Methods for modeling neurological development and diagnosing a neurological impairment of a patient
Inventors
Le, Tan • Mackellar, Geoffrey Ross
Assignees
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Publication Number
US-11553870-B2
Publication Date
2023-01-17
Expiration Date
Abstract
One variation of a method for modeling neurological development includes: aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses; identifying a synchronization pattern trend within the EEG data of the set of users; and correlating the synchronization pattern trend with neurological development within the set of users.
Core Innovation
The invention provides a method for determining a predicted neurological development outcome of a first user using noninvasive recording with a neuroheadset. For each user in a set of users in a user base, EEG signals are recorded at each time point of a first set of discrete time points, and for each EEG signal, a synchronization pattern is determined. A synchronization pattern trend is then determined based on the set of synchronization patterns.
For the set of users, synchronization pattern trends are organized into groups, and a tag is assigned to each group, where the tag is associated with a neurological development outcome. A neurological development model is determined for each group, and each neurological development model is configured to determine a predicted neurological development outcome. For the first user, a second set of EEG signals is recorded at discrete time points after the first set.
The method determines a first user synchronization pattern trend based on the second set of EEG signals and compares the first user synchronization pattern trend with the set of neurological development models. Correlations are determined based on comparing the first user synchronization pattern trend with the neurological development models, and the predicted neurological development outcome is determined based on the set of correlations. The method provides a notification and receives an input from the first user at an application, with the input used to add the first user synchronization pattern trend to a first group.
Claims Coverage
The document contains two independent claims. Each independent claim covers the same core workflow: recording EEG signals over discrete time points, deriving synchronization patterns and synchronization pattern trends, grouping trends with neurological development outcome tags, building group-specific neurological development models, and predicting a first user outcome by correlating a later synchronization trend with the models.
Group-based neurological development modeling from EEG synchronization trends
For each user of a set of users, recording EEG signals at discrete time points with a neuroheadset; determining a synchronization pattern for each EEG signal; determining a synchronization pattern trend based on the set of synchronization patterns; organizing a set of the synchronization pattern trends into a set of groups; assigning a tag to each group associated with a neurological development outcome; and determining a neurological development model for each group configured to determine a predicted neurological development outcome.
Correlation-based prediction for a first user using later synchronization pattern trends
For the first user, recording a second set of EEG signals at discrete time points occurring after the first set of discrete time points at a neuroheadset; determining a first user synchronization pattern trend based on the second set of EEG signals; comparing the first user synchronization pattern trend with the set of neurological development models; determining a set of correlations based on comparing the first user synchronization pattern trend with the set of neurological development models; and determining the predicted neurological development outcome based on the set of correlations.
Notification and user input for updating group assignment
Providing a notification to the first user at an application executing on a user device associated with the first user; receiving an input from the first user at the application; and adding the first user synchronization pattern trend to a first group of the set of groups based on the input.
Overall, the independent claims cover group-specific neurological development models built from synchronization pattern trends derived from EEG signals, and a prediction stage that uses correlations between a first user’s later synchronization pattern trend and the neurological development models, with a notification/input mechanism for updating group assignment in one claim.
Stated Advantages
Not explicitly described in patent.
Documented Applications
Not explicitly described in patent.
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