Epilepsy detection / prediction

Epilepsy detection / prediction


Worldwide, more than 70 million people suffer from epilepsy, a neurological condition characterized by seizures that affect their quality of life. Despite the different treatments currently available, still 35% of the patients continue to experience seizures for the rest of their lives.

Different stakeholders can benefit from improved monitoring of epilepsy patients. The quality of life of epilepsy patients and their relatives can be improved substantially either by an increased feeling of security at home or a better treatment selection (decreased seizure frequency). Neurologists get access to more accurate and objective information from the ongoing treatments through the automated seizure diaries that allow better treatment selection. 

Such an automated monitoring system allows the pharmaceutical industry to select more efficiently the right type of patients for their clinical trials, which could lead to large cost savings. The developed methods can also be reused for other health applications by the medical device industry. Insurance companies can also see a decrease in expenses as a seizure warning system decreases the risk of injuries or post-ictal complications.

Automated seizure warning and detections systems can be used to improve the patients’ quality of life. Such systems can automatically detect ongoing seizures and alert the relatives. This way, both patients and relatives can feel reassured, knowing someone will help them when a seizure occurs in their everyday environment. Such applications require mobile measurements. Based on the type of the seizure that the patient exhibits, multimodal algorithms can be employed and data from different sources can be collected (e.g. Accelerometer, ECG, EMG). As an example, for the detection of seizures in a patient with absence seizures only the EEG signal is used, in patients with tonic clonic seizures a combination of EMG and EEG is optimal (possibly also ECG), whereas for frontal seizures ECG and EEG are employed. We offer different types of models and algorithms that can be used for accurate seizure detection and prediction.

Use Case

Automated seizure diary

Currently, treatment evaluation relies on manual seizure diaries maintained by the patient itself, which have shown to have an accuracy under 50%. An objective and automated offline seizure diary could lead to better patient treatment due to more reliable information for the clinician.

Automated seizure detection in hospital environment

In order to help neurologists decrease the time needed to review and annotate full-day recordings from a large number of patients. We offer ML algorithms than can pre-classify segments with high probability of being a seizure. The neurologist only needs to review and annotate only such segments instead of going through long recordings. Algorithms with high sensitivity are a perquisite for this application.

Technologies we use: