 10000 times larger than the active data (11 ). 5. The method according to any one of the preceding claims, wherein the seizure-specific data (20) comprises multi-rater labels. 6. The method according to any one of the preceding claims, further comprising steps of: generating (S180) a validation set (40) by combining inactive data (12) and active data (11 ); and fine-tuning (S190) the artificial intelligence using the validation set (40). 7. The method according to any one of the preceding claims, wherein generating (S160) a training set (30) for the artificial intelligence comprises combining an amount of the inactive data (12) and an equivalent amount of a combination of the active data (11 ) and the primary data (21 ). 8. The method according to any one of the preceding claims, wherein the first EEG sensor comprises subcutaneous EEG electrodes. 9. The method according to any one of the preceding claims, wherein the first EEG sensor is limited to sensing an area above and around a temporal lobe of a user. 10. The method according to any one of the preceding claims, wherein detecting (S140) seizures in the secondary data (10) comprises transforming the secondary data (10) into images (15) and using image processing techniques to detect seizures in the images (15). 11 . The method according to claim 10, wherein detecting (S140) seizures in the secondary data (10) further comprises transforming segments (16) of the secondary data (10) between 90 and 150 seconds long into images (15) and using image processing techniques on the images (15) to detect seizures in the images (15). 12. The method according to claim 11 , wherein each sequential segment (16) overlaps by at least 60 seconds to the preceding segment (16). 13. The method according to any one of the preceding claims, wherein manipulating (S120) the seizure-specific data (20) into primary data (21 ) comprises simulating how the seizure-specific data (20) would appear if sensed using the first EEG sensor by removing data beyond the sensing range of the first EEG sensor and adding noise and/or artefacts specific to the first EEG sensor. 14. A method (200) for detecting seizures using a first EEG sensor, the method (200) comprising steps of: training (100) an artificial intelligence to detect seizures according to any one of the preceding claims; transmitting (S210) user data (50) sensed by the first EEG sensor to the trained artificial intelligence; and using (S220) the trained artificial intelligence to detect seizures within the transmitted user data (50). 15. The method according to claim 14, further comprising a step of alerting (S230) a user or caretaker that a seizure has been detected.

Description

SEIZURE DETECTION Al Technical Field The present inventive concept relates to methods for training an artificial intelligence (Al) to detect seizures. In particular, the present inventive concept relates to methods for training an Al to detect seizures using a non-standardized electroencephalogram (EEG) sensor. Background In order to train an Al, a lot of data is needed. There exists a lot of measured data for seizures, but this data is of a very specific type and is not useable for a nonstandardized EEG sensor. Summary It is thereby an object of the present inventive concept to make use of the available measured data for seizures. Another object of the present inventive concept is to make use of long-term EEG data, which is not typically available in the same data sets as the measured data for seizures. These and other objects are achieved by the features set out in the appended independent claims, with embodiments set out in the dependent claims. Accordingly, a first aspect of the inventive concept is provided by a method for training an artificial intelligence (Al) to detect seizures using a first EEG sensor. The method comprises steps of: receiving seizure-specific data sensed using a second EEG sensor of a different type than the first EEG sensor; manipulating the seizurespecific data into primary data, which corresponds to the seizure-specific data having been sensed using the first EEG sensor; receiving secondary data sensed using the first EEG sensor; detecting seizures in the secondary data; separating the secondary data into active data, which comprises the detected seizures, and inactive data, which does not comprise the detected seizures; generating a training set for the artificial intelligence by combining the inactive data and the primary data; and training the artificial intelligence using the training data. The first EEG sensor may be any EEG sensor, including non-standardized EEG sensors. The second EEG sensor is typically a standardized EEG sensor used to collect large data sets of seizure-specific data. Manipulating the seizure-specific data into data corresponding to having been sensed using the first EEG sensor may comprise removing some amount of data that would not be measurable by the first EEG sensor, and/or transforming some of the data to better match the measurements made by the first EEG sensor. The secondary data may comprise all data sensed by the first EEG sensor, i.e. not only seizure data but also inactive data. By training the Al also on inactive data, the training is improved by having a more realistic non-seizure data. The method may be implemented in any processing circuit and the trained artificial intelligence may be stored in a memory circuit either locally or on a (remote) server. The seizure-specific data may comprise at least two different types of seizures. There are many types of seizures, such as temporal lobe seizures, frontal lobe seizures and generalized seizures, so training the Al on at least two different types makes the detection of the Al more reliable. The secondary data may comprise data collected during at least one continuous week. By training the Al with data collected during at least a full week, the data contains normal everyday activity not normally collected at hospitals during seizure data collection. Further, by having access to more long-term data, long-term effects leading to seizures may be trained to be detected by the Al. The inactive data may be at least 10000 times larger than the active data. Seizures are generally rare, so training the Al on realistic amounts of data will provide more accurate detection. Further, training the algorithm to better recognize a non-seizure lowers the rate of incorrect detections (i.e. false positives or false negatives). Additionally, it may be easier to acquire inactive data. The seizure-specific data may comprise multi-rater labels. Labelled data, and especially multi-rater labelled data, may be used as absolute truth when training the Al to improve the accuracy of the training. The method may comprise steps of: generating a validation set by combining inactive data and active data; and fine tuning the artificial intelligence using the validation set. By using a validation set, the detection of the Al is improved by the fine-tuning made available by the validation set. Using the inactive and active data when generating the validation set has shown to be effective. Generating a training set for the artificial intelligence may comprise combining an amount of the inactive data and an equivalent amount of a combination of the active data and the primary data. Using active data when generating the training set has shown to improve the detection of the trained Al. Further, equivalent amounts of non-seizure data and seizure data has shown to improve the detection of the trained Al. The first EEG sensor may be portable and/or the second EEG sensor may be non-portable. Portability may comprise a lack of