Abstract: Methods and apparatuses for detecting and characterizing seizures are described. In some embodiments, the methods and apparatuses include collecting an electrical signal from one or more electrodes disposed on the head or scalp of a patient and processing the electrical signal to determine the dynamics of a seizure event.

Abstract: 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.

Abstract: A method and system for detecting a probability of psychogenic non-epileptic seizures comprising a portable battery powered device placed on the body of a patient and means for manually logging detected seizures within a time period. The device may advantagely comprise a seizure detection algorithm which automatically records seizures detected within that time period. The two sets of data may then be transferred to a second device where the logged time stamps are matched to the recorded time stamps for determining if the detected seizure is a GTCS or might be a PNES. This provides a cheap and simple method for registering a probability of PNES by using a seizure detection device comprising an EMG-sensor or an accelerometer.

Abstract: 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.

Abstract: A method and system for detecting the onset of seizures comprising a measuring unit (1) 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 (3) comprising a high-pass filter which filters out noise and motion artefacts related to normal muscle activities. The preprocessing module is connected to a feature extraction module (4) 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 (5) 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 preprocessing 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.