Noninvasive Transabdominal Fetal Electroencephalography
AssigneeYale University
InventorsJose Cortes-Briones, Emily Lee
Life of this patent
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Abstract
Examples described herein provide a computer-implemented method that includes receiving a noninvasive transabdominal fetal electroencephalography (TA-fEEG) signal associated with a pregnant subject. The method further includes reducing unwanted noise in the TA-fEEG signal using a first machine learning model. The method further includes reconstructing a fetal electroencephalography (fEEG) signal from the TA-fEEG signal using a second machine learning model.
Claims20
A computer-implemented method comprising:
- receiving a noninvasive transabdominal fetal electroencephalography (TA-fEEG) signal associated with a pregnant subject;
- reducing unwanted noise in the TA-fEEG signal using a first machine learning model;
- and
- reconstructing a fetal electroencephalography (fEEG) signal from the TA-fEEG signal using a second machine learning model.
A system for noninvasive transabdominal fetal electroencephalography (TA-fEEG), the system comprising:
- a noninvasive sensing device comprising a sensor to detect TA-fEEG signals of a pregnant subject;
- and
- a processing system in communication with the sensor, the processing system comprising a memory for storing computer readable instructions and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations comprising:
- removing, using a first machine learning model, artefacts from the TA-fEEG signals, the artefacts caused by maternal and fetal cardiac activity and movement;
- separating, using a second machine learning model, independent sources of activity embedded in the TA-fEEG signals;
- and
- predicting, based at least in part on at least one of the independent sources of activity embedded in the TA-fEEG signals, a likelihood of fetal hypoxia of a fetus of the pregnant subject.
A computer-implemented method for training machine learning models, the method comprising:
- training a first machine learning model to reduce unwanted noise in a noninvasive transabdominal fetal electroencephalography (TA-fEEG) signal;
- and
- training a second machine learning model to reconstruct a fetal electroencephalography (fEEG) signal from the TA-fEEG signal.
Dependencies are inferred from the claim text.