JP2018500111A - Method and apparatus for monitoring a patient for motor signs associated with seizure activity - Google Patents

Abstract

A method and apparatus for qualifying peaks contained in collected EMG signals is disclosed, including a method in which various test groupings of peaks are constructed from a group of peaks. The methods and apparatus described herein identify abnormal motor symptom patterns, initiate alarms when these patterns are identified, and organize data for inclusion in a searchable medical database; To make it easier.

Description

  The present invention relates to a method and apparatus for monitoring a patient for movement symptoms associated with seizure activity.

  Seizures can be characterized as abnormal or excessive entrained activity of the brain. Early in the stroke, neurons in the brain may begin to excite at specific locations. As the seizure progresses, this neuronal excitement spreads throughout the brain, and in some cases, many areas of the brain can become involved in this activity. With seizure activity in the brain, the brain sends electrical signals through the peripheral nervous system, activating different muscles in the body.

  Techniques designed to investigate and monitor seizures are usually electroencephalography (EEG) characterized by electrical signals using electrodes attached to the scalp or head of individuals or seizure patients who are prone to seizures. : Electroencephalography). In EEG, the electrodes can be arranged to measure the above activity, i.e. the electrical activity originating from neural tissue. Alternatively, electromyography (EMG) can be used for seizure detection. In EMG, electrodes are placed on the skin, in the vicinity thereof, or on muscles to detect electrical activity derived from muscle fiber activity.

  Detection of epileptic seizures using electroencephalography (EEG) typically requires the use of an amplifier with a number of electrodes and associated wires attached to the head and monitoring brain wave activity. Multiple EEG electrodes are very cumbersome and usually require some technical expertise for application and monitoring. Furthermore, in order to confirm the seizure, observation in an environment equipped with a video camera and a video recording device is necessary. Unless staffed and used in a clinical environment, such devices are often not just intended to determine if an attack is ongoing, but only provide a post-attack history. . These devices are usually intended for hospital-like environments where seizures can be confirmed by video camera recording or caregiver observation, and are usually used as part of an intensive care regimen for patients who have had multiple seizures. The At discharge, patients are often returned home without further monitoring.

  Portable devices for diagnosing seizures are generally EEG-based, but due to the above-mentioned drawbacks, are these devices not designed for long-term home use or everyday wear? Or not suitable for it. Other seizure warning systems can operate by detecting human body movements, usually limb movements. Typically, such a system can operate on the assumption that a person moves violently during a seizure. For example, an accelerometer can be used to detect intense limb movements. However, depending on the type of seizure, this assumption may or may not apply. Electrical signals sent from the brain during a seizure are often transmitted to many muscles at the same time, and the muscles can compete with each other to effectively cancel out intense exercise. In other words, the muscles may function to stiffen the person rather than cause actual intense exercise. As such, some seizures may not be detected consistently with accelerometer-based detectors.

  Portable devices for diagnosing seizures are generally not suitable for grading seizures based on the type of event and not suitable for grading seizure-related signals. Rather, different types of seizures and related events can often be grouped together. Accordingly, portable devices for detecting seizures may not be suitable for customizing the response to different types of detected seizure events. In addition, other portable devices may not be ideal for cost-effective monitoring of some patients. For example, using current portable devices, caregivers may misdiagnose several conditions, including those that may benefit from condition-specific therapy. For example, some events, such as psychogenic seizure events or non-epileptic seizure events, can be classified along with generalized tonic clonic seizure events. Statistical analysis of event signals and pattern recognition methods may encourage effective diagnosis of some commonly misdiagnosed conditions. However, other portable detection systems are generally not configured to provide organized statistical information to caregivers or are used to medically or surgically manage patient care It is not configured to process data for identification of specific activity patterns.

  Therefore, there is a need for a method and apparatus for detecting epileptic seizures that can be used in hospitalized or non-hospitalized environments without the use of numerous electrodes on the complicated head and limbs. In addition, grading seizures by type and / or intensity, identifying specific patterns of seizures or seizure-related activities, and customizing alarms to provide stable and cost-effective patient care, There is a need for a suitable detection method. There is also a need to monitor a system that organizes medical data in a database to medically and surgically manage patient care.

  In some embodiments, a method or apparatus for monitoring a patient for seizure activity includes identifying a peak contained in the collected EMG signal and one or more of the identified peak. Calculating a peak characteristic value. The characteristic value can be compared to a qualification threshold that includes at least one qualification threshold associated with each peak and at least one aggregate qualification associated with a peak group. And an aggregate qualification threshold. Peaks that meet the qualification threshold can be considered qualified. Then, once the peak is qualified, the level of activity of the qualified peak can be calculated and used to initiate a system response.

  In some embodiments, a method or apparatus for monitoring a patient for seizure activity can include collecting EMG signals and identifying peaks in elevated EMG signals. Peak data indicates seizure activity by building various test groupings of peaks, calculating one or more characteristic values for each test grouping, and comparing the characteristic values to one or more threshold characteristic values One or more groups of peaks can be searched. The peak is qualified if the peak is part of at least one test grouping that meets at least one of the threshold characteristic values, and from the qualified peak or burst, the activity level is determined and the system response is To get started, it is compared to the threshold level of activity.

Fig. 4 illustrates an embodiment of a method for qualifying peak data. FIG. 5 shows EMG model data including a set of peaks and a plurality of groups that can be constructed from a set of peaks. FIG. The model data of the further EMG is shown. The model data of the further EMG is shown. A bar graph related to model data of EMG is shown. 2 illustrates an embodiment of a method for combining peaks. Two peak groups of the EMG model data set are shown. 3 illustrates an embodiment of a method for detecting or retrieving seizure patterns in EMG data. 1 illustrates an embodiment of a seizure detection system. 2 shows an embodiment of a detection unit. 1 illustrates an embodiment of a base station.

  The devices and methods described herein can be used, for example, to detect abnormal motor muscle activity, including seizure activity, and alert the caregiver in a timely manner when activity is identified. These devices can include sensors placed on, near, or under the patient's skin, or attached to the patient's clothing, and configured to measure muscle electrical activity using EMG. can do. In some embodiments, an apparatus described herein can include one or more processors suitable for receiving an EMG signal and processing the signal to detect seizure activity. . Detection of seizures using EMG electrodes is described, for example, in Applicant's US Pat. And 14 / 816,924, Applicants 'international applications PCT / US14 / 61783 and PCT / US14 / 68246, and Applicants' US provisional patent applications 61 / 875,429, 61 / 894,793. 61 / 910,827, 61 / 969,660, 61 / 979,225, 62 / 001,302, 62 / 032,147, 62 / 050,504, No. 62 / 096,331 and 62 / 149,434, the disclosures of each of which are fully incorporated herein by reference. Some of the methods disclosed in the aforementioned references describe detection of elevated EMG signal amplitude peaks and qualification of peaks most likely to be associated with seizure activity. Peak detection methods include those that can be used in some of the embodiments described herein and are described in detail, for example, in Applicant's US Patent Application No. 14 / 920,665.

  The present disclosure also describes a method for detecting and qualifying peaks in collected EMG signals. Furthermore, embodiments are described in which a plurality of peak groups can be constructed from the peaks of the initial group. Multiple test peak groups may be compared to various qualification criteria or thresholds to facilitate qualification, for example, in aggregates or combinations of peaks that may be associated with seizure activity. Good. Some of those embodiments are, for example, patterns associated with seizure activity, which may otherwise be noise and cannot be identified in the data, or the quality of the data It can be particularly useful in detecting or identifying peak combinations associated with a particular pattern of motor signs, including patterns that can be intermittent or sporadic. In addition to embodiments for real-time patient monitoring, the embodiments described herein may be used to organize collected EMG data for use in a database of stored medical data. Good.

  In some embodiments, the method of monitoring a patient includes testing various groupings of peaks and searching for one or more groups that meet one or more eligibility criteria. Can do. Qualification criteria may be associated and adjusted to identify specific patterns of seizure activity. In some embodiments where multiple peak groups can be constructed, groups can be combined in various ways to determine the appropriate response. For example, a plurality of groupings of peaks can be constructed, each of which may independently meet qualification criteria in some cases. The procedures further described herein can then be used to determine the overall level of qualifying peak activity. For example, in some embodiments, all peaks that are members of at least one group of qualifying peaks can be included in determining the overall level of qualifying peak activity. The overall level of qualified peak activity can then be compared to one or more thresholds. Based on the comparison, an appropriate response can be initiated.

  In some embodiments, a method for monitoring a patient can include testing a plurality of peak groups and searching for one or more groups that best fit one or more eligibility criteria. . For example, one or more groups can be selected that are characterized as having qualifications with maximum confidence and having characteristic values that are most likely to correlate with seizure related activity. In some embodiments, peak data from a noise source, which may be most relevant to selecting or finding a peak grouping that can be associated with seizure activity with high confidence or to the patient's true physiological activity. One or more characteristic values for a group of peaks can be minimized or maximized in order to find peak groupings that are not deflected by inadvertent inclusion of.

  In some embodiments, peak data qualification may be performed in steps or stages. For example, a peak may be initially qualified, a peak that did not pass initial qualification may be removed from further qualification, and the remaining peaks may be organized into one or more groups. Good. The group can then be qualified based on a comparison of the group's characteristic values to become an aggregate characteristic threshold.

  In some embodiments, for identifying a particular pattern of seizure activity, including, for example, clonic-phase seizure activity, psychogenic non-epileptic seizure activity, other activity patterns, and combinations thereof In order to provide or enhance selectivity, characteristic values and / or qualification thresholds may be selected for use in routines. When a routine is referenced that may be selective for the identification of a particular activity, the routine can provide a positive response in a patient experiencing that activity, but the routine When there is no activity, it may provide a negative response even if other types of seizure activity exist. Thus, execution of one or more selective routines may prompt identification of a particular type of seizure activity, eg, identification of one or more particular sections or portions of seizures. The routines described herein, which can be configured for detection of clonic activity, distinguish detected clonic activity from other phases of seizure activity, including, for example, the tonic phase portion of seizure activity. May be used to Some of these routines may also further detect detected clonic activity with other activities commonly confused with clonic phases of generalized tonic clonic seizures (eg, non-epileptic psychogenic (Including activity resulting from seizure events).

  At a high level, the procedure for peak qualification may include a comparison of various peak characteristics with one or more qualification thresholds. For example, for a peak, if one or more peak characteristic values meet one or more qualification thresholds, the qualification criteria are considered to be met and the peak is qualified peak. Can be called. If a peak is qualified as meeting the selection criteria for clonic seizure activity, the peak can also be referred to as a qualified clonic burst.

  Several qualification procedures can be run for individual peaks. For example, certain characteristics of the peak, such as peak height, area, or duration width, can be defined without including data from other peaks. Thus, individual values for the characteristics may be calculated for each peak in the group and compared to the appropriate qualification threshold. Individual peak characteristics are, for example, peak height, peak area, signal-to-noise ratio (SNR) (for example, an estimate of uncertainty in peak amplitude as measured or estimated from the background region of the signal). Peak amplitude ratio), time width, duration of intervening periods where the signal on either the trailing or leading edge of the peak is small, other characteristics of individual peaks, and combinations thereof.

  In some embodiments, the peak is a minimum duration, maximum duration, minimum signal-to-noise ratio (SNR), a minimum duration of one or more intervening periods of the smaller signal on either side of the peak, The maximum duration of one or more intervening periods on either side of the peak and / or may be compared against a qualification threshold selected from a group of qualification thresholds including combinations thereof. The intervening period may be defined by the length of the duration of the region where the signal is more stable or lower in amplitude than the rising portion of the peak (eg, signal variability, RMS noise or signal strength is small). This can be indicated, for example, by the distance between the peak edge and an area where nearby signal strength increases or where signal stability decreases. In some embodiments, the peak signal-to-noise ratio can be calculated using peak amplitude data and signal noise estimation or calculation. Noise can be, for example, fluctuations or uncertainties in the baseline signal (eg, uncertainties in signal amplitude, height or area measurements that can result from fluctuations in EMG data for areas not included in the peak). May be determined by calculating or estimating the level of the EMG signal, eg, from data collected on either side of the peak or as part of the data collected when the patient is resting From the separately measured parts of For example, to calculate noise, signals may be collected and signal variability may be measured directly. Alternatively, the noise is estimated from signal magnitudes and fluctuation estimates that are predicted from fluctuations typical of signals of that magnitude predicted by one or more model functions including, for example, a normally distributed model function. May be. In some embodiments, baseline signal or noise fluctuations or uncertainty estimates may be selected or calculated during one or more system calibration routines.

  In some embodiments, the peak meets a threshold SNR, meets a minimum threshold of a peak time width of about 25 to about 75 milliseconds, and a peak time width of activity of about 250 milliseconds to about 500 milliseconds. May be qualified by meeting a maximum threshold of. In some embodiments, the peak may be qualified based on the presence of an intervening sequence of smaller signals on either side of the peak from about 50 milliseconds to about 300 milliseconds. Other embodiments of peak qualification, including those operating on individual peaks, are further described in various others of Applicant's co-pending application, incorporated herein by reference.

  Some characteristics of the peak data can be calculated for more than one peak. Also, in some embodiments described herein, the procedure for peak qualification may include comparing a plurality of peaks to one or more qualification thresholds. Good. That is, selecting a plurality of peaks as a group, determining an aggregate characteristic value for the group of peaks, and comparing the aggregate characteristic value with one or more associated thresholds Also good. When accurately characterized, the aggregate characteristics of multiple peaks can be highly selective for seizure activity. However, many aggregate characteristics may be biased by inadvertent inclusion of data from noise sources and / or biased from inadvertent exclusion of desired data associated with associated physiological events. . Many of the methods described herein address this concern. For example, some embodiments described herein may be used to find specific combinations of peaks that indicate seizure activity from noisy data.

  The qualification threshold for the characteristics of one peak group can be referred to as the aggregate qualification threshold. For example, aggregate qualification thresholds that can be used to qualify multiple peaks include peak minimum and / or maximum repetition rates, thresholds for variations in duration between peaks, etc. There are a number of aggregate characteristic thresholds, and combinations thereof. Aggregate qualification of multiple peak groups is further described in Applicant's co-pending US patent application Ser. No. 14 / 920,665, incorporated herein by reference.

  In some embodiments, one group of peaks for about 1 peak per second that is the threshold for the minimum peak repetition rate and about 7 peaks per second that is the threshold for the maximum peak repetition rate. Can be qualified. In some embodiments, for example, a greater or lesser number of peaks than the boundary defined by the above threshold may be used to measure multiple intervals as a single peak rate (e.g., multiple peaks). If present over appropriate intervals, it can be assumed that those peaks may not meet qualification.

  Among the various metrics for characterizing the variation in duration between peaks is the average deviation percentage. However, other metrics for characterizing peak timing variability, such as standard deviation values, average deviation values, or percent deviation values, can also be used. Any of the foregoing metrics can be calculated and used as an aggregate characteristic value that can be compared to an aggregate characteristic threshold as described herein. In some embodiments, a peak group may be qualified if the mean deviation rate value for time between peaks is greater than about 1% or about 5%. That is, in some embodiments, the aggregate characteristic threshold for the minimum average deviation rate may be between about 1% and about 5%. In some embodiments, a peak group may be qualified if the mean deviation rate value for the time between peaks is less than about 40% or about 50%. That is, in some embodiments, the aggregate characteristic threshold for the maximum average deviation rate may be between about 40% and about 50%. Routines for determining the variation in duration between peaks are described in even more detail in the applicant's various other co-pending applications, which are incorporated herein by reference.

  In some embodiments, the procedure for peak qualification includes an initial qualification step based on one or more of the above criteria (eg, criteria based on individual peaks) and the initial qualification. Removal of peaks that did not pass the qualification, and another qualification based on the calculation of one or more aggregate characteristic values for the remaining peaks (eg, all peaks that meet the initial qualification) A certification step. For example, identifying peaks and removing some peaks from the overall qualification (eg, removing peaks because they are too narrow or too wide) and then remaining If the peak data meets one or more aggregate threshold criteria as a whole, the remaining peaks can be qualified.

  FIG. 1 illustrates data qualification including an initial qualification step based on one or more criteria for individual peaks and further qualification of data based on one or more criteria for multiple peaks within a group. 1 illustrates an embodiment of a method 10 for gender recognition.

  In step 12, EMG signals can be collected and processed. The signal can be processed using various techniques, such as can be used to improve detection of seizure activity and / or to adjust the signal data for further analysis. For example, the collected signal is processed using one or more low-pass filters, high-pass filters, notch filters, or other filters to improve discrimination between seizure signals and other signals including noise sources. May be. In some embodiments, the signal may be processed by removing high frequency signal components, such as removing components above about 120 Hz or about 240 Hz. In some embodiments, one or more frequency bands may be separated from the collected EMG signal. Separation of one or more frequency bands includes, for example, using one or more filters for signal data separation and / or performing one or more other procedures, including performing a Fourier transform. You may go out. In some embodiments, multiple frequency bands can be separated from the collected EMG signal and T-square statistics can be calculated from the separated signal data. In some embodiments, the signal may be adjusted to prepare data for processing using one or more peak detection programs, or the signal may be adjusted for wavelet analysis. Good. For example, the signal data may be rectified, smoothed, and / or both.

  In step 14, one or more routines may be executed that may be configured to identify peaks in the collected EMG signal. Peak identification includes, for example, identifying portions of smoothed EMG data where the portion of EMG data or the curvature of the data changes (eg, inflection points or other important points can be identified in the data) Can do. The trailing and / or leading edge of one or more peaks may be determined and may be used to define the temporal boundaries of the peaks. Various peak features or characteristics can then be determined. For example, one or more peak widths, peak height values, peak areas, or other peak characteristics may be calculated. Reference may be made to peak amplitude, which as used herein may refer to either peak area or peak height if not further specified. If a peak is successfully qualified, those characteristics can be used, for example, to determine the level of activity of the qualified peak.

  In some embodiments, processing of the EMG signal and / or peak detection (steps 12 and 14) is performed by a smoothing technique (eg, moving average filter, Savitzky-Golay filter, Gaussian filter, Kaiser window, various wavelet transforms, etc.). ), Baseline correction process (eg, monotonic minimum, linear, loss normalization, minimum moving average, etc.) and peak discovery criteria (SNR, detection / intensity threshold, peak slope, local maximum, shape ratio, ridge, model Base criteria, peak width, etc.) and may include processing of rectified or non-rectified data.

  In some embodiments, the signal can be processed and peaks detected without loss or significant loss of time resolution of the collected signal (steps 12 and 14). For example, the data may be smoothed, integrated, or both, but the level of processing detects and qualifies peaks in relation to clonic activity May be appropriate. For example, a protocol for smoothing and integrating data will maintain a temporal resolution suitable for reliably detecting whether a peak meets a selected time width threshold indicative of clonic activity. May be selected.

  In step 16, peak data may be initially qualified against one or more qualification thresholds associated with individual peaks. As described above, various qualification thresholds can be used to qualify individual peaks. For example, in some embodiments, the peak is by meeting a threshold SNR, by meeting a minimum threshold for a peak time width of about 25 milliseconds to about 75 milliseconds, and from about 250 milliseconds to about 500 milliseconds. May be qualified by meeting a maximum threshold for a peak time span of.

  At step 18, peaks that did not pass the initial qualification may be removed. Thus, step 18 may serve as an initial screen for filtering the peaks from being included in other qualification steps. In step 20, the remaining peaks that were not removed after step 18 are qualified by calculating one or more aggregate characteristics of the plurality of peaks and comparing the characteristic value to one or more aggregate characteristic thresholds. You may be certified. For example, multiple peaks can be qualified together as a qualified group using any of the aggregate characteristics of the peaks described above, including the repetition rate or the amount of change in time between peaks.

  Once the peak data is qualified (e.g., qualified as containing some number of qualified peaks), the level of the qualified peak is determined. For example, following qualification of the remaining peaks (as shown in step 20), the level of activity can be determined and compared to an activity level threshold. As shown in step 22, a comparison between the activity level and the threshold activity level can be performed. Based on that comparison and / or other information, in at least some embodiments, a determination can be made whether to initiate one or more responses. For example, a qualified peak count or peak rate is determined and compared to a qualified peak count or peak rate threshold to determine, for example, whether a response including an alarm response is legitimate be able to. In some embodiments, the response can include recording a positive detection of seizure activity and can initiate an alarm response if several consecutive or nearby positive detections are made. it can. In some embodiments, in addition to or as an alternative to other alarm responses, other responses or actions may be initiated based at least in part on the qualified peak activity level. For example, if a certain percentage of qualifying peaks are identified in a portion of the collected EMG signal, that portion can be flagged or marked as a possible seizure event, The ratio of qualified peaks that have been made may be further linked to that portion of the collected signal data. The data and other relevant qualified peak metrics may then be stored and included in a searchable medical data database.

  The steps in method 10 may be performed within one or more time windows that may be the same or different duration. For example, data may be collected and processed over several suitable acquisition time windows so as to be suitable to maintain the desired time resolution of the monitoring system (step 12). Steps 14, 16, and 18 are generally run when individual peaks are detected, or at other appropriate times, so that these steps are completed before the remaining peaks are processed in step 20. Can be done. The timing window for group qualification (step 20) and for determining the overall activity level and response start of the qualified peak (step 22) may be adjusted for different routines. . In general, the qualification time window applied in step 20 and the response window applied in step 22 collect and analyze the desired amount of statistical data to reliably detect the presence of seizures or seizure-related patterns. For purposes, it is suitable for duration length. For example, in some embodiments, the qualification time window and the response time window may last for a duration of about 1 second to about 10 seconds or more. For example, some patterns related to recovery from seizure activity, or patterns indicating non-epileptic psychogenic seizure events, may show signals collected over a time scale that is at least somewhat longer than can be used to detect initial seizure activity. It can be detected by observing.

  In some embodiments, all remaining peaks may be qualified together within a given window for qualification at step 20. For example, all remaining peaks within the qualification time window may or may not meet the aggregation threshold. Thus, all of these remaining peaks may be qualified and counted as qualified peaks, or all of the remaining peaks do not meet the aggregation threshold, thereby increasing the overall This means that you did not pass the qualification. Accordingly, these peaks may not be counted in step 22 against the overall level of activity of the qualified peak.

  In some embodiments, the aggregation threshold may be selected to correspond to the probability that one or more physiological events may be missed within the qualification time window. For example, an aggregate eligibility threshold may be used to aggregate other appropriately detected events even if some physiological events (eg, individual physiological events that tend to produce seizure-related peaks) are not detected. It may be wide enough so as not to lose gender certification. For example, in a series of eight adjacent physiological events related to a burst of muscle activity during the seizure phase, 7 peaks are detected, but the peak detection program cannot identify a single event Unidentified events may not distort aggregate peak data so that not all peaks pass aggregate qualification.

  In some embodiments, the activity level of a qualified peak may be calculated in a response window that may span over several adjacent or overlapping qualification time windows. For example, the response window can be extended over a time frame that includes several qualification time windows. In some embodiments, for example, as understood in the art, to avoid or minimize latency between detection of seizure activity and response initiation, continuous or adjacent response time windows and Qualification time windows can be overlaid. The response window may be adjusted to a predetermined pattern. For example, two or more routines may be run simultaneously, which are configured or optimized for the detection of different patterns and set to analyze data in response windows of different duration lengths.

  Thus, although the qualification time window and the response time window can advantageously be the same duration, it should be understood that this need not necessarily be the case. For example, in some embodiments, qualification time windows may last about 2 seconds and adjacent qualification time windows may overlap for about 0.5 seconds. In each qualification time window, peaks are qualified and several qualified peaks are determined. The activity level of the qualified peak is determined, for example, at the completion of each qualification time window, eg, data from previous groups in three qualification time windows (eg, 5 seconds of data). Can be included. If the calculated qualified peak activity level exceeds the threshold, an appropriate response is initiated. One or more spurious peaks can eliminate all peak data from aggregate qualification, since the qualification peak activity level calculation can include more than one qualification time window Risk (step 20) is reduced.

  As described above, in some embodiments, the qualifying peak activity threshold and / or other qualification threshold may be determined even if some physiological event is not detected (or limited). It may be wide enough so that an alarm or other appropriate response can be initiated, even if the number of false peaks is mistakenly counted). In some embodiments, an estimate of the actual number of peaks resulting from physiological signs of motor activity during the seizure can be made so that the number eliminates peaks that are unrelated to seizure activity, such as noise spikes. Can be processed. Because spurious events may be eliminated, in some embodiments, the threshold for alarm initiation ranges, for example, based closely on the number of physiological events typically expected during an attack. May be centered more narrowly around the expected range. In some embodiments, the threshold for real-time detection of seizure events can be configured to improve the sensitivity of seizures, for example, creation of metadata and / or peak statistics on collected data For other purposes, including multiple links (archives in searchable databases), processing routines containing different criteria can be applied. In some embodiments, the routine for removing peaks that are expected to be spurious may be performed as part of archiving or retrieving data from a searchable database. Thus, in some embodiments, several routines or combinations of routines, including those with different threshold settings, can be used to create a real-time seizure detection method, a searchable database, or a data archiving method It may be executed as part of both.

  In some embodiments of the method for real-time seizure monitoring and / or data organization method of the database, one or more test groups consisting of a plurality of peaks are constructed and one or more consisting of the same plurality of peaks. Each of the test groups is a subset of the original peak group. As used herein, a subset of peaks or subsets of peaks is a subset of peaks or a subset of peaks wherein all peaks contained in another set are members of another set Or part of another set of peaks. In some embodiments, one or more aggregate characteristic values can be determined for each of a plurality of test groups that are a subset of an initial set of peaks. Some of those embodiments may further determine whether any of the subset of test groups has an aggregate characteristic value that satisfies an aggregate characteristic value threshold. For example, a test group consisting of a plurality of peaks that can be constructed from an initial set consisting of a plurality of peaks may include several consecutive peaks in the initial peak set. For example, FIG. 2 illustrates various combinations or groupings of multiple peaks that can be used as a calculation group that can be included as part of a qualification procedure with multiple test groups of peaks.

  In some embodiments, test group constructs may be integrated together using method 10. For example, as an alternative to an embodiment of the method 10, the processor need only calculate one or more aggregate characteristic values for all of the remaining peaks in step 20, and one or more of the plurality of peaks. A test group may be constructed from the remaining group of peaks, and one or more aggregate characteristics of each of the one or more test groups may be compared to one or more aggregate characteristic thresholds.

Referring again to FIG. 2, model data 24 is shown. The model data 24 may include, for example, initial qualification of the peak (as in some embodiments, as may be performed as described in step 16) and (in some embodiments, in step 18). Peak removal (as can be performed as is) is generated in a scenario that provides ₈ remaining peaks (x ₁ , x ₂ ,..., X ₈ ) within a time interval. May be. A test group may be created from these eight remaining peaks. For example, the peaks may be ordered based on the timestamp when they were identified, and the first four consecutive peaks (x ₁ , x ₂ , x ₃ , x in the time interval shown in FIG. _A first test group including ₄ ) may be created. As part of qualification, it can be determined whether one or more aggregate characteristic values of the first test group meet one or more aggregate characteristic thresholds. More generally, in some embodiments, some routines may include one or more aggregate characteristic values for a set of test groups, where each test group member of the set May determine whether any successive peaks (including four or more peaks) in the initial set of peaks from which it was created satisfy one or more aggregate characteristic thresholds. Referring back to the specific example of the model data 24, other test groups can be created where again the aggregate characteristic value can be determined and tested against the threshold. For example, the next group of four consecutive peaks (x ₂ , x ₃ , x ₄ , x ₅ ) within the time interval can be constructed and used to calculate the second aggregate characteristic value. In the next group, for example, the peak x ₅ is applied, the peak x ₁ is removed. As shown in FIG. 2, additional test groupings may be created and associated calculations may be determined. For example, with respect to the model data 24, a series of five consecutive groups may be created, and an aggregate characteristic value may be determined with respect to an appropriate threshold value for each generated group and tested.

  In some embodiments, the test group may be created, for example, by incrementally selecting the next peak present within an interval and keeping the number of peaks in the test group the same. . In some embodiments, aggregate property value calculations may be performed continuously or in other order so that all combinations of test groups within an interval that may include several consecutive peaks are considered. . In each calculation, for example, an aggregate characteristic value of the constructed test group can be determined and compared with a threshold aggregate characteristic value.

  In some embodiments, test groups that include other numbers of peaks (eg, a number different from 4) may be constructed. Building a test group can include scanning a time-stamped list of peaks in the order in which they are detected and selecting a number of consecutive numbers of peaks included in the test group. The list can then be incremented and the next group in the list can be selected. For example, as shown in FIG. 2, groups 1-5 may be configured by incrementing an ordered list by a single unit. In some embodiments, the list of peaks can be incremented in other desired units. When a set of test groups is constructed, the characteristic values of the members in the set of test groups can be calculated, for example, in a convenient order to identify peaks that meet qualification for at least one of the test groups. . For example, in some embodiments, a peak can be considered qualified if it is part of at least one test group that meets qualification. For example, the peaks in the window can be processed with groups of 4 or other suitable numbers, such as about 4 to about 8 peaks, as described above. Such a procedure may be used, for example, to limit the number of calculations performed in the processing of data. Furthermore, it may be more convenient to write a protocol executed by a computer processor to build a test group that includes a preselected number.

  In some embodiments, in addition to using a predetermined pre-selected number of peaks to create a test group, a test group that includes other numbers of peaks or a test that includes a range of peaks. Other test groups containing groups can be created. Thus, another set of aggregate characteristic value calculations can be performed. For example, one aggregate characteristic value can be calculated for each group. One or more aggregate property values may also be calculated for each group.

Different combinations of aggregate characteristics and aggregate characteristic thresholds can be applied to identify a particular pattern. Continuing with the example described herein where a grouping of four consecutive peaks is considered, an aggregate characteristic value can then be determined for a group of five consecutive peaks. In some embodiments, the plurality of groups of peaks may be limited to those that fall within a preselected time window. In other embodiments, one or more test groups of peaks may be surrounded by or expanded into adjacent windows. Appropriate rules may be established for a particular routine for processing groups of tests that can be extended to adjacent windows. Referring to the example of FIG. 2, for ease of explanation, considering only combinations of consecutive peaks that fall within the time window, and considering all combinations of test groups that include five consecutive peaks, 4 Two test groups are created. That is, the first group of five consecutive peaks in this example can include peaks (x ₁ , x ₂ , x ₃ , x ₄ , x ₅ ). Other groups containing the last group in the set containing the peaks (x ₄ , x ₅ , x ₆ , x ₇ , x ₈ ) (when repeating time-stamped data in single peak increments forward) It may be created.

  In some embodiments, some minimum number of peaks (e.g., the minimum number selected to qualify as a group having a suitable number of peaks to perform the desired calculation-e.g., between peaks A group of peaks that range from a standard deviation over time (such as a number to reliably calculate) to the total number of peaks identified in the time window may be tested. For example, if 4 peaks are selected as the minimum number of peaks suitable for qualifying peaks accurately using specific aggregate characteristics, and 8 peaks are identified in 2 seconds, the group Various groupings can be constructed that include 4, 5, 6, 7, and 8 peak counts and included in the qualification calculations. In that case, by way of example only, a total of 15 groups can be constructed.

  In some embodiments, the test group can consist of a peak group that includes only consecutive peaks within one peak group from which the test group can be constructed. In other embodiments, the test group is a continuous peak, a non-continuous peak (eg, one or a combination of peaks where one or more intervening peaks are excluded from the test group), or both Can be included.

  For example, FIG. 3 shows various groups of model data 26 and multiple peaks that can be constructed. The model data 26 may be, for example, initial qualification of peaks (as performed in some embodiments as described in step 16), and (in some embodiments, described in step 18). Peak removal) as generated in a scenario that provides five remaining peaks (A, B, C, D and E) within a time interval. Groups can be constructed from that group of remaining peaks. In the model data 26, groups of peaks (A, B, C, D, and E) are shown. Peak C is a noise artifact and may be a peak unrelated to the desired physiological signal intended for measurement in EMG, but may not have been removed by other processing methods. A grouping that includes non-contiguous peaks is a seizure or seizure-related activity where a spurious peak that was not removed in the initial qualification or other protocol and procedure excludes one peak from a group of peaks. This may be done under the assumption that a group of peaks that can be qualified to test can be tested for qualification. For example, if peak C is removed, the expected pattern or peak group can occur. For example, such a pattern may be characterized by satisfying both the aggregation threshold for repetition rate and the amount of change in time between peaks. The combination of peaks (A, B, D, E) can then be considered to meet the criteria for the seizure pattern, which is identified by removing one spurious peak from the initial peak group. .

  For some patients, especially if contact between the skin and the electrode is poor, at least in some cases, excessive sweating and / or the patient is transitioning to or from a seizure One or more peaks that are not directly related to clonic activity are subject to initial qualification or other screening means if they are in a state that may be the result of another state that is likely to be at recovery It may not be removed. In that case, an improved protocol for examining whether spurious peaks are present in a given data set may be advantageous depending on some patients and / or some situations. Construction of a test group consisting of non-continuous peaks can meet the aforementioned need for an improved protocol.

  In some embodiments, if some peaks are removed from the data as part of peak qualification, other requirements for alarm initiation or emergency alarm initiation to minimize false positive detection May be used. Alternatively, the response may be different if the peaks are removed to meet the activity level threshold. For example, the detection protocol can identify seizure-related patterns by removing a number of peaks from a particularly noisy data set. The system then includes one or more protocols including protocols that involve contacting or not taking care of the caregiver or patient (eg, one or more protocols may be passively terminated). The alert protocol may be initiated to test the data for detection that can improve the reliability of one or more supporting events, seizure detection. Some procedures for removing peak data may also be performed as part of the data archive. For example, a set of routines can be used to initiate one or more alarm protocols. Another set of routines, including routines configured to remove suspicious or false noise peaks and look for seizure patterns, isolate or detect peak patterns that indicate true physiological activity, and peak data from noise sources It can be configured not to be biased by inadvertent inclusion.

  In some embodiments, the protocol for removing spurious peaks does not specifically consider whether the peaks may be incorrect or more than other peaks that are in the peak set. It may be done by removing one or more peaks without specifically considering whether it may be more or less spurious. For example, in some embodiments, peaks may be removed sequentially or in any convenient order from the remaining groups of peaks to test different combinations, but the order in which peaks are removed is , May not be related to any particular characteristic of a given remaining peak. However, in some embodiments, the decision or algorithm for removing the peak may be based on other factors. For example, peak qualification by determining aggregate characteristics of a plurality of peaks (eg, as described in step 20) may be that any of the plurality of peaks is an outlier. Statistical processing may be included to determine whether one or more peak characteristics should be removed as indicated. For example, it can be seen that including a given peak in a group excessively biases characteristic values (or aggregate characteristic values) such as the amount of change in time between peaks. For example, a spurious peak can generate one or more data points in a characteristic value calculation that bias the group data in a larger manner than would be expected for a data population that is normally or otherwise distributed. is there. And, by removing that given peak from a group of peak data that may otherwise lose qualification, the subset peak set may be fully qualified. In some embodiments, any of a variety of mathematical techniques for processing the data to determine the presence of outliers may be performed.

  In some embodiments, a test grouping of peaks may be created from a group of peaks that are themselves a subset of another group. An embodiment in which a test group can be constructed from other groups, which are themselves a subset of another group of peaks, can be understood with reference to FIG. FIG. 4 shows a model set 28 consisting of a plurality of peaks. The multiple peak model set 28 may include, for example, initial qualification of peaks (such as may be performed as described in step 16 in some embodiments) and step (in some embodiments, steps). Peak removal (as can be performed as described in FIG. 18) can be generated in a scenario that provides 10 remaining peaks (A, B,..., J) within the time interval. A group of peaks 30 is shown including peaks (C, D, E, F and G) which are a subset of the remaining peaks (A, B,..., J). For example, as described with reference to FIG. 2, the peak group 30 can be constructed from a model set 28 including a plurality of peaks. For example, the peak group 30 can be obtained by scanning a time-stamped list of model sets 28 consisting of a plurality of peaks in the order in which they were detected, and determining the number of several consecutive peaks contained in the subset group. By selecting, it can be constructed. Next, the list can be incremented to identify multiple groups, including subset group 30 shown in FIG. Then other groups of peaks (eg, groups 32, 34, 36, 38, and 40) can be constructed from subset group 30. For example, the aforementioned groups (32, 34, 36, 38 and 40) can be constructed by continuously removing one peak from group 30.

  Continuous removal of peaks from subset group 30 can generate a limited number of test groups, while other subset groups can be generated from model set 28 consisting of multiple peaks. Each of these other subsets can serve as a basis for creating a test group. In general, build a significant number of test groupings based on building a set of subsets and then performing a continuous removal of one or more peaks from members of the set of subsets can do. Thus, the methods described herein can be effective in searching for a significant number of peak combinations and finding patterns of activity from noisy data. In some embodiments, only an appropriate number of peaks, such as one peak, can be removed from the first subset group. In some cases, the maximum ratio of peaks that can be removed during test group construction can be defined in the algorithm. For example, in some embodiments, the maximum allowable ratio of removed peak to peak in the group remaining after peak removal may be about 1: 5 to about 1:10. Exclude or remove from the subsets by selecting the appropriate number of peaks in the constructed subsets (eg, 5 peaks as shown in FIG. 4) and / or to generate test groups By selecting the appropriate number of peaks to be played, multiple test groups can be built, increasing the chances of building specific patterns of peaks that meet qualification. However, the number of peaks that can be excluded from the test grouping can be controlled. Thus, the risk of pattern misdetection can also be limited. Thus, in some embodiments, suitable for searching noisy data, but the risk of false positive detection, problems that exist when test groups are created randomly, and / or indiscriminately Appropriate procedures can be performed to minimize the risk of using other protocols that may allow for peak elimination. The deleterious consequences of false positive detection can be more problematic in methods associated with real-time detection of abnormal muscle activity. However, these concerns are not the same for caregivers reviewing and searching data in a database. Thus, in some embodiments, different procedures are performed for building test groups that can be used in organizing data for inclusion in a searchable database than that used in real-time monitoring. It can be advantageous. For example, a more aggressive peak removal algorithm may be applied when including data in a searchable database.

  In some embodiments, the procedure for generating a test grouping includes removing a peak from an initial group of peaks, the peak removal being performed in a logical order or pattern. For example, a method for monitoring a patient can include building a test group in which the test group most likely to be associated with a physiological pattern is first tested or ranked with a higher degree of confidence. For example, the procedure can order the peaks based on one or more peak characteristics. Based on the ordering, a list of candidate peaks that are most likely to be spurious (eg, not related to physiological activity) may be generated. For example, a group of peaks may be ordered based on a time width, where the order of time of most peaks is concentrated around a central range, but one or more peaks are centered around it. It may be used to identify being characterized as having a time span that is out of range. Peaks outside the most common range are removed and can be removed first in the generation of test groupings, or test groups created from such removal are ranked with higher confidence than other test groups. Can be done. In some embodiments, a test grouping can be constructed by removing one or more peaks at either end of the peak characteristic order. For example, the highest or lowest value in the characteristic order of one or more peaks may be selected to be removed.

  For example, FIG. 5 shows a bar chart of a model that characterizes the distribution of peaks identified during the collection period, and shows bars 52 and 54. The peaks associated with bars 52 and 54 are characterized as having a minimum characteristic value 52 and a maximum characteristic value 54 of a group of identified peaks. The peaks associated with these bars 52, 54 may indicate peaks having characteristic values at the edges of the initial qualification threshold. As shown in FIG. 5, the majority of the peaks tend to be clustered towards larger values of peak duration. And the peak associated with bar 52 represents the maximum deviation from the other peaks in the group. Thus, the peak may be removed from the set of peaks or may be removed first in the test group selection. The test group generated by removing the peak associated with bar 52 can be viewed with higher confidence than the other test group associated with removing the other peaks. The aggregate characteristic values for the remaining subset of peaks can then be determined. In some embodiments, an average value of an initial group of peaks is determined, and a deviation value from the determined average value is determined for each individual member in the initial group of peaks. Good. A test group may be created by removing peaks in the order of the peaks identified as having the highest deviation from the average. For example, as shown in FIG. 5, the peak associated with bar 52 may have the highest deviation from the mean, most likely wrong, or noise in the collection window. It may be most relevant. Thus, the peak can be removed or initially removed in a protocol for generating a test group of peaks.

  In some embodiments, the routine may remove a peak from another group of peaks based on how the peaks in the group meet the initial qualification criteria. For example, the routine may test whether assigning an initial certainty value to a peak and removing one or more peaks facilitates qualification and identification of seizure-related patterns. it can. In order to minimize computational assistance or to provide other metrics of overall certainty, routines can identify seizure patterns that can identify the removal of the peak with the lowest certainty value (eg, aggregate characteristics). It is generally tested whether it identifies a group of peaks that meet a threshold, or may be tested first. In some embodiments, once seizure-related patterns are identified, computing or signaling resources can be allocated to perform one or more system operations most efficiently. For example, a warning message or emergency message is issued as part of the alarm initiation protocol, and further configuration of the test group or transmission of data related to the test group (eg, transmitted from a remote device to a management device or base station) However, it may be terminated or only performed after completion of other operations, such as those most important to confirm that emergency care is being provided to the patient. The peak certainty may in some embodiments be based on the patient or patient demographics, or may be based on matching with other peaks collected during a given time window. Good. In some embodiments, the confidence value for the peak is determined, for example, as described in detail in Applicant's US Pat. No. 8,983,591, incorporated herein by reference, such as SNR, width and It may be based on a combination of metrics including amplitude.

  For any given time interval during the monitoring period, the number of detected peaks or the number of initially qualified peaks may be determined, and for the majority of the monitoring period, these numbers are generally It may be low. For example, even if a random noise event is incorrectly identified as a peak, most time windows may contain only a limited number of such events. In addition, normal movements independent of seizures generally do not produce peaks, or fail to produce a high percentage of initially qualified peaks. That is, the procedures described herein for peak initial qualification can be used to remove peak data from most motor signs associated with non-seizure motion.

  In some embodiments, if a certain number of peaks or initial qualified peaks are identified, the algorithm can attempt to fit the remaining peak data to the seizure pattern. For example, in some embodiments, a test group of peaks may be constructed only when an appropriate number of peaks for generating a test grouping are identified. For example, the remaining peaks following step 18 of method 10 are low for most of the monitoring interval, so the method also needs to perform further calculations related to the construction of the test group, and the There is no need to perform calculations. Thus, in some embodiments, operations associated with test group generation and property value calculations are performed only when necessary, and the computational resources dedicated to the calculations and remote transmission of the data may be further limited. Several implementations, including embodiments where operations associated with test group construction are performed on one processor and then sent to another processor for execution of peak construction and / or comparison with multiple patterns In form, a protocol for selective execution of test group construction may be applied.

  In some embodiments, certain calculations, such as test group construction described herein, are automated and for various components (eg, detection units and / or base stations) described herein. When running with any suitable processor in and finds a threshold number of peaks or initial qualified peaks within a given time window, especially the time window, initial qualification protocol, and remaining required May be performed only if the number of peaks (required to invoke further calculations) is appropriately selected as described in the various embodiments described herein.

  In some of the embodiments described herein, more than one peak group may be qualified during a particular time period, such as a response window. For example, such a scenario is when multiple peaks are identified from EMG data collected on the response window, multiple test groups are built over that period, and different test groups are found to meet qualification Can happen. In some embodiments, peaks in different test groups may be distinguished. In other embodiments, the test group may sometimes include peaks that are members of more than one group. Then, once qualified peak groups are identified, the groups may be combined and used to calculate the overall level of activity for the qualified peak. Various methods for combining peak groups to determine the level of qualifying peak activity and initiating one or more system responses based on the level of qualifying peak activity are: It can be implemented in the embodiments described in the specification. For example, techniques are described herein for calculating the overall level of peak activity where the group contains only distinct peaks. Other techniques may be applied where individual peaks may be qualified separately in two or more groups.

  FIG. 6 shows an embodiment of a method 60 for combining peaks. For example, as will be understood with reference to a model set 70 (shown in FIG. 7) of a plurality of peaks, when the first test group 72 includes the first four peaks therein, It can be seen that the first test group 72 has passed qualification. Thus, the first test group 72 can also be referred to as a first group of qualified peaks. The second test group 74 of peaks may also be identified as a second group of qualifying multiple peaks that pass qualification as well. Thus, it can be seen that the model set 70 of peaks includes a total of 8 qualifying peaks that exist as two separate groups 72 and 74. In this scenario, the two groups contain distinct peaks. That is, there are no peaks that are not members of both groups. It should also be understood that groups 72, 74 can be qualified even if including all 8 peaks results in a qualification failure. For example, if all the peaks in the peak model set 70 are qualified by calculating the peak repetition rate, the data set may be biased by a relatively long gap 75 and the comparison A long gap 75 may be present, for example, if the peak finding routine fails to detect one or more abnormal motor signs during the stroke.

  Referring again to FIGS. 6 and 8, in step 62, the two groups 72, 74 are identified as meeting qualification and then combining the two groups using one or more techniques. Can do. For example, in step 64, separate groups of qualified peaks, such as groups 72, 74, may be combined by summing each of the two separate groups of peaks. The overall level of qualified peak activity can then be calculated. For example, for a set of peaks 70 in which groups 72 and 74 are separately qualified, the eight qualified peaks are combined together to calculate the overall level of activity for the qualified peaks. can do. In step 66, a determination may be made whether the overall activity level is above a threshold. For example, it may be determined that an alarm should be initiated if a threshold activity level is exceeded.

  In some embodiments, combining two or more groups of qualified peaks (step 64) includes comparing one or more characteristic values for different qualified groups. Can do. For example, for each group of peaks, peak statistics can be calculated to determine the degree of similarity or difference between the various peak statistics between the groups. Of note, the degree of similarity between peak groups can be predicted for some patterns. For example, if the peak group is part of the middle part of the mesophase of a generalized tonic-clonic seizure, the duration of the period adjacent to the elevated peak part may generally be similar. Thus, if two or more groups of peaks show similarity in this statistical metric, the combined group can be considered to match the expected pattern of the middle part of the clonic phase. For other patterns, the degree of metric difference described above may be expected between groups of peaks collected over time. For example, if a peak across two or more groups within the response window is characterized by an increase in duration of the period adjacent to the elevated peak portion, normal progression from the seizure phase can be recognized.

  In some embodiments, combining two or more groups of qualified peaks (step 64) may include one or more pooled characteristic values based on peak data from among various groups. Determining. For example, two or more groups of peaks can be qualified separately, and then pooled characteristic values are calculated to determine the relationship (eg, degree of similarity) between the two or more groups can do. In some embodiments, separate qualifications when multiple groups of pooled characteristic values meet a threshold, eg, if the pooled characteristic values are within a specified minimum threshold and / or maximum threshold. Peak groups can be combined to determine the overall level of qualifying peak activity. Thus, in some embodiments, further qualification steps can be applied to independently qualified peak groups prior to determining the overall peak activity value.

  For example, two temporally separated peak groups may each be independently qualified, and pooling characteristic values together ensures that the two groups are part of the same part of the seizure. It may be predicted whether it will be treated. For example, in two separate duration intervals in the middle part of the seizure phase, two peak groups can be measured, but in the intervening portion of the data collected at the time between the two intervals, the data is noisy And that part of the data may not produce useful peaks. However, each of these two peak groups may be qualified separately, and pooling the data together indicates that each train is part of the middle part of the clonic phase If so, these two separate groups may be combined to determine the overall activity level of the qualified peak. Thus, the overall activity level of the qualified peak can be used in determining an appropriate response.

Pooled characteristic values can refer to characteristic values calculated by including data from two or more peak groups weighted according to the number of peaks and the degree of freedom. For example, in some embodiments, a characteristic value for a group of peaks can be a change in time between peaks that can be characterized as a standard deviation. In order to calculate the pooled characteristic values for two groups, a first standard deviation value s ₁ is calculated for one group peak, where n ₁ is the sample size of that group. A second standard deviation value s ₂ for the second group of peaks can also be calculated, where n ₂ is the sample size of the second group. A pooled standard deviation value containing data from the two peak groups can then be calculated as follows.

S _(pooled) = Sqrt [[(n ₁ -1) S ₁ ² + (n ₂ -1) S ₂ ² ] / [(n ₁ -1) + (n ₂ -1) -1]] Equation 1
More generally, the standard deviation values s ₁ , s ₂ ,. . . , S _k and any number of peak groups n ₁ , n ₂ . . . For _nk , the pooled value may be calculated as follows:

S _(pooled) = Sqrt [[(n ₁ -1) S ₁ ² + (n ₂ -1) S ₂ ² +. . . + (N _k −1) S _k ² ] / [(n ₁ −1) + (n ₂ −1) +. . . (N _k −1)]] Equation 2
In some embodiments, the pooled values can serve as a check to verify that a group of peaks is logically considered part of the same part of the seizure. For example, in general, in some of the clonic phases, the peak velocity can be maintained within a particular boundary even though there is a general decrease in peak velocity at a later stage of the clonic phase. Also, by pooling data from multiple groups of separately detected and qualified peak groups, sporadic detected peak data of a noisy signal can still be reliably combined.

  FIG. 8 illustrates an embodiment of a method 80 that collects EMG signals, identifies peak data in the EMG signals, and evaluates whether one or more seizures or seizure-related movement symptom patterns may be present. In some embodiments, the method 80 can be performed in real time as a patient monitoring strategy and EMG signals can be collected directly. In other embodiments, the method 80 may be performed following signal collection, including, for example, adding data to a medical database or retrieving data in a medical database. In step 82, various classes of embodiments are described instead. For example, at step 82, EMG signals may be collected and peaks may be identified in the collected EMG signals. Alternatively, previously collected signal data may be accessed, such as by downloading a portion of the signal data.

  In some embodiments, EMG signals can be collected, peaks detected, and initial filtering or screening of these peaks can be performed before retrieving data for the presence of one or more patterns. For example, individual peaks may be selected for one or more characteristics of individual peaks before performing other operations. For example, in some embodiments, peak screening operations such as step 16 and step 18 of method 10 can be performed as part of step 82. In some embodiments, if the method 80 is performed from existing EMG data, the database operator selects, for example, one or more patients and / or one or more time ranges of the collected data can do. The program can subject the data to peak detection, or the already identified peaks can be downloaded or accessed for further processing.

  In step 84, the method 80 may include searching for identified peaks for one or more peak groups that exhibit one or more seizures or seizure-related activity patterns. In some embodiments, step 84 can include two parts. For example, in the first part, the first group of identified peaks may be subjected to the construction of a test group. That is, multiple subset test groups can be created from the first group of identified peaks. Second, the characteristic value of the test group can be compared to a characteristic value threshold suitable for qualifying the test group as an indicator of a given pattern.

  Some embodiments described herein include patterns associated with, for example, epilepsy seizure activity, progression during generalized tonic-clonic seizures, non-epileptic psychogenic seizure activity, and combinations thereof Various motion sign patterns can be detected. In some embodiments, patterns of activity associated with movement after paroxysmal exercise can also be identified. To detect or search for a pattern, for example, compare peak data containing different groups of peaks constructed from the original group with one or more qualification thresholds suitable for defining the pattern can do. The pattern can also be defined by the protocol used to build the test group. For example, some patterns may be identified using different algorithms for test group construction. For example, an algorithm may be defined with rules that allow or do not allow the application of test groups with excluded peaks, or define the maximum number or ratio of excluded peaks.

  In some embodiments, more than one pattern may be searched or identified in the collected EMG signal. For example, in some embodiments, the first seizure pattern is characterized by the detection of a threshold number of qualified peaks for at least 8 qualified peaks included in only one test group. It may be attached. In another example, the seizure pattern may be characterized by detecting a threshold number of qualified peaks for at least 12 qualified peaks included in no more than two test groups. In another example, the seizure pattern may be characterized by detecting a threshold number of qualified peaks for at least 15 qualified peaks that are included in no more than three test groups. Such patterns may be defined by one or more qualification thresholds or pooled characteristic thresholds. For example, if the peak group shows a peak rate that is greater than the minimum peak rate of about 2 peaks per second and less than the maximum peak rate of about 7 peaks per second, the peaks in the above pattern are qualified Can be done. In some embodiments, the peak may be defined by alternative or additional aggregate qualification or other qualification thresholds. For example, the peak in the aforementioned pattern is about a minimum threshold peak time width of about 100 milliseconds and a maximum threshold peak time width of about 300 milliseconds, the duration of time between peaks that is between about 3% and about 40%. May be qualified against an aggregate average deviation rate value, or both.

  In some embodiments, the seizure pattern is characterized by detecting a threshold number of qualified peaks from at least 8 qualified peaks to at least 12 qualified peaks. Also good. This pattern is further characterized by a peak rate qualification threshold or pooled characteristic threshold that is greater than the minimum rate of about 5 peaks per second and less than the maximum rate of about 7 peaks per second. Also good. The aforementioned pattern may be part of a group of patterns. For example, another pattern can identify peaks in the range of about 4 to about 6 peaks / second. Yet another pattern can identify peaks in the range of about 2 to about 5 peaks / second. Another pattern can identify peaks in the range of about 1 to about 3 peaks / second. Yet another pattern can identify peaks in the range of about 0.2 to about 1 peak / second. For example, the minimum or maximum duration of the rising part of the peak, the duration of one or more intervening periods of the smaller signal on either side of the rising part of the peak, the duration of the time between peaks Other qualification thresholds may also be included, including required average deviation rate values for and / or combinations thereof.

  A particularly useful pattern is that peaks are presented at a relatively low rate, such as less than about 3 peaks per second, with a time threshold for the rising portion of the peak and the duration of the smaller signal on either side of the peak. A threshold time span may be defined when used for peak qualification. In general, such a low repetition rate may be related to the time that the patient is recovering either from a seizure phase normally or abnormally. Define a specific pattern or combination of patterns that organizes the data so that, for example, it can be determined whether a patient generally or always transitions from an attack with a sudden or gradual change in peak repetition rate can do. The pattern is that the peak repetition rate changes as the time width of the peak rising portion, the time width of the smaller portion of the signal between the peak rising portions, or the peak rising for the smaller portion of the signal between the peak rising portions It can be further specified whether it can be linked to a given change in the ratio between the selected parts. For example, if the patient tends to transition from seizure without experiencing the expected rate of increase in the time span of the smaller portion of the signal during the rising portion of the peak, the pattern may be psychogenic non-epileptic seizure activity. May be flagged as possibly showing. In addition, group qualifications based on the time distribution between the peak patterns may be created that indicate either a gradual progression from the seizure or a more sporadic pattern. Changes in peak amplitude may be further considered.

  In some embodiments, more than one pattern indicative of seizures or seizure related activity may be detected during a patient monitoring period. For example, at step 84, it may be determined that one seizure pattern can be identified and later another pattern can be identified. A combination of seizure patterns can be used to obtain information that is difficult to gather from other patterns or simpler patterns. In a related aspect, in some embodiments, the simpler patterns described herein can function as building blocks for more complex patterns. For example, more complex patterns can be defined using the patterns described above. Some of these patterns can last for a significant duration extending over two or more portions of the seizure phase, including transition from the clonic phase.

  In some embodiments, different patterns that can be associated with a common clinical diagnosis can be grouped together. Thus, if one or more of these patterns are identified, the importance of that pattern can be flagged and presented to the caregiver. For example, in general, if the transition from seizures is abrupt and is not accompanied by shorter duration duration changes on either side of the peak, such a pattern is probably a psychogenic non-epileptic seizure It can be flagged as related to the occurrence of activity. Patterns that exhibit this behavior can be flagged even when other characteristics are different, such as the average repetition rate or the average duration of shorter periods on either side of the peak.

  It should be understood that some patterns may include characteristic value thresholds associated with individual peaks in addition to aggregate characteristic thresholds. For example, it may be useful to compare peak data to individual characteristic value thresholds as part of an initial screen used to generate an initial set of peaks that are subsequently subjected to test group construction. It is also useful to compare the peak during or after this initial selection with one or more individual characteristic values. For example, in different parts of the seizure, it is expected that some characteristics of individual peaks will change. Therefore, the best selection for generating a test group suitable for use in testing part of a seizure is to generate a test group suitable for testing other parts of the seizure. It doesn't have to be the same. Thus, in some embodiments, an initial broad selection of individual peak characteristics can be applied to define an initial group of peaks for test group construction. Further processing can include determining whether the peaks in the test group have individual characteristic values that can be more stringent than those applied in the initial screening of the peaks.

  Other patterns (eg, in addition to the specific embodiments described above) may also be stored in the database and compared to the collected data. A pattern can be defined to see activity over a time window. For example, one pattern looks for a group of peaks that show characteristic values that are typically found near the beginning of the seizure phase, and another pattern is typically near the end of the seizure phase portion of the seizure. You may look for the peak group which shows the characteristic value found. The windows suitable for viewing different parts of the seizure may be different. For example, the window can be appropriate such that peak data is limited to a relatively short time window (eg, about 2 to about 10 seconds). Other patterns may consider data over a longer duration and look for activity where peak activity changes over time, such as expected during an episode, for example. A pattern that considers data over a longer period of time can look for the presence of independently qualified groups and look for statistical trends in consecutive or nearby groups.

  For example, the pattern looks for a first pattern set with a threshold that is typically achieved by activity at the beginning of the seizure part of the seizure, and whether the pattern repeats or is associated with the late part of the clonic part Also look for whether there is a second pattern to do. The first pattern and the second pattern may have different thresholds, eg, for peak width, peak repetition, peak amplitude, or combinations thereof, and in some embodiments, the patterns are selected and It may be seen whether or not the characteristics change, or for example, if there is no characteristic change in peak width, repetition and / or amplitude and the activity ends. And, in some embodiments, the caregiver may have qualified peaks over time to assist the caregiver in identifying whether the patient is prone to non-epileptic psychogenic events. Data can be searched for patterns that change to.

  To collect large amounts of EMG and other patient-related data, organize such data for system optimization, and initiate alarms in response to suspected seizures or post-seizure motor signs Various systems are suitable. FIG. 9 illustrates an exemplary embodiment of such a system that may be configured to monitor a patient for seizure activity using the methods described herein. In the embodiment of FIG. 9, seizure detection system 100 can include an acoustic sensor 108, a video camera 109, a detection unit 112, a base station 114, and an alert transceiver 116. The detection unit 112 is configured as a portable and wearable device placed on (or just attached to) on or near any suitable muscle or group of muscles that may exhibit motor symptoms during the seizure May be. And in some embodiments, the system 100 can include any of a variety of wireless local area network technologies. For example, the detection unit 112 can communicate wirelessly to the Internet using WiFi®, Bluetooth®, or via another local network. Then, using the local network, the detection unit 112 may send data directly over the Internet or via the intermediate base station 114 in some embodiments. In some embodiments, the caregiver may be contacted directly through a local network such as WiFi. Base station 114 may be connected to the Internet wirelessly (eg, through a local network) or may be linked to the Internet through a hard connection. The detection unit 112 may include one or more EMG electrodes that can detect electrical signals from muscles at or near the patient's skin surface and send those electrical EMG signals to the processor for processing. it can. The EMG electrode can be coupled or attached to the patient and in some embodiments may be implanted in the patient's tissue in the vicinity of the muscle that can be activated during the stroke. Implantable devices may be particularly suitable for some patients where the EMG signal can usually be weak, for example, a patient with a significant amount of adipose tissue.

  Base station 114 may have received and processed an EMG signal from detection unit 112, acoustic data from acoustic sensor 108, and / or data from other sensors, and seizures may have occurred from the processed signal. A computer capable of determining whether and sending a warning to the caregiver may be provided. Alert transceiver 116 may be carried by or placed near a caregiver to receive and relay alerts sent by base station 114 or to the Internet. Included in system 100, including, for example, wireless devices 117, 118, storage database 119, electronic devices for detecting changes in electrode-skin interface integrity, and one or more environmental transceivers Other components that may be described are also described in Applicant's US Pat. No. 8,983,591 and other references incorporated herein.

  When using the device of FIG. 9, for example, a patient 120 susceptible to epileptic seizures can rest, for example, in bed or be in other places that can be included in daily life, and can be physically and physically Can have a detection unit 112 that touches or is close to the body. The detection unit 112 may be a wireless device so that the patient can get up and walk around without being constrained by a fixed power source or a more bulky base station 114. For example, the detection unit 112 may be woven into a shirt sleeve, mounted on an armband or bracelet, or an implantable device. In other embodiments, the one or more detection units 112 or other sensors may be a bed, chair, infant car seat, or other suitable clothing, furniture, equipment, and accessories used by people who are prone to seizures. Can be arranged or incorporated in The detection unit 112 can include a simple sensor such as an electrode that can send signals to the base station 114 for processing and analysis, or it can include a “smart” sensor that has some data processing and storage capabilities. Can be included. The detection unit 112 can include one or more smart client applications. In some embodiments, the simple sensor can be wired or wirelessly connected to a battery operated transceiver attached to a belt worn by a person. In some embodiments, the detection unit 112 may be configured with a pattern database and / or execute instructions for construction of one or more subsets of multiple peaks from a set of initial peaks. A processor configured as described above may be included.

  The system 100 can monitor the patient 120 during rest, such as in the evening and at night. If the patient detection unit 112 detects a seizure, the detection unit 112 may be wired or wireless, eg, far away from the base station 114 via a communication network or wireless link and via Bluetooth (Bluetooth®) Simultaneous communication with the mobile phone 117 or other portable or desktop device 118 or to the base station 114 and the remote mobile phone 117 or other device 118 may be possible. In some embodiments, the detection unit 112 may send several signals to the base station device for a more thorough analysis. For example, in some embodiments, the base station 114 can include a broader pattern of databases suitable for comparison with one or more portions of EMG data.

  In some embodiments, the detection unit 112 processes and uses EMG signals (and optionally or in some embodiments, ECG, temperature, orientation sensor, saturated oxygen, and / or audio sensor signals). Thus, an initial assessment of the likelihood of occurrence of a seizure can be made, and those signals and their assessments can be sent to the base station 114 for separate processing and confirmation. If the base station 114 confirms that a seizure may have occurred, the base station 114 initiates an alarm and sends an email, text, cell phone, or any suitable wired or wireless message indicator. To transmit the warning to the designated individual on the network 115. As will be appreciated, in some embodiments, the detection unit 112 is smaller and more compact than the base station 114 and is convenient for using a power source that has limited strength. Thus, in some embodiments, it is advantageous to control the amount of data transferred between detection unit 112 and base station 114. This is because the life of an arbitrary power supply element incorporated in the detection unit 112 is extended by doing so. In some embodiments, when one or more of the detection unit 112, base station 114, or caregiver, eg, a remote caregiver monitoring a signal provided from the base station, determines the occurrence of a seizure. The video monitor 109 may be triggered to collect video information for the patient 120.

  A base station 114 powered by a general household power source and containing a backup battery has more processing, transmission and analysis capabilities available for operation than the detection unit 112, and a greater amount of signal history. And the received signal can be evaluated in light of the large amount of data. The base station 114 communicates with a warning transceiver 116 that is remotely located from the base station 114, such as a family bedroom, or with wireless devices 117, 118 that are carried by a caregiver or that are placed in the workplace or clinic. can do. Base station 114 and / or transceiver 116 may send alerts or messages to designated people via mobile phone 117, PDA 118, or other client device via any suitable means such as network 115. Thus, the system 100 can provide an accurate log of seizures to make the patient's physician more quickly aware of the success or failure of the treatment regime. Of course, the base station 114 can simply comprise a computer with an installed program that can receive, process, and analyze the signals described herein and transmit alerts. Base station 114 may include one or more smart client applications. In other embodiments, the system 100 is configured to transmit signal data to a smartphone, such as an iPhone (iPhone®), for example, and an EMG signal as described herein using an installed program application. Can be simply provided with an EMG electrode as part of a device configured to receive an EMG signal from the electrode. In a further embodiment, so-called “cloud” computing and storage devices can be used over the network 115 for storage and processing of EMG signals and associated data. In yet another embodiment, the one or more EMG electrodes are collectively as a single unit having a processor capable of processing EMG signals and sending alerts over the network as disclosed herein. It could also be packaged. In other words, the device can comprise a single product that is placed on the patient and does not require a base station or a separate transceiver. Or a base station may be a smart phone or a tablet, for example.

  In the embodiment of FIG. 9, EMG signal data may be sent to remote database 119 for storage. In some embodiments, EMG signal data is transmitted from a plurality of epilepsy patients to a central database 119 to establish and improve the general “baseline” sensitivity level and signal characteristics of epileptic seizures. Can be “anonymized” to provide Database 119 and base station 114 are remotely accessed from remote computer 113 via network 115 to allow detector unit and / or base station software updates and data transmission. Also, in some embodiments, alarm computer and EMG signal data between different devices associated with a specified individual configured to receive a signal by remote computer 113 or another computer is any number. The function of monitoring the exchange of data including the exchange of. Base station 114 may generate an audible alarm or a visible alarm such as may be remote transceiver 116 or detection unit 112. All wireless links can be bi-directional for software and data transmission and message delivery confirmation. Base station 114 may also employ one or all of the messaging methods listed above for notifying seizures. The base station 114 or the detection unit 112 can be provided with a “release warning” button to end the incident warning.

  In some embodiments, the transceiver can be additionally mounted in a furniture unit or other structure, such as an environmental unit or object. If the detection unit 112 is sufficiently close to the transceiver, such transceiver can transmit data to the base station. Thus, the base station 114 can recognize that the information has been received from the transducer and thus from the associated environmental unit. In some embodiments, the base station 114 may specify a particular template file (eg, including thresholds and other data described later herein) depending on whether it is receiving a signal from a particular transceiver. Can be selected. Thus, for example, when receiving information from the detector 112 and from a transducer associated with the bed or crib, the base station 114 may, for example, wear clothes that an individual normally wears during exercise or, for example, a patient brushes their teeth. Data can be handled differently than when receiving data from a transducer associated with another environmental unit, such as an item near the user's washbasin at the location. More generally, in some embodiments, the surveillance system may be composed of one or more elements that have global positioning (GPS) capability and use location information. , One or more routines available in the detection algorithm may be adjusted. For example, GPS performance may be included with or within one or more microelectromechanical sensor elements included in the detection unit 112.

  The embodiment of FIG. 9 can be configured to have minimal invasiveness for use during sleep or to inhibit daily activities only marginally, such as one or two minimum Requires only a few electrodes, no head electrode, can detect seizures with motor signs, alerts to the presence of seizures at one or more local and / or remote sites Can be cheap enough for home use.

  FIG. 10 illustrates one embodiment of the detection unit 112 or detector. The detection unit 112 can include an EMG electrode 122 and, in some embodiments, can also include an ECG electrode 124. The detection unit 112 can further include an amplifier having a lead-off detector 126. In some embodiments, the one or more lead-off detectors indicate whether the electrode is in physical contact with the human body or too far away from the human body to indicate muscle activity, body temperature, brain activity, Alternatively, a signal can be provided that detects other patient events. The detection unit 112 may further include one or more elements 128 configured to detect the position and / or orientation of the detection unit 112, such as a solid-state microelectromechanical (MEMS) structure. . For example, element 128 may include one or more micromechanical inertial sensors, such as one that may include one or more gyroscopes, accelerometers, magnetometers, or combinations thereof.

  The detection unit 112 may further include a temperature sensor 130 that senses the person's body temperature and one or more orientation or position sensitive elements 128. Other sensors (not shown) such as accelerometers, microphones, oximeters may also be included in the detection unit. Signals from electrodes 122 and 124, temperature sensor 130, orientation and / or position sensor 128, and other sensors can be provided to multiplexer 132. Multiplexer 132 can be part of detection unit 112 or can be part of base station 114 if detection unit 112 is not a smart sensor. The signal can then be communicated from multiplexer 132 to one or more analog-to-digital converters 134. The analog-to-digital converter can be part of the detection unit 112 or can be part of the base station 114. The signal can then be communicated to one or more microprocessors 136 for processing and analysis disclosed herein. Microprocessor 136 can be part of detection unit 112 or can be part of base station 114. The detection unit 112 and / or the base station 114 may further include an appropriate amount of memory. Microprocessor 136 can communicate signal data and other information using transceiver 138. Communication by and between components of detection unit 112 and / or base station 114 can be wired or wireless communication.

  Of course, the exemplary detection unit of FIG. 10 can be configured differently. Many of the detector components of FIG. 10 may be located in the base station 114 rather than the detection unit 112. For example, the detection unit may simply comprise an EMG electrode 122 that is in wireless communication with the base station 114. In such an embodiment, A-D conversion and signal processing may be performed at the base station 114. Multiplexing can also be performed at the base station 114 if an ECG electrode 124 is included.

  In another example, the detection unit 112 of FIG. 10 includes one or more of an EMG electrode 122, an ECG electrode 124, and a temperature sensor 130 for wired or wireless communication with a small belt-mounted transceiver portion. An electrode portion can be provided. The transceiver portion may include a multiplexer 132, an A-D converter 134, a microprocessor 136, a transceiver, and other components such as memory and I / O devices (eg, alarm release buttons and visual displays).

  FIG. 11 illustrates various communication means such as one or more microprocessors 140, a power supply 142, a backup power supply 144, one or more I / O devices 146, and an Ethernet connection 148 and transceiver 150. 1 illustrates one embodiment of a base station 114 that may include: The base station 114 has a higher processing and storage capability than the detection unit 112 to review the EMG signal in real time as the caregiver is received from the detection unit 112 or to review the historical EMG signal from the memory. Can include a larger electronic display for display of the EMG signal graph. Base station 114 can process EMG signals and other data received from detection unit 112. If the base station 114 determines that a seizure may have occurred, a warning can be sent to the caregiver via the transceiver 150.

  Various devices of the apparatus of FIGS. 9 to 11 can communicate with each other via wired or wireless communication. System 100 comprises a client-server or other architecture and can communicate over network 115. Of course, the system 100 may comprise more than one server and / or client. In other embodiments, the system 100 may comprise a peer-to-peer architecture, or other type of network architecture, such as any combination or combination thereof.

  In general, the device of the seizure detection system can be of any suitable type and configuration that achieves one or more of the methods and goals disclosed herein. For example, the server may comprise one or more other computers or programs or one or more computers or programs that respond to commands or requests from clients. A client device may comprise one or more other computers or programs or one or more computers or programs that issue commands or requests for services provided by a server. The various devices in FIG. 9 can be servers or clients depending on functions and configurations. Servers and / or clients include, for example, mainframe computers, desktop computers, PDAs, smartphones (Apple iPhone ™), Motorola's Atrix ™ 4G, Motorola ( Motorola's Droid (TM), Samsung's Galaxy S (TM), Samsung's Galaxy Note (TM), and Research In Motion Blackberry ™ devices, etc.), tablets (Sony's Xperia (trade) ), Samsung Galaxy Tab ™, Amazon Kindle ™, etc., netbooks, portable computers, portable media players with network communication capabilities (Microsoft) Zune HD (TM) and Apple's ipod Touch (TM) devices), cameras with network communication functions, smart watches, wearable computers, etc. May be.

  The computer can be any device that can accept input, process the input according to a program, and generate output. The computer can include, for example, a processor, a memory, and a network connection function. Computers can be of various types, such as supercomputers, mainframes, workstations, microcomputers, PDAs, and smartphones, depending on the size, speed, cost, and function of the computer. Computers can be fixed or portable and must be programmed for various functions such as mobile phone, media recording and playback, data transfer, web browsing, data processing, data query, process automation, video conferencing, artificial intelligence, etc. Can do.

  The program can be any sequence of instructions, such as an algorithm, whether it is in a computer-executable format (object code), a human-readable format (source code), or otherwise. Can be included. A program can include or call one or more data structures and variables. The program can be embodied in hardware or software, or a combination thereof. The program can be created using any suitable programming language such as C, C ++, Java (registered trademark), Perl, PHP, Ruby, or SQL. The computer software can include one or more programs and associated data. Examples of computer software include system software (such as operating system software, device drivers, and utilities), middleware (such as web servers, data access software, and enterprise messaging software), application software (such as databases, video games, and media players). , Firmware (such as calculators with device-specific software, keyboards, and mobile phones), and programming tools (such as debuggers, compilers, and text editors).

  The memory may comprise any computer-readable medium on which information is stored temporarily or permanently. Examples of the memory include various RAMs and ROMs such as SRAM, DRAM, Z-RAM, flash, optical disk, magnetic tape, punch card, and EEPROM. The memory can be provided virtualized, such as with RAID technology, to one or more devices and / or geographic locations or their entirety. An I / O device may comprise any hardware that can be used to provide information to a computer and / or receive information from a computer. Exemplary I / O devices include disk drives, keyboards, video display screens, mouse printers, printers, card readers, scanners (such as barcodes, fingerprints, irises, QR codes, and other types of scanners). RFID devices, tape drives, touch screens, cameras, motion sensors, network cards, storage devices, microphones, audio speakers, touch pens and transducers, and related interfaces and drivers.

  Networks are virtually switched, routed, fully connected cellular networks, the Internet, intranets, local area networks (LANs), wide area networks (WANs), metropolitan area networks (MANs) : Metropolitan Area Network), including other types of area networks, cable TV networks, communication satellite networks, telephone networks, public networks, private networks, wired or wireless networks, and any combination thereof and sub-networks thereof it can. The network can use various network devices such as routers, bridges, switches, hubs, repeaters, converters, receivers, proxies, firewalls, translators and the like. The network connection is wired or wireless, and a multiplexer, a network interface card, a modem, an IDSN terminal adapter, a line driver, or the like can be used. The network can include any suitable topology, such as point-to-point, bus, star, tree, mesh, ring, and any combination or hybrid thereof.

  Wireless technology can be used for ISM band devices, WiFi (registered trademark), Bluetooth (Bluetooth (registered trademark)), mobile phone SMS, cellular (CDMA2000, WCDMA (registered trademark), etc.), WiMAX (registered trademark), WLAN, etc. It can take various forms such as person-to-person wireless, person-to-fixed receiving device, person-to-remote alert device using one or more of various wireless technologies.

  Communication within or between computers, I / O devices and network equipment can be accomplished using various protocols. Protocols can include, for example, signaling, error detection and correction, data formatting, and address mapping. For example, the protocol can be provided according to a 7-layer open system interconnection model (OSI model) or a TCP / IP model.

Although the disclosed method and apparatus and their advantages have been described in detail, it will be understood that various changes, substitutions and modifications may be made without departing from the invention as defined by the appended claims. Should. Furthermore, the scope of this application is not intended to be limited to the specific embodiments of the processes, machines, products, compositions, or materials, means, methods, and steps described herein. For example, any feature described for one embodiment may be used in any other embodiment. For example, use of the term “include” should be construed to be the term “comprising”, ie, open-ended. As can be readily appreciated from the present disclosure, existing or later developed processes that achieve substantially the same function or achieve substantially the same results as the corresponding embodiments described herein. , Machines, products, material compositions, means, methods, or steps may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of materials, means, methods, or steps.

Claims (16)

A method of monitoring a patient for an abnormal motor symptom activity pattern indicative of seizure activity, the method comprising:
Collecting an electromyogram signal;
Detecting a plurality of peaks contained in the electromyogram signal;
Removing the peak member from the plurality of peaks when the peak member does not meet one or more first qualification thresholds;
Determining a remaining group of peaks remaining after removal of the peak member;
Constructing a plurality of test sub-groups comprising a plurality of peaks, which can be constructed from the remaining group comprising the plurality of peaks;
One or more aggregate qualifications of group members in the plurality of test sub-groups of the plurality of peaks to determine one or more qualified groups of the plurality of peaks Identifying group members that meet the threshold;
Including methods. The one or more aggregate eligibility thresholds are from a plurality of peaks indicative of one or more patterns of motor symptom activity selected from the group consisting of clonic seizure activity and non-epileptic psychogenic seizure activity. The method of claim 1, wherein the method is configured to identify qualified groups. To determine the overall level of qualifying peak activity when multiple qualified groups are identified in one or more qualified groups of said multiple peaks The method of claim 1, further comprising combining groups in one or more qualified groups of the plurality of peaks. Determining the overall level of qualifying peak activity includes determining the number of qualified peaks that are at least one member of one or more qualified groups of the plurality of peaks. 4. The method of claim 3, comprising determining. The method of claim 1, wherein the plurality of test sub-groups comprising a plurality of peaks comprises a combination of a subset of successive peaks in a time-stamped list created from the remaining group comprising the plurality of peaks. The plurality of test subgroups order a time-stamped group of the remaining group of the plurality of peaks, and a consecutive group including some preselected number of peaks of the time-stamped group The method of claim 1, created by selecting. The method of claim 1, wherein the plurality of test subgroups comprising a plurality of peaks comprises a group comprising a plurality of peaks that are greater than some minimum number of peaks. 8. The method of claim 7, wherein the minimum number of peaks is from about 4 peaks to about 8 peaks. The method of claim 1, wherein the plurality of test sub-groups comprising the plurality of peaks comprises a group of non-continuous peaks that can be constructed from the remaining group comprising the plurality of peaks. In a method of organizing electromyogram data for inclusion in a searchable database of seizure data, the method comprises:
Monitoring the patient for seizure activity by collecting electromyographic signals over a monitoring period using one or more electromyographic electrodes;
Processing the electromyogram signal to identify peak data in one or more time windows of a collection included in the monitoring period;
Building a plurality of test subgroups comprising a plurality of peaks from the peak data;
Identifying a portion of the peak data in which a test subgroup of peaks is constructed that meets one or more criteria of one or more seizure patterns;
Storing the peak data along with information about one or more seizure patterns to which the peak data was adapted;
The caregiver has an interface configured to allow a caregiver to find stored peak data by searching for instances of data that match one or more of the one or more seizure patterns. Providing steps to,
Including methods. The method of claim 10, wherein the plurality of test sub-groups comprising the plurality of peaks comprises a combination of non-contiguous peak subsets in the peak data. A device for detecting abnormal motor muscle symptoms, the device comprising:
One or more electromyographic electrodes configured to provide an electromyographic signal indicative of athletic muscle activity;
Receiving the electromyogram signal to identify a plurality of peaks in one or more time windows of the collection included during the monitoring period and calculating a peak characteristic value of the peak; A processor configured to process
The processor is configured to compare the peak characteristic value against a qualification threshold; and the qualification threshold is at least one qualification threshold associated with an individual peak characteristic; Including at least one aggregate qualification threshold associated with a plurality of peak characteristics;
The processor is configured to qualify a peak based on whether the peak meets the qualification threshold;
The processor is configured to determine a qualifying peak activity level and, if so, compare an activity threshold level identifying the presence of abnormal motor muscle activity with the level. And a device comprising: The apparatus of claim 12, wherein the processor is further configured to send an alarm to a caregiver when the abnormal athletic muscle activity is identified. The apparatus of claim 12, wherein the processor is further configured to send the peak data to a searchable database. The processor does not meet the at least one qualification threshold associated with individual peak characteristics before comparing the characteristic values of the remaining group of peaks with the at least one aggregate qualification threshold. The apparatus of claim 12, further configured to remove a peak. The processor determines a plurality of aggregate peak characteristic values for a plurality of test groupings constructed from the plurality of peaks, and determines whether any of the plurality of test groupings satisfies the at least one aggregate qualification threshold. The apparatus of claim 12, further configured to determine.