Method for Detecting Seizures

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.

1 - 18 . (canceled) 
     
     
         19 . A system for detecting the onset of seizures, such as epileptic seizures with a monitoring or detection device, comprising:
 a monitoring or detection device;   a measuring unit in the monitoring or detection device having at least one sensor having a configuration for detecting an electromyography signal generated by a muscle on a body of a user;   a data processing unit connected to the measuring unit and configured to process and analyze the measured signal, wherein the data processing unit comprises:
 a pre-processing module configured to provide a filtered signal, 
 a feature extraction module connected to the pre-processing module and having a threshold detector, wherein the feature extraction module is configured to apply a first number of time windows to the filtered signal, and 
 a classification module connected to the feature extraction module and configured to generate an output signal, 
   wherein   the feature extraction module including a threshold detector with a hysteresis value, defining a positive and a negative threshold value, and is configured to use the threshold detector to count a number of zero-crossings of the filtered signal within each of the time windows, and   the classification module is configured to compare the count with a first threshold value, to compare the number of time windows having a count above the first threshold value with a second threshold value, and to generate the output signal if the number of time windows having a count above the first threshold value is above the second threshold value.   
     
     
         20 . The system of  claim 19 , wherein the data processing unit is connected wirelessly to the measuring unit. 
     
     
         21 . The system of  claim 19 , wherein the hysteresis value is between ±0 μV and ±500 μV. 
     
     
         22 . The system of  claim 19 , wherein said first threshold is between about 100 and 400 crossing counts. 
     
     
         23 . The system of  claim 19 , further comprising an adaptive update module, wherein the adaptive update module is configured to adjust said first threshold for the system as seizures are detected. 
     
     
         24 . The system of  claim 19 , further comprising an adaptive update module, wherein the adaptive update module is configured to adjust said first threshold, said second threshold, or both said first threshold and said second threshold for the system as seizures are detected. 
     
     
         25 . The system of  claim 19 , further comprising an adaptive update module, wherein the adaptive update module is configured to update one or more thresholds settings for the system as seizures are detected in order to reduce a latency of seizure detection for the system. 
     
     
         26 . The system of  claim 19 , wherein the system comprises at least a second measuring unit which is configured to detect the muscle activities of one or more muscles on the body of the user or another signal characteristic of a seizure, and at least a second data processing unit which is connected to the second measuring unit and configured to process and analyze the detected signal of the second measuring unit and further configured to generate an output signal indicating whether the detected signal is above a third threshold value or not. 
     
     
         27 . The system of  claim 26 , wherein the classification modules in the data processing unit is further connected to an evaluation module which is configured to generate the event signal if two or more of the output signals have a higher output value the hysteresis value or if a weighted sum of the output signals is above a fourth threshold value. 
     
     
         28 . The system of  claim 27 , wherein the system further comprises an alarm unit connected to the evaluation module. 
     
     
         29 . The system of  claim 19 , wherein the system further comprises an alarm unit connected to the data processing unit. 
     
     
         30 . A method for indicating the onset of seizures, such as epileptic seizures with a monitoring or detection device, comprising:
 providing a monitoring or detection device;   providing a measuring unit in the monitoring or detection device and a data processing unit connected to the measuring unit in the monitoring or detection device, the data processing unit including a feature extraction module and a classification module;   detecting one or more electromyographic signals generated by at least one muscle on the body of a user using the measuring unit and transmitting at least one detected signal to the data processing unit,   processing and analyzing the detected signal using the data processing unit in order to generate an event signal by:
 filtering the detected signal in order to provide a filtered signal and analyzing the filtered signal within a first number of time windows, 
 applying a hysteresis value, defining a positive and a negative threshold value, to count a number of crossing of the filtered signal within each of the first number of time windows, 
 determining a second number of time windows having a count above a threshold count value, 
 further comparing the second number of time windows to a threshold value of the classification module, and 
 generating the event signal if the second number of time windows is above the second threshold value.