CN115813351A - Tooth grinding action detection method and system - Google Patents
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Abstract
The invention provides a method and a system for detecting a tooth grinding action, wherein the method comprises the steps of acquiring first facial electromyographic data when a detection object executes a preset action; determining individual baseline intensity according to the first facial electromyography data; acquiring second facial electromyographic data of a detection object in a monitoring period; determining a bruxism event from the second facial electromyography data and the individual baseline intensity. According to the molar motion detection scheme provided by the invention, before the detection is started, the individual baseline intensity is determined based on the electromyographic data of a detection object when specific masseter activity is executed, the electromyographic threshold suitable for the detection object can be set through the individual baseline intensity, and the electromyographic data during the monitoring period is identified on the basis, so that the molar motion can be identified more accurately, and the influence of the individual difference of the detection object on the detection result is avoided.
Description
Technical Field
The invention relates to the field of medical equipment, in particular to a method and a system for detecting a tooth grinding action.
Background
Bruxism is a common and frequently occurring condition in the stomatology department, the most common type of which is the occurrence of a grinding action after the patient falls asleep at night. The long-term grinding of teeth can cause abnormal abrasion of teeth, and various diseases such as tooth ache, looseness, fracture, inflammation and the like are caused. In order to diagnose and treat bruxism, the physician needs to know the grinding actions that occur throughout the patient's sleep. The facial electromyogram data can be used for detecting molar actions, and a detection object can wear the electromyogram detection device all night, so that the facial electromyogram data in the sleep period can be collected.
When the electromyographic data is analyzed, a corresponding detection standard, particularly a threshold value of the electromyography needs to be set, and when the electromyographic data exceeds the threshold value, the fact that the tooth grinding action occurs at the moment is judged. However, different patients have great individual differences, the same threshold cannot be adapted to all patients, the threshold is difficult to set manually, and when the threshold is not set to be suitable for the current detection object, the accuracy of an analysis result is extremely poor, myoelectric data can only be manually analyzed, and the efficiency is extremely low.
Disclosure of Invention
In view of the above, the present invention provides a method for detecting a tooth grinding action, including: acquiring first facial electromyographic data of a detection object when the detection object executes a preset action; determining individual baseline intensity according to the first facial electromyography data; acquiring second facial electromyography data of a detection object in a monitoring period; determining a bruxism event from the second facial electromyography data and the individual baseline intensity.
Optionally, the first facial electromyography data includes electromyography data acquired when the detection object performs the same preset action multiple times.
Optionally, the preset action comprises a sustainable action and/or a transient action.
Optionally, the preset actions include at least one of forceful biting, swallowing, chewing, coughing, mouth closing to mouth opening and mouth closing to mouth closing, and tooth grinding.
Optionally, when there are a plurality of preset actions, determining the corresponding individual baseline intensity according to the first facial electromyography data of each preset action, respectively.
Optionally, the preset action is to bite the posterior teeth with force, and the duration reaches a preset duration.
Optionally, determining the individual baseline intensity according to the first facial electromyography data specifically includes: determining a plurality of key time points in the first facial electromyography data; calculating the individual baseline intensity using segments of data between the plurality of key time points.
Optionally, the plurality of key time points includes at least a bite action settling time and a bite action destabilizing time.
Optionally, the plurality of key time points further include a start biting time and an end biting time, and the start biting time, the biting action stabilization time, the biting action destabilization time, and the end biting time are sequentially determined according to a change in a time dimension of the first facial electromyogram data; calculating the individual baseline intensity using a data segment between the bite action stabilization time and the bite action destabilization time.
Optionally, the individual baseline intensity is a mean electromyographic power density within a data segment.
Optionally, determining a molar event according to the second facial electromyography data and the individual baseline intensity specifically includes: screening all data segments exceeding a molar threshold value from the second facial electromyographic data, wherein the molar threshold value is calculated based on the individual baseline intensity; identifying at least two types of molars incident data according to the time length of each screened data segment, wherein the types comprise a sustained attack and a phasic attack, and the length of the molars incident data segment of the sustained attack is larger than that of the molars incident data segment of the phasic attack.
Optionally, identifying at least two types of data segments of the molar event according to the time length of each screened data segment, further comprising: respectively judging the duration t of the data segments aiming at each data segment in all the data segments 2 -t 1 Whether or not t is satisfied tonic ＞t 2 -t 1 ≥ t phasic Or t is 2 -t 1 ≥t tonic (ii) a For satisfying t tonic ＞t 2 -t 1 ≥ t phasic Is recorded as a molar event data segment of a phasic attack for satisfying t 2 -t 1 ≥t tonic The data segment of (a) is recorded as a sustained onset molars event data segment.
Optionally, after identifying at least two types of data segments of the molar event according to the time length of each of the screened data segments, the method further includes: obtaining a molar event data segment according to the identified phasic attack type, and judging whether the time interval of every two adjacent molar event data segments is lower than an interval threshold value or not; when the time interval of two adjacent segments of the molar event data is below an interval threshold, the two adjacent segments of the molar event data are merged.
Optionally, iteratively performing the combined process until the time interval of all every two adjacent molars event data segments is above an interval threshold; after the merging process, the method further comprises the following steps: acquiring the number of the combined molar event data segments aiming at each molar event data segment obtained by combining; and judging the combination result with the combination number larger than the number threshold value as the phasic episode event.
Optionally, before acquiring the first facial electromyography data when the detection object performs the preset action, the method further includes: and guiding and indicating the detection object to execute a preset action.
Optionally, the guiding and instructing the detection object to perform the preset action specifically includes: prompting the action content with voice and/or images, as well as duration and/or number of repetitions.
The present invention also provides a system for detecting a bruxism event, comprising: the wearable recording instrument is suitable for being worn on the face of a person and used for collecting electromyographic data, and the host machine can acquire the electromyographic data through the recording instrument and used for executing the tooth grinding action detection method.
According to the method and the system for detecting the tooth grinding action, provided by the embodiment of the invention, before the detection is started, the individual baseline intensity is determined based on the electromyographic data of the detection object when the specific clenching muscle activity is executed, the electromyographic threshold value suitable for the detection object can be set through the individual baseline intensity, and the electromyographic data during the monitoring period is identified on the basis, so that the tooth grinding action can be identified more accurately, and the influence of the individual difference of the detection object on the detection result is avoided.
The alternative provided by the invention can distinguish two different bruxism events, particularly can accurately identify the phasic attack event, and the indexes of the frequency, the occurrence time and the like of the two events play a better auxiliary role in the diagnosis and treatment of bruxism.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic view of a bruxism event detection system;
FIG. 2 is a flow chart of a method of detecting a tooth grinding action;
FIG. 3 is a schematic diagram of a process for detecting a bruxism event;
FIG. 4 is a diagram illustrating electromyographic data for a predetermined action;
FIG. 5 is a segment of electromyographic data containing two types of episodes.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 shows a molar event detection system, which includes a host 1 and a wearable recorder 2, wherein the wearable recorder 2 is suitable for being worn on a human face for collecting electromyographic data, and the host 1 can acquire the electromyographic data through the wearable recorder 2. The host 1 may be equipped with one or more wearable recorders 2, two wearable recorders 2 being shown in fig. 1. Wearable record appearance 2 can take out from the charging groove of host computer 1, pegs graft flexible flesh electricity and gathers the electrode, wears in the masseter region of facial both sides.
Under the operating condition, the host 1 and the wearable recorder 2 communicate in a wireless mode, and control instructions and data collected by the recorder are transmitted. When the host 1 is turned off, the wearable recorder 2 is in a dormant state; when the host 1 is powered on and started, the host 1 and the wearable recorders 2 automatically complete wireless connection, a user can disconnect one or more wearable recorders 2 through the operation of a software system of the host 1 and convert the wearable recorders into a dormant state, and only the wearable recorders 2 which are not dormant are used; when the host 1 is turned off, all the wearable recorders 2 are disconnected and turned to the sleep state.
The data collected by the wearable recorder 2 can also be transmitted to a general purpose electronic device, such as a smartphone. The electronic device pre-loaded with the supporting software can realize the whole software functions equivalent to those of the host 1.
As shown in fig. 2, an embodiment of the present invention provides a method for detecting a tooth grinding action, which may be executed by the host 1 or a general-purpose electronic device, and includes the following operations:
s1, acquiring first facial electromyographic data of a detection object when the detection object executes a preset action. The preset action may be one or more actions that cause the masseter to produce significant movement.
By way of example, the predetermined action includes, but is not limited to, forceful biting, swallowing, chewing, coughing, closing the mouth to open the mouth and then restoring the mouth, and grinding the teeth. Wherein swallowing, chewing, coughing and tooth grinding belong to instantaneous actions, and forceful tooth biting and mouth closing are converted into mouth opening and mouth closing again belong to sustainable actions. It is understood that a sustainable action means that at least one segment of the action may be controllably persisted for a certain time by the subject, such as a tooth bite for several seconds, a mouth opening for several seconds, etc.
It should be noted that any preset action in this step is an action that is autonomously and controllably performed by the test subject, rather than an action that happens to be performed involuntarily during sleep.
In a preferred embodiment, an operation of guiding and instructing the detection object to perform a preset action may be performed before step S1, such as prompting the action content, the duration and/or the number of repetitions in voice and/or image by a host or a smartphone.
For example, when the detection object finishes wearing the recorder and starts the instrument host, the recorder and the host are automatically and wirelessly connected. The host computer sends out a command with a set sequence, and guides the detection object to actively execute the following preset actions according to the content of the command:
1. biting the posterior teeth with the maximum force for more than 3 seconds, and repeating the steps twice;
2. swallowing the saliva and repeating twice;
3. cough, repeated twice;
4. closing the mouth, opening the mouth to the maximum, keeping the mouth for more than two seconds, closing the mouth, and repeating the steps for two times;
after the instructions of the four times are completed, the user is prompted to enter the next starting waiting stage, and the user keeps the equipment and the electrodes stably attached to the face.
The electromyographic data of the 2-time event 1, the 2-time event 2, the 2-time event 3 and the 2-time event 4 are respectively obtained according to the sampling frequency
After filtering by the designed band-pass filter, myoelectric information in the main frequency band is obtained.
And if the detection object does not execute the related action as required or the acquired data is interfered and subsequent calculation cannot be carried out, acquiring again.
And S2, determining the individual baseline intensity according to the first facial electromyography data. The individual baseline intensity in the scheme refers to the highest myoelectric level (the maximum autonomic contraction value, namely a signal at the time of 100% muscle activation) when the test object actively performs the masseter muscle activity, the specific calculation modes are various, and the calculation modes adopted for different preset actions are different. For example, for an embodiment with only one preset action, corresponding key points or key segments can be extracted from electromyographic data according to the characteristics of the action, and the average electromyography or peak value and the like in the key points or key segments are calculated to be used as the individual baseline intensity; for embodiments with multiple preset actions, the corresponding individual baseline intensity may be determined according to the first facial electromyography data of each preset action, and the final individual baseline intensity may be further synthesized according to the results, such as averaging, and the like.
And S3, acquiring second facial electromyographic data of the detection object in the monitoring period. Generally, the scheme is used for detecting the non-autonomous molars in the sleep period, and the steps S1 to S2 can be carried out at any time except the sleep period. If the detection object does not immediately enter the night sleep state after the personal baseline collection is finished, the waiting starting time T can be set after the baseline collection and before the beginning of the monitoring of the molars at night wait (if the test subject is asleep and performing a personal baseline acquisition, T may be set wait = 0). And during the waiting starting period, the recorder enters a dormant state and is disconnected from the host, and the recorder is connected with the host again and enters a continuous monitoring state until the waiting starting time is over. There are various ways for the system to enter the continuous monitoring state from the wait for start state, for example: presetting T in software system wait The device is automatically triggered after the clock timing of the device is finished; t is not preset in the software system wait The user sends out an instruction through a software system to trigger; presetting T in software system wait When the timing is not finished, the user issues an instruction through the software system to trigger, the timing is stopped, and the continuous monitoring state is entered from the waiting starting state.
During monitoring, wearable recorder and host computer continuously collect and transmit electromyographic data, during monitoringLong T monitor The control method comprises the following steps: presetting T in software system monitor After the self clock timing of the equipment is finished, the monitoring is automatically finished; t is not preset in the software system monitor The user sends an instruction to finish monitoring through the software system; presetting T in software system monitor And when the timing is not finished, the user gives an instruction to finish the monitoring through the software system.
And S4, determining a molar event according to the second facial electromyographic data and the individual baseline intensity. This operation may be performed automatically after the monitoring period is over, or may be initiated by the user. For example, a physician may operate in a software system to perform data analysis and generate statistical reports. The step does not necessarily exist after each monitoring, can be uniformly performed after multiple times of monitoring, and can also be performed for certain historical monitoring data independently.
A molar threshold suitable for the detected object can be determined based on the individual baseline intensity, facial electromyographic data in the monitoring period is compared with the molar threshold, and a data segment higher than the molar threshold is determined as that the monitored object generates a molar action, namely a molar event is generated. It is of course also possible to combine multiple molars from the time dimension, e.g. for two molars very close in time, one molars.
Therefore, indexes such as the tooth grinding event of the detected object at which time points, the total times and the like in the whole monitoring period can be obtained.
Fig. 3 shows an exemplary process of detecting a bruxism event, wherein T1 to T3 refer to the time when a subject performs a corresponding preset action and is myoelectric collected, and the exemplary step of determining the baseline intensity of an individual uses a plurality of preset actions and repeats the preset actions a plurality of times. Step S4 is not labeled as it may be performed at any time after step S3.
According to the method for detecting the molar action, provided by the embodiment of the invention, before the detection is started, the individual baseline intensity is determined based on the electromyographic data of the detection object when the specific biting muscle activity is executed, the electromyographic threshold value suitable for the detection object can be set through the individual baseline intensity, and the electromyographic data during the monitoring period is identified on the basis, so that the molar action can be identified more accurately, and the influence of the individual difference of the detection object on the detection result is avoided.
In one embodiment, the predetermined action is to bite the posterior teeth with a force for a predetermined duration, specifically, the maximum force may be more than 3 seconds. Under the prompt and guidance of the host, the detected object executes the action, and the collected facial myoelectric data is shown in fig. 4, wherein the abscissa is time and the ordinate is myoelectric voltage value.
Further, determining the individual baseline intensity from the first facial electromyography data includes:
a plurality of key time points are determined in the first facial electromyography data. Individual baseline intensities are calculated using segments of data between the plurality of key time points. The key time points of different preset actions are different, taking the action of biting the posterior teeth forcefully as an example, the key time points at least comprise the time for stabilizing the biting action and the time for destabilizing the biting action. For other actions, corresponding key time points are defined according to the masseter muscle activity characteristics of the actions, such as chewing actions, and the key time points comprise a plurality of pairs of muscle activity starting time and ending time in a periodic manner; for example, the mouth is closed, the mouth is opened, and then the mouth is closed again, and the key time points comprise the myoelectric activity starting time of the mouth opening action and the myoelectric activity ending time after the mouth is closed again.
As a preferred embodiment, the plurality of key time points of the action of biting the back teeth hard further comprise the time t of starting biting r And ending the biting time t q Sequentially determining the time t for starting to bite according to the change of facial electromyography data in the time dimension r And the tooth-biting action stabilization time t b Time t of instability of tooth-biting action s And ending the biting time t q . In the embodiment shown in FIG. 4, the time t is stabilized according to the biting action b Time t until tooth biting action is unstable s The electromyography data between the individual baseline intensity is calculated, preferably the mean electromyography power density of the data segment is calculated.
More specifically, firstly, the electromyographic data in a resting state before the start of action is utilized to calculate the resting amplitude of a detection object and the device itself after the device is worn, and then the key time point can be identified through a pre-established statistical model based on the electromyographic data after the resting amplitude is analyzed frame by frame. The statistical model is a model that clearly defines the magnitude relationship between various critical time points, and may be obtained by statistics using a large amount of experimental data or may be set based on an empirical value. Regarding the mean myoelectric power density, a person skilled in the art should know how to calculate the mean myoelectric power density, knowing the frequency information, the target time period, and the myoelectric voltage value at each time.
In one embodiment, the present scheme distinguishes between two types of bruxism events, a sustained episode and a phasic episode. A persistent (tonic) episode defined as a single masseter activity in which the electromyographic data exceeds a threshold (the threshold is set according to the individual baseline intensity) and which lasts for a longer period of time (e.g., 1 to 3 seconds); phasic seizures are defined as at least N consecutive myoelectrical activities of short duration (e.g. 0.1 to 0.5 seconds), short interval, with myoelectrical data exceeding a threshold.
The operation of determining a molar event according to the second facial electromyography data and the individual baseline intensity in this embodiment specifically includes:
screening all data segments exceeding a molar threshold value from the second facial electromyographic data, wherein the molar threshold value is calculated based on the individual baseline intensity, and the molar threshold value can be a certain percentage of the individual baseline intensity and can be expressed as V base ×w%，V base The individual baseline strength is shown, and the value of w is 20 to 40.
Identifying at least two types of molars event data segments according to the time length of each screened data segment, wherein the types comprise a sustained attack and a phasic attack, and the length of the molars event data segment of the sustained attack is larger than that of the molars event data segment of the phasic attack.
Two time thresholds t may be set tonic And t phasic To distinguish between two types of bruxism events, t tonic ＞t phasic . For each of all data segmentsA data segment, the duration t of the data segment is judged respectively 2 -t 1 Whether or not t is satisfied tonic ＞t 2 -t 1 ≥ t phasic Or t is 2 -t 1 ≥t tonic (ii) a Wherein t is 2 Indicating the point in time, t, corresponding to the end of the data segment 1 Indicating the point in time corresponding to the start position of the data segment.
For satisfying t tonic ＞t 2 -t 1 ≥ t phasic Is recorded as a molar event data segment of a phasic attack for satisfying t 2 -t 1 ≥t tonic Is recorded as a sustained onset molars event data segment.
Figure 5 shows typical electromyographic data segments for two events, according to which a sustained episode of molar event data segment, and a plurality of phased episodes of molar event data segments, can be identified. Aiming at the data segment of the identified sustained attack type, the data segment can be directly judged as a one-time sustained attack; for the data segment of the phasic seizure type, the number, interval, etc. can be counted and directly used as the output result, or can be processed again.
As an alternative embodiment, the following processing may be further performed:
for the identified molar event data segments of the phasic episode type, judging whether the time interval of every two adjacent molar event data segments is lower than an interval threshold value; when the time interval of two adjacent segments of the molar event data is below an interval threshold, the two adjacent segments of the molar event data are merged. It should be noted that, in the present scheme, two data segments are merged, and the two end data are not fused to form one data segment, but the two data segments are regarded as one data segment.
Specifically, first, two initial molars event data segments (referred to as phasic data segments) of phasic onset are extracted, and the time interval Δ between the two phasic data segments is determinedtWhether or not less than the interval thresholdt interval . If yes, two phasic data segments belong to one phasic attack, and the two phasic data segments are combined into one phasic actionRecording, and then proceeding with delta with the next phasic data segmenttJudging; if not, then the two phasic data segments do not belong to the same phasic episode, and the second phasic data segment is continued to be delta with the next phasic data segmenttAnd (6) judging. And so on until the delta of the last two phasic data segments is finishedtAnd judging to obtain the merged phasic action record. The merging process is iteratively performed, and the time interval of the adjacent phasic data segments after merging is higher than an interval threshold.
On the basis, the following processing can be further executed:
and acquiring the number of the combined molar event data segments aiming at each molar event data segment obtained by combining. In the above-mentioned phasic data segment merging process, the merging number of each new phasic data segment is recorded. And judging the combination result with the combination number larger than the number threshold value as the phasic episode event. Taking the electromyogram data shown in fig. 5 as an example, 5 pieces of phasic data can be initially screened out, and after merging, they are merged into 1 piece of phasic data, the number of mergers being 5. Assuming the number threshold is 3, there are 1 episode of phasic onset in the example shown in fig. 5; assuming there are combined results with a combined number less than 3, the combined result does not belong to a phasic episode.
The preferred embodiment provided by the invention can distinguish two different bruxism events, particularly can accurately identify the phasic attack event, and the indexes of the frequency, the occurrence time and the like of the two events play a better auxiliary role in diagnosing and treating bruxism.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (17)
1. A method of detecting a tooth grinding action, comprising:
acquiring first facial electromyographic data of a detection object when the detection object executes a preset action;
determining individual baseline intensity according to the first facial electromyography data;
acquiring second facial electromyographic data of a detection object in a monitoring period;
determining a bruxism event from the second facial electromyography data and the individual baseline intensity.
2. The method according to claim 1, characterized in that said first facial electromyographic data comprises electromyographic data collected while the test subject performed the same predetermined action a plurality of times.
3. The method of claim 1, wherein the preset action comprises a sustainable action and/or a transient action.
4. The method of claim 3, wherein the predetermined action comprises at least one of forceful biting, swallowing, chewing, coughing, mouth closing to mouth opening and mouth closing to mouth closing and tooth grinding.
5. The method according to claim 4, wherein when there are a plurality of said preset actions, determining the corresponding individual baseline intensity according to the first facial electromyography data of each of said preset actions.
6. The method of claim 1, wherein the predetermined action is biting the posterior teeth hard for a predetermined duration.
7. The method according to claim 6, wherein determining the individual baseline intensity from the first facial electromyography data specifically comprises:
determining a plurality of key time points in the first facial electromyography data;
calculating the individual baseline intensity using segments of data between the plurality of key time points.
8. The method of claim 7, wherein the plurality of key time points includes at least a bite action settling time and a bite action destabilizing time.
9. The method according to claim 8, wherein the plurality of key time points further include a start biting time and an end biting time, the start biting time, the biting action stabilization time, the biting action destabilization time, and the end biting time being sequentially determined according to a change in a time dimension of the first facial electromyogram data; calculating the individual baseline intensity using a data segment between the bite action stabilization time and the bite action destabilization time.
10. A method according to any of claims 1-9, wherein the individual baseline intensity is the mean myoelectric power density within one data segment.
11. The method according to claim 1, wherein determining a bruxism event from the second facial electromyography data and the individual baseline intensity comprises:
screening all data segments exceeding a molar threshold value from the second facial electromyographic data, wherein the molar threshold value is calculated based on the individual baseline intensity;
identifying at least two types of molars incident data according to the time length of each screened data segment, wherein the types comprise a sustained attack and a phasic attack, and the length of the molars incident data segment of the sustained attack is larger than that of the molars incident data segment of the phasic attack.
12. The method of claim 11, wherein at least two types of molars event data segments are identified based on the time length of each of the screened data segments, further comprising:
respectively judging the duration t of the data segments aiming at each data segment in all the data segments 2 -t 1 Whether or not t is satisfied tonic ＞t 2 -t 1 ≥ t phasic Or t is 2 -t 1 ≥t tonic ；
For satisfying t tonic ＞t 2 -t 1 ≥ t phasic Is recorded as a molar event data segment of a phasic attack for satisfying t 2 -t 1 ≥t tonic Is recorded as a sustained onset molars event data segment.
13. The method of claim 11, further comprising, after identifying at least two types of molars event data segments based on the time length of each of the screened data segments:
obtaining a molar event data segment according to the identified phasic attack type, and judging whether the time interval of every two adjacent molar event data segments is lower than an interval threshold value or not;
when the time interval of two adjacent segments of the molar event data is below an interval threshold, the two adjacent segments of the molar event data are merged.
14. The method of claim 13, wherein the process of combining is performed iteratively until the time interval of all of every two adjacent molars event data segments is above an interval threshold;
after the merging process, the method further comprises the following steps:
acquiring the number of the combined molar event data segments aiming at each molar event data segment obtained by combining;
and judging the combination result with the combination number larger than the number threshold value as the phasic episode event.
15. The method according to claim 1, before acquiring the first facial electromyography data when the detection object performs the preset action, further comprising:
and guiding and indicating the detection object to execute a preset action.
16. The method according to claim 15, wherein guiding and instructing the detection object to perform the preset action specifically comprises: prompting the action content with voice and/or images, as well as duration and/or number of repetitions.
17. A system for detecting a bruxism event, comprising: the wearable recording instrument is suitable for being worn on the face of a person and used for collecting electromyographic data, and the host machine acquires the electromyographic data through the wearable recording instrument and is used for executing the tooth grinding action detection method according to any one of claims 1 to 16.
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