• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    A method and system for detecting seizures onset comprising a measuring unit (1) having one or more sensors for detecting the electromyographic signal of one ore more musc 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 musc activities. The pre-processing module is connected to a feature extraction module (4) comprising a threshold detector which counts the number of zero-crossings with a hysteresis within a predetermined time window. The feature extraction module is connected to a c 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.
    公开号:DK201100556A
    申请号:DKP201100556
    申请日:2011-07-19
    公开日:2013-01-20
    发明作者:Kim G Gommesen；Hoppe Karsten
    申请人:Ictalcare As；
    IPC主号:
    专利说明:

    METHOD FOR DETECTING SEIZURES
    Technical field
    The invention relates to a method for detecting the onset of seizures, such as epileptic seizures, comprising a measuring unit which detects one or more electromyographic signals generated by at least one muscle on a user's body, and a data processing unit which processes and analyzes the detected signal and generates an event signal if the analyzed signal is above at least one threshold value which then triggers an event.
    The invention also relates to a system for detecting onset of seizures, such as epileptic seizures, comprising a measuring unit having at least one sensor detecting an electromyographic signal generated by a muscle on a user's body, and a data processing unit processing and analyzing the detected signal and generating an event signal if the analyzed signal is above at least one threshold value.
    Prior art
    Today there is a need for a way to improve the detection of seizure onsets in order to improve treatment or to the alert caretakers or the patient of a seizure in order to prevent potential dangerous situations associated with a seizure. Seizures, such as epileptic seizures, can occur as partial or generalized seizures or a combination of both, where generalized seizures can occur as tonic, clonic, or tonic-clonic seizures. In a tonic-clonic seizure, the tonic phase starts before the clonic phase and typically followed by the clonic phase.
    Different methods or algorithms for detecting seizure onsets are described in the literature. An example of such is disclosed in WO 2008/131782 A1 which detects epileptic seizures by evaluating a sensed EMG signal over a number of time windows, if the sensed EMG signal is above a threshold value within the first time window, where the threshold value is determined according to the user's maximum voluntary contraction. The algorithm has the disadvantage that the parameters have to be adjusted to each individual person in order to reduce the number of false positives and accurately detect the seizure. US 2007/0142873 A1 describes an adaptive method and system for detecting seizures by sensing IEEG signals which is used in a feedback loop to administer a drop or simulate to counteract the seizure. The parameters used to detect the seizures are determined by using a time consuming and very complex data process, where the system performs several measurements with different parameter settings from which the best parameter settings are selected. This system requires the desired signals to be captured by multiple channels in order to accurately detect the signals and to reduce the number of false positives. Furthermore, this method uses an invasive procedure to place the IEEG sensors on the body. WO 2007/072425 A2 describes a method and a device for detecting epileptic seizures by measuring the heart beats combined with measuring the body movement and measuring EEG signals at different positions in order to reduce the number of false positives. This method also requires the desired signals to be captured by multiple channels in order to accurately detect the signals and to reduce the number of false positives.
    Object of the invention
    The invention solves these problems of the closest prior art by providing a method for detecting the onset of seizures characterized in that the data processing unit comprises a feature extraction module which counts the number of zero crossings between the amplitude of the detected signal and a hysteresis. and transmits the count to a classification module which generates the event signal if the count is above a first threshold value. According to claim 2, the feature extraction module applies a predetermined number of time windows, such as overlapping time windows, to the detected signal, and counts the number of crossings within each time window. The classification module compares the number of time windows, which has a count above the first threshold value, with a second threshold value, according to claim 3, and generates the event signal if the number of time windows is above the second threshold value. This method can be implemented as a generic algorithm in a seizure detection or monitoring device without having to calibrate the parameters for each individual user first. The seizure detection method has a low false detection rate and a short latency, so that seizures can be detected faster and more accurately. Furthermore, this enables seizures to be detected using only one measuring channel, thus reducing the complexity of the detection system and the number of components needed to detect seizures.
    According to claim 4, the data processing unit comprises a pre-processing module which filters out noise and / or motion artifacts which relate to normal muscle activities. The pre-processing module uses a high-pass filter with a predetermined cut-off frequency, according to claim 5. This enables the algorithm to filter out most of the noise and motion artifacts, which do not relate to the seizure, before analyzing the detected signal.
    The muscle activities of one or more muscles on the user's body are detected by a number of measuring units, according to claim 6, which are then processed and analyzed in one or more data processing units which generate an output signal for each of the detected signals if the detected signals are above at least a third threshold value. According to claim 7, the output signals are transmitted to an evaluation module which applies an optional weighing factor to each of the output signals and generates the event signal if two or more of the output signals are above the third threshold value. This improves the detection of seizures by comparing the detected signal to other signals, which are detected at different locations, thus reducing the number of false positives.
    The event signal is transmitted to an alarm unit, according to claim 8, which generates an alarm or an alarm message based on the event signal. This enables the algorithm to inform or alert the user or an external caretaker that a seizure is occurring so that the necessary actions can be taken.
    The invention also solves these problems by providing a system for detecting the onset of seizures characterized in that the data processing unit comprises a feature extraction module which comprises a threshold detector with a hysteresis counting the number of zero crossings with the hysteresis, and a classification module, which is connected to the feature extraction module and generates the event signal if the count is above a first threshold value. According to claim 10, the threshold detector counts the number of crossings with a hysteresis between 0-150pV, preferably between 20-1 OOpV, preferably 50pV. The feature extraction module applies a predetermined number of time windows to the detected signal and counts the number of crossings within each time window, according to claim 11. According to claim 12, the feature extraction module uses overlapping time windows with an overlap between 0- 95%, preferably between 50-75%, preferably 75%, and a length between 0.5-2 sec., Preferably 1 sec. The classification module compares the number of time windows, which has a count above the first threshold value, with a second threshold value, according to claim 13, and generates the event signal if the number of time windows is above the second threshold value. According to claim 13, the first threshold is between 180-340, preferably between 240-260, preferably 250, and the second threshold is between 1-40, preferably between 10-25, preferably 18. This enables the detection system to be implemented. as a generic detection system that does not require the parameters of the algorithm to be calibrated for each individual user. The system is able to detect seizures by using only one measuring channel, thus reducing the complexity of the detection system and the number of components needed to detect seizures. The seizure detection system has a low false detection rate and a short latency so that seizures can be detected faster and more accurately.
    The data processing unit comprises a pre-processing module having a high-pass filter for filtering out noise and / or motion artifacts related to normal muscle activities, according to claim 14. The high-pass filter has a predetermined cut-off frequency between 20-200Hz, preferably between 100-150Hz, preferably 150Hz, according to claim 15. This enables the system to filter out most of the noise and motion artifacts, which do not relate to the seizure, before analyzing the detected signal.
    According to claim 16, the system comprises a number of measuring units detecting the muscle activities of one or more muscles on the body of the user, which are connected to one or more data processing units which process and analyze the detected signals and generate an output signal for each of the detected signals if the detected signals are above at least a third threshold value. The data processing units are connected to an evaluation module, according to claim 17, which applies an optional weighing factor to each of the output signals and generates the event signal if two or more of the output signals are above the third threshold value. This improves the detection of seizures by comparing the detected signal to other signals, which are detected at different locations, thus reducing the number of false positives.
    According to claim 18, the event signal is transmitted to an alarm unit which generates an alarm or an alarm message based on the event signal. This enables the algorithm to inform or alert the user or an external caretaker that a seizure is occurring, so that the necessary actions can be taken.
    The invention also describes the use of the method or system to detect seizures having a tonic phase, such as tonic-clonic seizures.
    The drawings
    The embodiments of the invention will now be described with reference to the drawings, in which
    FIG. 1 shows a first embodiment of the seizure detection system according to the invention, and
    FIG. 2 shows a second embodiment of the seizure detection system according to the invention.
    Description of exemplary embodiment
    Figure 1 shows a first embodiment of the detection system comprising a measuring unit 1 having one or more sensors 1a for sensing the muscle activities generated by one or more muscles, i.e. the skeletal muscles, on the body of a user. The sensors 1a may be configured as electromyographic sensors. The sensors 1a may be integrated into the measuring unit 1, which may be attached or fixed to the body by using an adhesive agent or a fixing band or strap, or alternatively may be placed at different measuring positions and connected to the measuring unit 1 by a wired or wireless connection. The sensors 1a may be connected to a controller and a memory module, and data from the sensors may be stored in the memory module before being transmitted to a data processing unit 2 either periodically, continuously or upon request from the data processing unit 2. In a preferred embodiment of the measuring unit 1 uses a single measuring channel to detect the electromyographic signal. This enables the sensors to be easily placed in the measuring positions without using any invasive procedures.
    The detected data is then transmitted via a wired or wireless connection to the data processing unit 2 which processes and analyzes the data. The data processing unit 2 may be an external device or integrated into the measuring unit 1. The detected data is transmitted to a pre-processing module 3 comprising filter means for filtering out noise and motion artifacts related to normal muscle activities. The filter means may be configured as a high-pass filter with a predetermined cut-off frequency which filters out most of the noise and motion artifacts not related to the seizure. Studies have shown that all or most of the data related to the seizure is located in a frequency band above 100Hz, while all or most of the noise and motion artifacts are located in a frequency band of 0-20Hz. The cut-off frequency may be selected as any frequency between the upper frequency of the lower frequency band and the lower frequency of the upper frequency band, i.e. between 20-200Hz, preferably between 100-150Hz. The pre-processing module 3 may comprise a biasing circuitry which removes any bias so that the filtered signal is symmetric around zero. In a preferred embodiment the cut-off frequency for the filter means is selected to be 150Hz.
    The filtered data is then transmitted to a feature extraction module 4 which extracts one or more predetermined features. The feature extraction module 4 applies a number of predetermined time windows to the filtered data. The time windows may be configured as overlapping time windows with a predetermined overlap and length. The time windows may be shaped like a rectangular, a triangular, a cosine, a sine or another type of window where the overlaps have a rectangular, a triangular or another shape. In order to improve the data analysis, the time windows may have an overlap between 0-95%, preferably between 50-75%. The length of time windows is selected so that the detection system has a sufficiently short latency, i.e. the length may be between 0.5-2 sec. In a preferred embodiment the time window is selected to have an overlap of 75% and a length of 1 sec.
    The feature extraction module 4 uses a threshold detector to count the number of crossings, i.e. the zero crossings, between the amplitude of the filtered signal and at least one predetermined threshold value within each time window. The threshold detector may be configured as a threshold detector with a predetermined hysteresis value, i.e. a positive and a negative threshold value. The hysteresis value may be selected so that low-level noise and motion artifacts, which are not removed by the pre-processing module 3, have no or minimal effect on the counts. The hysteresis value may be selected within a range of 0-150pV, preferably within 20-1 OOpV. In a preferred embodiment the threshold detector has a hysteresis value of 50μν. If the hysteresis has a low value, the threshold detector mainly detects the frequency of the filtered signal, while the threshold detector detects both the frequency and the amplitude of the filtered signal if the hysteresis has a high value.
    The feature extraction module 4 transmits the count for each time window to a classification module 5 which evaluates the extracted features and generates an event signal if at least one of the extracted features is above a threshold value. The classification module 5 compares the count for each time window to a first threshold value and generates an event signal if the count is above the threshold value. Alternatively, the classification module 5 may compare the number of time windows, which has a count above the first threshold value, to a second threshold value and generate an event signal if the number of time windows is above the second threshold value. The number of time windows with a count above the first threshold value may be determined by counting the number of consecutive and / or non-consecutive time windows within a second predetermined time window.
    The first and second thresholds may be adjusted in order to more accurately detect the seizures. The thresholds may be determined according to the seizure characteristics detected at that measuring position.
    Studies have shown that a tonic-clonic seizure generates a high number of crossings (counts) in the tonic phase, which then drops to a lower number at the beginning of the clonic phase. The first threshold value for the number of crossings may be between 180-340, preferably between 240-260. The second threshold value for the number of time windows may be between 1-40, preferably between 10-25. The first and second threshold values may be selected so that all seizures are detected while reducing the number of false positives to a minimum and ensuring a short latency for the detection system. In a preferred embodiment, the first threshold value is selected to be 250 and the second threshold value is selected to be 18.
    The event signal triggers an event which informs or alerts the user or an external caretaker that a seizure is occurring. The event signal may be transmitted to an alarm unit 6 by a wired or wireless connection. The alarm unit 6 may be an external device or integrated into the measuring unit 1 or the data processing unit 2. The alarm unit 6 may generate one or more types of alarms or messages, i.e. an audio, a visual, a vibrating alarm, an alarm message, or any combination thereof. The measuring units 1, the data processing units 2, and optionally the alarm unit 6 may be integrated into a single unit which can be easily attached to or placed on the body.
    Figure 2 shows a second embodiment of the detection system, where the reference numbers are the same as in Figure 1. The detection system comprises a number of measuring units 1, 7, 8, which are placed at different measuring positions on the body. The measuring units 7, 8 may have the same configuration as the measuring unit 1 or different configurations. The measuring unit 1.7, 8 comprises one or more sensors 1a, 7a, 8a for measuring the muscle activities generated by one or more muscles, i.e. skeletal muscles. Alternatively, the measuring units 1, 7, 8 may comprise different types of sensors, i.e. EMG, MMG or PMG, for measuring muscle activity or may comprise at least one other sensor for measuring another type of signal related to a seizure, i.e. respiration, heart rate or galvanic skin response. The sensors may be easily placed at the measuring positions without using any invasive procedures by using an adhesive agent or a fixing band or strap. This enables the system to improve the detection of seizures and reduce the number of false positives by comparing the detected signal with other signals detected at different locations.
    The detection system may comprise a number of data processing units 2, 9, 10, which are connected to each of the measuring units 1, 7, 8. The data processing unit 9, 10 may have the same configuration as the data processing unit 2 or a different configuration. The data processing unit 2, 9, 10 may comprise processing means 3, 4, 11, 12 which analyzes and extracts one or more features or patterns from the measured signals, and a classification module 5, 13, 14, which compares the extracted features or patterns to one or more threshold values and generates an output signal, ie a high or a low output signal, indicating whether a seizure is present or not. The outputs of each classification modules 5, 13, 14 in the data processing units 2, 9, 10 are transmitted by a wired or wireless connection to an evaluation module 15 which evaluates the output signals and generates an event signal if a seizure is detected. The evaluation module 15 may generate an event signal if two or more of the output signals are high. Alternatively, evaluation module 15 may apply a predetermined weighing factor to each of the output signals and generate an event signal if the sum of these weighted outputs is above a third threshold value. The event signal is then transmitted by a wired or wireless connection to the alarm unit 6 which informs or alerts the user or an external caretaker that a seizure is occurring.
    The data processing units 2, 9, 10 may be arranged as parallel pipelines in a common data processing unit or as separate data processing units connected to the evaluation module. The evaluation module 15 may be integrated into the common data processing unit or into one of the separate data processing units.
    The described method in figures 1 and 2 for detecting the onset of seizures can be implemented as a generic algorithm in a detection or monitoring system, which only uses a single EMG measuring channel to detect seizures. This enables the generic algorithm to be implemented in a small detection or monitoring device without having to calibrate the parameters, i.e. the threshold values, for each individual user first. This reduces the complexity of the detection system and reduces the number of components needed to detect seizures.
    The described detection system may comprise an adaptive update module (not shown) which updates the threshold values each time the system has detected a seizure. This enables the first and second threshold values for the number of counts and time windows to be adjusted so that the system is able to detect the seizures more accurately. This in turn will reduce the number of false positives and also reduce the latency for the system.
    Example
    Electromyographic measurements were conducted on six different persons of different ages and genders as shown in Table 1. The persons all had at least one tonic-clonic seizure which was verified by other measurements, including video and ECG measurements.
    The EMG sensors were placed on the left deltoid muscle with the active electrode on the center of the muscle and the reference electrode placed on the acromioclavicular joint. The EMG signal was sampled at a sampling rate of 1024Hz. The measured EMG signals were pre-processed with a high-pass Butterworth filter with an order of 20 and a cut-off frequency of 150Hz and analyzed with time windows with a length of 1 sec. and an overlap of 75% and a hysteresis value of 50pV. The first and second thresholds chosen for each of the test subjects can be seen in Table 2.
    As shown in table 2, the described detection system has a 100% sensitivity, meaning that it has already been detected! the seizures. The detect system has a false detection rate (FDR) between 0-0.1885 per hour and a latency between 7-10.5 sec.
    权利要求:
    Claims (20)
    [1]
    A method for detecting the onset of seizures, such as epileptic seizures, comprising a measuring unit (1), which detects one or more electromyographic signals generated by at least one muscle on a user's body, and a data processing unit ( 2), which processes and analyzes the detected signal and generates an event signal if the analyzed signal is above at least one threshold value, which then triggers an event characterized in that the data processing unit (2) comprises a feature extraction module (4) , which counts the number of zero crossings between the amplitude of the detected signal and a hysteresis, and transmits the count to a classification module (5) which generates the event signal if the count is above a first threshold value.
    [2]
    A method according to claim 1 characterized in that the feature extraction module (4) applies a predetermined number of time windows, such as overlapping time windows, to the detected signal, and counts the number of crossings within each time window.
    [3]
    Method according to claim 2 characterized in that the classification module (5) compares the number of time windows, which has a count above the first threshold value, with a second threshold value, and generates the event signal if the number of time windows is above the second threshold value.
    [4]
    Method according to any one of claims 1-3 characterized in that the data processing unit (2) comprises a pre-processing module (3), which filters out noise and / or motion artifacts, which relate to normal muscle activities.
    [5]
    Method according to claim 4 characterized in that the pre-processing module (3) uses a high-pass filter with a predetermined cut-off frequency.
    [6]
    Method according to any one of claims 1-5 characterized in that the muscle activities of one or more muscles of the user's body are detected by a number of measuring units (1,7,8) which are then processed and analyzed in one or more data processing units (2, 9, 10), which generate an output signal for each of the detected signals if the detected signals are above at least a third threshold value.
    [7]
    Method according to claim 6 characterized in that the output signals are transmitted to an evaluation module (15) which applies an optional weighing factor to each of the output signals and generates the event signal if two or more of the output signals are above the third threshold value.
    [8]
    Method according to any one of claims 1-7 characterized in that the event signal is transmitted to an alarm unit (6) which generates an alarm or an alarm message based on the event signal.
    [9]
    A system for detecting the onset of seizures, such as epileptic seizures, comprising a measuring unit (1) having at least one sensor detecting an electromyographic signal generated by a muscle on a user's body, and a data processing unit (2) processing and analyzing the detected signal, and generating an event signal, if the analyzed signal is above at least one threshold value characterized in that the data processing unit (2) comprises a feature extraction module (4) comprising a threshold detector with a hysteresis counting the number of zero crossings with the hysteresis, and a classification module (5), which is connected to the feature extraction module and generates the event signal if the count is above a first threshold value.
    [10]
    System according to claim 11 characterized in that the threshold detector counts the number of crossings with a hysteresis between 0-150pV, preferably between 20-1 OOpV, preferably 50pV.
    [11]
    System according to any one of claims 9-10 characterized in that the feature extraction module (4) applies a predetermined number of time windows to the detected signal and counts the number of crossings within each time window.
    [12]
    12. System according to claim 11 characterized in that the feature extraction module (4) uses overlapping time windows with an overlap between 0-95%, preferably between 50-75%, preferably 75%, and a length between 0.5-2. sec., preferably 1 sec.
    [13]
    System according to claim 11 or 12, characterized in that the classification module (5) compares the number of time windows, which has a count above the first threshold value, with a second threshold value, and generates the event signal if the number of time windows is above the second threshold value.
    [14]
    A system according to claim 13 characterized in that the first threshold is between 180-340, preferably between 240-260, preferably 250, and the second threshold is between 1-40, preferably between 10-25, preferably 18.
    [15]
    System according to any one of claims 9-14, characterized in that the data processing unit (2) comprises a pre-processing module (3) having a high-pass filter for filtering out noise and / or motion artifacts which relate to normal muscle activities.
    [16]
    System according to claim 15 characterized in that the high-pass filter has a predetermined cut-off frequency between 20-200Hz, preferably 100-150Hz, preferably 150Hz.
    [17]
    System according to any one of claims 9-16, characterized in that the system comprises a number of measuring units (1, 7, 8) detecting the muscle activities of one or more muscles on the body of the user, which are connected to one or more data processing units (2, 9, 10), which process and analyze the detected signals and generate an output signal for each of the detected signals if the detected signals are above at least a third threshold value.
    [18]
    System according to claim 17, characterized in that the data processing units (2, 9, 10) are connected to an evaluation module (15), which applies an optional weighing factor to each of the output signals and generates the event signal if two or more of the output signals are above the third threshold value.
    [19]
    A system according to any one of claims 9-18 characterized in that the event signal is transmitted to an alarm unit (6) which generates an alarm or an alarm message based on the event signal.
    [20]
    20. Use of the method as defined in any one of claims 1-8 or the system defined in any one of claims 9-19 to detect seizures having a tonic phase, such as tonic-clonic seizures.
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    法律状态:
    2021-02-26| PBP| Patent lapsed|Effective date: 20200719 |
    优先权:
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    DK201100556|2011-07-19|
    DKPA201100556A|DK177536B1|2011-07-19|2011-07-19|Method for detecting seizures|DKPA201100556A| DK177536B1|2011-07-19|2011-07-19|Method for detecting seizures|
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