Abstract: A method of detecting the likelihood of a seizure event in a patient includes at successive expirations of a first time interval, determining a first likelihood that the patient is experiencing a seizure based on electrodermal activity and a movement of a limb of the patient. The method also includes at successive expirations of a second time interval, determining whether the patient experienced a seizure in a second time period preceding the determining based on a heart rate of the patient. In response to determining that the third comparison result satisfies at least a third detection criterion, the method compares electrodermal activity and the movement of a limb of the patient to determine a second likelihood. In response to determining that the second likelihood satisfies a second detection criterion, the method triggers presentation of a second alert regarding a potential seizure.

Abstract: Systems and methods of the present disclosure enable improved seizure detection and/or prediction using a seizure monitoring system. The system receives a data stream including wearable sensor data associated with a user, where the data stream includes electrodermal activity data and where the electrodermal activity data includes circadian rhythm-dependent amplitudes. The system receives a time associated with a seizure of the user. The system trains seizure machine learning model to identify a pre-ictal period associated with a time segment based on the circadian rhythm dependent amplitudes and the time associated with the seizure. The system deploys the seizure machine learning model to ingest a new data stream. Based on the new data stream, the seizure machine learning model predicts a seizure likelihood in a prediction period.

Abstract: Systems and methods of the present disclosure determine whether the patient experiences a grand tonic clonic seizures using video recordings or sensor data or both. The systems and methods receive data from a device that continuously records video and/or sensor data, continuously analyzes the data with a processing unit utilizing machine learning to classify segments of the data as seizure or no seizure, and to classify seizure types. An alarm is produced using an output unit when an epileptic data segment is detected. The processing unit thus provides continuous and in real-time monitoring of an epilepsy patient in the home or hospital setting, e.g. while the patient is sleeping in bed. In case a seizure is detected, an alarm may inform caregivers or clinicians to help them intervene and limit the complications of this seizure for the patient.

Abstract: To enable real-time seizure warnings, systems and methods of the present disclosure include a wearable sensor in communication with processors that are configured to receive from the wearable sensor data streams associated with a user that include biomarker data parameters. The processors utilize a seizure forecasting machine learning model to predict a pre-ictal period probability associated with a forecasted time segment based on values of the data streams. The processors determine a segment value for an integration window of a history pre-ictal period probabilities for the forecasted time segment and previously forecasted time segments and determine a pre-ictal period based on the segment value exceeding a pre-ictal probability threshold. The processors determine a pre-ictal risk indication include a seizure treatment administration and cause a computing device to produce the pre-ictal risk indication to indicate a predicted risk of a seizure.

Abstract: An apparatus for generating a prediction that a patient will experience a seizure by monitoring a patient's body temperature over time is provided. The apparatus may include a sensor to sense temperature. The apparatus may monitor, using the sensor, the body temperature of the patient and compare the body temperature over a first period of time and a second period of time. The apparatus may generate a prediction of whether the patient will experience a seizure following the second period of time based at least in part on a result of the comparing.

Abstract: An apparatus for generating a prediction that a patient will experience a seizure based on a blood volume signal is provided. The apparatus may include a blood volume sensor to sense the blood volume in a location of a patient's body. The apparatus may extract one or more features from the blood volume signal and determine if the feature has changed over time. The apparatus may generate a prediction of whether the patient will experience a seizure based on the determination of whether the feature changed over time.

Abstract: A method of detecting the likelihood of a seizure event in a patient includes at successive expirations of a first time interval, determining a first likelihood that the patient is experiencing a seizure based on electrodermal activity and a movement of a limb of the patient. The method also includes at successive expirations of a second time interval, determining whether the patient experienced a seizure in a second time period preceding the determining based on a heart rate of the patient. In response to determining that the third comparison result satisfies at least a third detection criterion, the method compares electrodermal activity and the movement of a limb of the patient to determine a second likelihood. In response to determining that the second likelihood satisfies a second detection criterion, the method triggers presentation of a second alert regarding a potential seizure.

Abstract: Techniques for determining treatment of a subject with IS based on EEG-derived biomarkers are provided. According to some aspects, a method of adapting treatment of a subject having infantile spasms (IS) is provided, the method comprising obtaining electroencephalogram (EEG) data of the subject, determining a measure of delta power of the EEG data and/or a measure of spike frequency of the EEG data, and determining subsequent treatment of the infantile spasms of the subject based at least in part on the determined measure of delta power of the EEG data and/or measure of spike frequency of the EEG data.

Abstract: An apparatus for generating a prediction that a patient will experience a seizure based on a blood volume signal is provided. The apparatus may include a blood volume sensor to sense the blood volume in a location of a patient's body. The apparatus may extract one or more features from the blood volume signal and determine if the feature has changed over time. The apparatus may generate a prediction of whether the patient will experience a seizure based on the determination of whether the feature changed over time.

Abstract: Methods and apparatus for identifying and using at least one nonlinear feature determined from multiscale electroencephalography (EEG) data to evaluate an epileptogenicity level of a patient is described. A multiscale algorithm is applied to EEG data recorded from the patient to produce scaled EEG data. At least one nonlinear feature value for the received EEG data and/or the scaled EEG data is determined and the at least one nonlinear feature value is used, at least in part, to evaluate the epileptogenicity level of the patient.

Abstract: An apparatus for generating a prediction that a patient will experience a seizure by monitoring a patient's body temperature over time is provided. The apparatus may include a sensor to sense temperature. The apparatus may monitor, using the sensor, the body temperature of the patient and compare the body temperature over a first period of time and a second period of time. The apparatus may generate a prediction of whether the patient will experience a seizure following the second period of time based at least in part on a result of the comparing.

Abstract: Methods and apparatus for identifying and using at least one nonlinear feature determined from multiscale electroencephalography (EEG) data to evaluate an epileptogenicity level of a patient is described. A multiscale algorithm is applied to EEG data recorded from the patient to produce scaled EEG data. At least one nonlinear feature value for the received EEG data and/or the scaled EEG data is determined and the at least one nonlinear feature value is used, at least in part, to evaluate the epileptogenicity level of the patient.