Method of indicating the probability of psychogenic non-epileptic seizures

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.

1 - 12 . (canceled) 
     
     
         13 . A method of indicating the probability of non-epileptic seizures, wherein the method comprises the steps of:
 automatically recording patient data by means of a first device placed on a patient's body, wherein the first device comprises at least one sensor unit measuring at least one parameter on the patient's body, and wherein the data is recorded over a predetermined time period;   transmitting the recorded data from the first device to a computer unit for further analysis;   operating the first device in a non-alarm mode in which the at least one parameter is compared to at least one threshold value for determining whether a seizure is present or not;   manually logging data comprising at least a first time stamp of at least one seizure within the predetermined time period,   comparing the recorded data to the manually logged data in the computer unit, and   determining if the recorded data matches the manually logged data or not.   
     
     
         14 . A method according to  claim 13 , further comprising automatically detecting at least one seizure within the predetermined time period with the first device and recording at least a second time stamp of the at least one seizure detected by the first device, and comparing the second time stamp to the first time stamp to determine if the second time stamp matches the first time stamp or not. 
     
     
         15 . A method according to  claim 13 , wherein the at least one sensor unit measures one of an electromyographic signal and an acceleration signal. 
     
     
         16 . A method according to  claim 15 , wherein said one of an electromyographic signal and an acceleration signal is measured on at least one of a limb and skeletal muscle of the patient. 
     
     
         17 . A method according to  claim 15 , wherein the first device calculates a root-mean-square value of the at least one parameter within at least one time window and compares the root-mean-square-value to the at least one threshold value. 
     
     
         18 . A method according to  claim 15 , wherein the first device transforms the at least one parameter into both a frequency domain and a time domain, and compares at least one calculated value from each of the frequency and time domains to the at least one threshold value. 
     
     
         19 . A method according to  claim 15 , wherein the first device extracts at least one predetermined pattern from the at least one parameter, and compares the at least one pattern to the at least one threshold value. 
     
     
         20 . A method according to  claim 13 , wherein the signal from the at least sensor unit is transmitted directly to a third device and recorded in the third device. 
     
     
         21 . A method according to  claim 13 , wherein the patient manually logs the data or at least one subject monitoring the patient manually logs the data. 
     
     
         22 . A system for indicating the probability of non-epileptic seizures, comprising:
 a first device configured to be placed on a patient's body, wherein the first device comprises at least one sensor unit configured to measure at least one parameter on the patient's body, and is configured to automatically record data over a predetermined time period;   a computer unit configured to be coupled to the first device and comprising data processing means configured to analyze data recorded over said predetermined time period; and   means for manually logging data comprising at least a first time stamp of at least one seizure within the predetermined time period time period, and   wherein the first device is configured to operate in a non-alarm mode in which the at least one parameter is compared to at least one threshold value for determining whether a seizure is present or not; and   wherein the computer unit is configured to compare the recorded data with the manually logged data and determine if the recorded data matches the manually logged data or not.   
     
     
         23 . A system according to  claim 22 , wherein the first device is configured to automatically detect at least one seizure within the predetermined time period and to record at least a second time stamp of the at least one seizure, and wherein the computer unit is configured to compare the second time stamp to the first time stamp to determine if the second time stamp matches the first time stamp or not. 
     
     
         24 . A system according to  claim 20 , wherein the at least one sensor unit is one of an electromyographic sensor and an accelerometer, and wherein the first device is configured to detect said seizure based on the signal from the electromyographic sensor or accelerometer. 
     
     
         25 . A system according to  claim 22 , wherein the first device is configured to transmit a signal from the at least one sensor unit directly to a third device which is configured to record said signal. 
     
     
         26 . A system according to  claim 22 , wherein one of first device and the computer unit comprises user input means for manually logging the data.