Brain Sentinel, Inc.

Ictalcare A/s

Isa Conradsen

Kim Gomme Gommesen

Karsten Hoppe

A system and method for detecting and analyzing the characteristics of seizures, such as tonic-clonic seizures, comprising at least one processor unit capable of recording a signal from a sensor unit, one or more analysis modules configured to analyze the recorded signal, one or more evaluation modules configured to evaluate the output signals from the analysis modules. A first analysis module is configured to analyze the recorded signal within a first frequency band, and a second analysis module is configured to analyze the recorded signal within a second frequency band, and the evaluation module is configured to calculate the ratio between the two output signals from the analysis modules. The system includes a seizure detection module configured to detect a seizure and a time-point extraction module configured to determine at least one starting point and an end point of the seizure.

Method and system of detecting seizures

A method and system for detecting a probability of psychogenic non-epileptic seizures using a portable battery powered device placed on the body of a patient and a device for manually logging detected seizures within a time period. The device may, advantageously, have a seizure detection algorithm which automatically records seizures detected within that time period. The two sets of data may then be transferred to a another device where the logged time stamps are matched to the recorded time stamps for determining if the detected seizure is a generalized tonic-clonic seizure (GTCS) or might be a psychogenic nonepileptic seizure (PNES). This provides a cheap and simple method for registering a probability of PNES by using a seizure detection device having an EMG-sensor or an accelerometer.

Method of indicating the probability of psychogenic non-epileptic seizures

A method and system for detecting the onset of seizures comprising a measuring unit having one or more sensors for detecting the electromyographic signal of one or more muscles of the user. The sensors are connected to a pre-processing module comprising a high-pass filter which filters out noise and motion artefacts related to normal muscle activities. The pre-processing module is connected to a feature extraction module comprising a threshold detector which counts the number of crossings with a hysteresis within a predetermined time window. The feature extraction module is connected to a classification module which compares the extracted features to a first and second threshold and generates an event signal if the extracted features are above the first and second threshold.

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Isa Conradsen