Physiological metrics for determining stroke risk

A system for determining stroke risk, the system comprising:

• a headband configured to encircle a head of a wearer of a headset;
• a plurality of light sources attached to the headband;
• a plurality of light detectors attached to the headband;
• and
• one or more processors configured to:
• cause, using the one or more light sources, light to be emitted into the head of the wearer;
• obtain, using the one or more light detectors, information indicative of light reflected from one more structures within the head of the wearer, wherein a portion of the obtained information spans a time period during which the wearer was holding their breath;
• based on the obtained information, determine one or more cerebral blood metrics;
• determine a likelihood the wearer will experience a stroke over a predetermined future time period based on the one or more cerebral blood metrics.

A method of determining stroke risk, the method comprising:

• causing, using one or more light sources disposed on a headset worn by a user, light to be emitted into the head of the user;
• obtaining, using one or more light detectors disposed on the headset, information indicative of light reflected from one more structures within the head of the user, wherein a portion of the obtained information spans a time period during which the user was holding their breath;
• based on the obtained information, determining a cerebral blood flow as a function of time and a cerebral blood volume as a function of time, wherein the cerebral blood flow and the cerebral blood volume include the time period during which the user was holding their breath and a baseline time period before the user was holding their breath;
• and
• determining a likelihood the user will experience a stroke over a predetermined future time period based on the cerebral blood flow and the cerebral blood volume.

A method of training a machine learning model to predict stroke risk, the method comprising:

• obtaining training data, the training data comprising, for a group of users, representations of cerebral blood metrics, wherein for each training sample, a portion of the obtained data spans a time period during which the user was holding their breath, and wherein each training sample includes a corresponding ground truth stroke risk for the user;
• providing the training data to a machine learning model, wherein the machine learning model takes, as input, the representations of the cerebral blood metrics and generates, as an output, a prediction of stroke risk;
• and
• updating the machine learning model based on differences between the ground truth stroke risk and the predicted stroke risk to generate a trained machine learning model configured to predict stroke risk.