International Business Machines Corporation

Filiz Isabell Kiral-Kornek

Stefan Harrer

Benjamin Scott Mashford

Subhrajit Roy

Susmita Saha

Mahtab Mirmomeni

Paul R Bastide

Jianbin Tang

Stefan Von Cavallar

Arun Vishwanath

Dwarikanath Mahapatra

Adam Joseph Makarucha

Robert R Kerr

One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction. The streams may be from multiple people and applied to an individual person.

Epilepsy seizure detection and prediction using techniques such as deep learning methods

A recommendation method, system, and computer program product include monitoring a patient using a plurality of sensors, receiving patient information including a comfort level corresponding to a sensor of the plurality of sensors, determining a relevance of each sensor of the plurality of sensors to at least one health conditions of the patient, and determining at least one sensor of the plurality of sensors to disconnect based on the comfort level and the relevance of each sensor.

Dynamic recommendations of sensors for patient monitoring

Embodiments are directed to a computer implemented method of analyzing media files to improve the presentation of media files to users. The method includes using a processor to analyze a set of media files. The media files are represented by a set of vectors according to characteristics of each media file. A set of preferences is gathered for a user. A configuration from the user is then obtained. The media file vectors are adjusted based on the preferences and configuration. The media files are selected for presentation to the user based on the user's preferences and configuration.

Discovering media content using natural language processing and machine learning

A health-monitoring method, system, and computer program product include operating at least one sensor of a health-monitoring device having a plurality of sensors, detecting a health condition event that requires operation of an additional sensor of the plurality of sensors to monitor the health condition event, activating the additional sensor of the health-monitoring device, and deactivating the additional sensor when the health condition event that requires the second sensor is no longer detected by the detecting.

Energy-efficient multisensory system for mobile health-monitoring

Providing an activity for a participant may include receiving at least location data specifying a location of the participant. An engagement level of the participant may be predicted based on the location data. Sensor data associated with the participant may be received, the sensor data comprising at least current physiological data associated with the participant. Based at least on the predicted engagement level and the sensor data, an exercise for the participant to perform may be determined. A notification signal may be transmitted to the participant to perform the exercise.

Machine learned optimizing of health activity for participants during meeting times

A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes, responsive to the first machine learning model predicting that the patient will have a seizure, using a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure. The method further includes using a neurostimulator to apply the determined neuromodulation signal pattern to the patient. The method also includes, after applying the determined neuromodulation signal pattern to the patient, detecting whether the patient had the predicted seizure based on the monitored brain activity of the patient. The method further includes adjusting at least the second machine learning model based on whether the patient had the predicted seizure.

Seizure detection, prediction and prevention using neurostimulation technology and deep neural network

In an approach, a processor receives classified data, wherein the classified data has been output by a second processor. A processor adjusts a count based on the classified data. A processor determines whether the count is greater than a pre-set threshold, wherein the pre-set threshold is set by a switching module of the processor. Responsive to determining that the count is greater than the pre-set threshold, the processor triggers an alarm of a pre-set alarm length, wherein the pre-set alarm length is set by the switching module of the processor.

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Collingwood, Australia

Filiz Isabell Kiral-Kornek