CN115844337A - Molar event detection system and molar data processing method - Google Patents
Molar event detection system and molar data processing method Download PDFInfo
- Publication number
- CN115844337A CN115844337A CN202310135252.4A CN202310135252A CN115844337A CN 115844337 A CN115844337 A CN 115844337A CN 202310135252 A CN202310135252 A CN 202310135252A CN 115844337 A CN115844337 A CN 115844337A
- Authority
- CN
- China
- Prior art keywords
- data
- suspected
- event
- molar
- bruxism
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 15
- 238000003672 processing method Methods 0.000 title claims abstract description 8
- 230000001815 facial effect Effects 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 28
- 230000003183 myoelectrical effect Effects 0.000 claims abstract description 9
- 238000002567 electromyography Methods 0.000 claims abstract description 7
- 206010006514 bruxism Diseases 0.000 claims description 53
- 230000001133 acceleration Effects 0.000 claims description 30
- 238000012545 processing Methods 0.000 claims description 20
- 230000001256 tonic effect Effects 0.000 claims description 17
- 230000002459 sustained effect Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 3
- 230000009471 action Effects 0.000 description 21
- 238000010586 diagram Methods 0.000 description 11
- 238000004590 computer program Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 7
- 239000000853 adhesive Substances 0.000 description 6
- 230000001070 adhesive effect Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 102220621241 Proline-rich membrane anchor 1_S32A_mutation Human genes 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 102220213553 rs1026192345 Human genes 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 102220198146 rs1057519886 Human genes 0.000 description 2
- 206010061218 Inflammation Diseases 0.000 description 1
- 102220471486 Protein MON2 homolog_S36C_mutation Human genes 0.000 description 1
- 206010044038 Tooth erosion Diseases 0.000 description 1
- 239000002390 adhesive tape Substances 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000004886 head movement Effects 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 102220198036 rs121913400 Human genes 0.000 description 1
- 102220293834 rs1553603207 Human genes 0.000 description 1
- 102220062469 rs786203185 Human genes 0.000 description 1
- 102220064657 rs786205565 Human genes 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
- 208000004371 toothache Diseases 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
- A61B5/4542—Evaluating the mouth, e.g. the jaw
- A61B5/4557—Evaluating bruxism
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/251—Means for maintaining electrode contact with the body
- A61B5/256—Wearable electrodes, e.g. having straps or bands
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/296—Bioelectric electrodes therefor specially adapted for particular uses for electromyography [EMG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
- A61B5/394—Electromyography [EMG] specially adapted for electroglottography or electropalatography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a molar event detection system and a molar data processing method, wherein the method comprises the following steps: acquiring facial electromyographic data and auxiliary data acquired by a wearable recorder worn on the face of a detection object; determining a suspected molar event according to the facial electromyography data and a molar threshold; verifying the suspected molars event using the assistance data to determine a molars event. Auxiliary data are collected while facial myoelectric data are collected through a wearable recorder, when a molar event is analyzed, suspected molar events are preliminarily screened out by utilizing the facial myoelectric data and a molar threshold value, the suspected molar events are analyzed one by combining the auxiliary data, and the actual molar event is verified, so that artifacts in the myoelectric data can be removed, and the accuracy of molar event identification is improved.
Description
Technical Field
The invention relates to the field of medical equipment, in particular to a molar incident detection system and a molar data processing method.
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 analyzing electromyographic data, generally, a data segment which may be a molar action is screened out by comparing facial electromyographic data with a set threshold, but in practical application, various conditions such as turning over, facial actions and the like may occur to a detection object during sleep, and the non-molar action may cause the facial electromyographic data to obviously fluctuate, so that misjudgment is easy to occur.
Disclosure of Invention
In view of the above, the present invention provides a method for processing molar data, including: acquiring facial electromyographic data and auxiliary data acquired by a wearable recorder worn on the face of a detection object; determining a suspected molar event according to the facial electromyography data and a molar threshold; verifying the suspected molars event using the assistance data to determine a molars event.
Optionally, the assistance data comprises acceleration data and/or sound data.
Optionally, verifying the suspected molar event using the auxiliary data to determine a molar event, further comprising: determining time information of the suspected bruxism event; extracting acceleration data corresponding to the time information; and determining whether the suspected molar event is a molar event according to the extracted variation of the acceleration data.
Optionally, verifying the suspected molar event using the auxiliary data to determine a molar event, further comprising: determining time information of the suspected bruxism event; extracting sound data corresponding to the time information; and determining whether the suspected bruxism event is a bruxism event according to the extracted characteristics of the sound data.
Optionally, verifying the suspected molar event using the auxiliary data to determine a molar event, further comprising: determining time information of the suspected bruxism event; extracting acceleration data corresponding to the time information; judging whether the variation of the extracted acceleration data is larger than a threshold value; extracting sound data corresponding to the time information when the variation of the extracted acceleration data is greater than a threshold value; judging whether the characteristics of the sound data accord with the characteristics of the teeth grinding sound; when the characteristics of the sound data do not match the bruxism sound characteristics, a non-bruxism event is determined.
Optionally, the time information comprises a start time and an end time of the suspected bruxism event; in the step of verifying the suspected bruxism event with the assistance data to determine a bruxism event, the assistance data between the start time and the end time is extracted.
Optionally, determining a suspected molar event according to the facial electromyography data and a molar threshold, further comprising: screening all data sections exceeding a molar threshold value from the facial electromyographic data; and identifying at least two types of suspected grinding events 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 data segment of the sustained attack is larger than that of the data segment of the phasic attack.
Optionally, at least two types of suspected grinding events are identified according to the time length of each screened data segment, and the method further includes: respectively judging the duration t of the data segment 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 suspected bruxism event of a phasic attack for satisfying t 2 -t 1 ≥t tonic The data field of (a) is recorded as a suspected bruxism event of a sustained attack.
Optionally, after identifying at least two types of suspected grinding events according to the time length of each of the screened data segments, the method further includes: for suspected molars of the identified phasic episode type, judging whether the time interval of every two adjacent suspected molars is lower than an interval threshold value; merging two adjacent suspected molars when the time interval of the two adjacent suspected molars is below an interval threshold.
Optionally, iteratively performing the combined processing until the time interval of all every two adjacent suspected bruxism events is above an interval threshold; after the merging process, the method further comprises the following steps: aiming at each suspected molar event obtained by merging, acquiring the number of the merged suspected molar events; and deleting the merging results of which the merging quantity is smaller than the quantity threshold value.
Accordingly, the present invention provides a molar data processing device comprising: a processor and a memory coupled to the processor; wherein the memory stores instructions executable by the processor, the instructions being executable by the processor to cause the processor to perform the above-described method of molar data processing.
Accordingly, the present invention provides a system for detecting a bruxism event, comprising: the wearable recorder is used for acquiring facial myoelectric data and auxiliary data; the host is used for executing the molar data processing method.
According to the molar event detection system and the molar data processing method provided by the invention, the wearable recorder collects the facial electromyographic data and auxiliary data at the same time, when a molar event is analyzed, the facial electromyographic data and a molar threshold are firstly utilized to preliminarily screen out suspected molar events, then the suspected molar events are analyzed one by combining the auxiliary data, and the actual molar event is verified, so that artifacts in the electromyographic data can be removed, and the accuracy of molar event identification is improved.
Drawings
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 description of the embodiments or the prior art 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 diagram of a circuit architecture of a wearable recorder in an embodiment of the present invention;
FIG. 3 is a connection state diagram of the wearable recorder and the flexible myoelectric recording electrode;
FIG. 4 is a flow chart of a method of processing molar data in an embodiment of the invention;
FIG. 5 is a segment of electromyographic data including two seizure types.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 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 face of a person for collecting electromyographic data and auxiliary data, and the host 1 can obtain the electromyographic data and the auxiliary 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.
Fig. 2 is a circuit architecture diagram of the wearable recorder 2, which includes three sensing modules: the myoelectricity acquisition module is used for directly acquiring original myoelectricity signals by a flexible myoelectricity recording electrode inserted on the recorder and inputting the signals into the recorder, and further performing signal processing such as amplification, filtering, analog-to-digital conversion sampling and the like; the body movement acquisition module acquires the motion information of the head of the user by the accelerometer and is used for judging whether the fluctuation of the electromyographic signals is an artifact introduced by the head movement; and the sound collection module is used for collecting surrounding environment sounds through a microphone, judging whether the fluctuation of the electromyographic signals is accompanied by molar sounds or not and assisting in judging whether the electromyographic signals are generated by the molar teeth or not. The data collected by the three sensing modules are all input into the microcontroller, the microcontroller performs data compression, subpackage and other processing, and the data are further input into the wireless communication module and are transmitted outwards.
The recorder internal circuit is provided with a memory, and data output by the microcontroller can be written into the local memory to be used as data backup while being wirelessly transmitted. If the data transmitted wirelessly is found to be lost, the data in the memory can be derived again by the microcontroller. There are two modes of data export: the microcontroller reads the data in the memory and outputs the data to the wireless communication module for wireless transmission; the recorder is placed in a charging slot of the main machine of the instrument, and data in the memory is transmitted to the main machine 1 through a bottom contact of the recorder and an inner contact of the charging slot.
The wireless communication module has a transceiving function, can receive a control command from the outside (such as the host 1) besides wirelessly transmitting data input by the microcontroller, and can control the wireless connection and disconnection of the recorder, the starting and closing of each sensing module, the reading and writing of the memory and the like after the command is input into the microcontroller.
In addition, the power module of the recorder internal circuit comprises a battery, a power management circuit and a charging circuit, and supplies power to other modules of the recorder.
In other embodiments of the circuit architecture of the recorder, the sound collection module may not be provided, and the sound collection module may be disposed inside the main unit 1 of the recorder to realize the function of collecting the sound of the surrounding environment.
As shown in fig. 3, the wearable recorder 2 needs to be combined with a matched flexible electromyographic recording electrode 3 for use, and the electromyographic recording electrode 3 is composed of a recorder back adhesive 31, a plug interface 32, a skin back adhesive 33 and a flexible circuit 34. The recorder back adhesive 31 is used for adhering and fixing the wearable recorder 2, a release film covers the surface when the wearable recorder is not used, and the release film is torn off to be adhered to the recorder when the wearable recorder is used; the interface 32 can be inserted into the interface 22 on the recorder, and the myoelectric signals collected by the electrodes are input into the internal circuit of the recorder; the skin back adhesive 33 is made of medical adhesive tape, a release film covers the surface when the skin back adhesive is not used, and the release film is torn off to be adhered to the skin of the facial masseter area when the skin back adhesive is used; the flexible circuit 34 is a core structure of the flexible electromyographic recording electrode 3, and is provided with electrode points and leads for collecting and transmitting electromyographic signals.
As shown in fig. 4, an embodiment of the present invention provides a method for processing molar data, which may be executed by the host 1 or the general-purpose electronic device, and includes the following operations:
s1, acquiring facial myoelectric data and auxiliary data acquired by a wearable recorder worn on the face of a detection object. Generally, the scheme is used for detecting non-autonomous tooth grinding events during sleep, and the acquired facial myoelectric data and auxiliary data are data of a detected object during sleep.
The assistance data may be one or both of acceleration data and sound data. After the monitoring is finished, the software system processes the received original data, including digital filtering through a preset filter, and time alignment is carried out on the facial electromyographic data and the auxiliary data according to the time information in the data packet.
And S2, determining a suspected molar event according to the facial electromyogram data and the molar threshold value. And comparing the facial electromyography data in the monitoring period with the molar threshold value, wherein the molar threshold value is a preset value, and the data segment higher than the molar threshold value is judged as that the suspected molar action of the monitored object occurs, namely a suspected molar event is generated. Of course, multiple suspected grinding events may be combined from the time dimension, for example, two suspected grinding events that are very close to each other in time may be combined into one suspected grinding event.
And S3, verifying the suspected molar event by using the auxiliary data to determine the molar event. And analyzing each suspected molar event one by one, wherein each suspected molar event corresponds to one electromyographic data section, each suspected molar event has a start-stop time point, auxiliary data in the start-stop time can be extracted, a longer auxiliary data including the start-stop time point can be extracted in an expanded range, only the auxiliary data near the start time point of the electromyographic data section is extracted, or only the auxiliary data near the end time point of the electromyographic data section is extracted.
In one embodiment, using only acceleration data as assistance data, step S3 specifically comprises:
S31A, determining time information of a suspected molar event, wherein the time information can be one or all of start time and end time;
S32A, extracting acceleration data corresponding to the time information, wherein the acceleration data can be acceleration data in a period of time before and after a starting time point, acceleration data in a period of time before and after an ending time point, and acceleration data between the starting time and the ending time;
and S33A, determining whether the suspected molar event is a molar event according to the extracted acceleration data value. Specifically, the extracted acceleration data is also a section of acceleration data which changes along with time, and the variation in the acceleration data section is compared with a preset threshold, wherein the variation refers to the difference between the acceleration value at a certain moment and the initial acceleration value of the data section; if the acceleration variation of all the time in the time period does not reach the preset threshold value, which indicates that the wearer keeps still in the time period, the myoelectricity fluctuation in the time period is determined not to be caused by the abnormal action of the face of the detected object, so that the occurrence of the tooth grinding event can be confirmed.
In another embodiment, only the sound data is used as the auxiliary data, and the step S3 specifically includes:
S31B, determining time information of the suspected molar event, and referring to the step S31A specifically;
S32B, extracting the sound data corresponding to the time information, which may refer to step S32A;
and S33B, determining whether the suspected bruxism event is a bruxism event according to the characteristics of the extracted sound data. Specifically, the extracted sound data is a sound data segment which is performed over time, and if the detection object has a tooth grinding action in the time segment, a tooth grinding sound can be generated, wherein the sound has the characteristic of being accompanied with the tooth grinding action, is started along with the start of the tooth grinding action, is enhanced along with the enhancement of the tooth grinding action, and finally disappears along with the disappearance of the tooth grinding action, and the sound is obviously different from the sound generated by other actions caused by rubbing a pillow and the like. Referring to fig. 5, in the electromyographic data segment (electromyographic data in a time period) corresponding to the suspected molar event, the electromyographic data has a distinct fluctuation characteristic, namely, the electromyographic data is gradually increased from a lower level and then gradually decreased, so that the sound intensity variation characteristic can be set. If the intensity change of the extracted sound accords with the set sound intensity change characteristic, namely the change characteristic of the electromyographic intensity of the molar action, the detected object has the molar action, and the suspected molar event is indeed the molar event; if the extracted sound intensity change characteristics do not accord with the change characteristics of the electromyographic intensity of the molar action, the fact that the detected object does not have the molar action is indicated, and the facial electromyographic data exceeds a molar threshold value possibly caused by other body actions, so that the suspected molar event is judged to belong to an artifact and not a real molar event.
In the third embodiment, using two data, i.e. acceleration data and sound data, as the auxiliary data, step S3 specifically includes:
S31C, determining time information of the suspected molar event, and referring to the step S31A specifically;
S32C, extracting acceleration data corresponding to the time information, and referring to the step S32A specifically;
and S33C, judging whether the variation of the extracted acceleration data is larger than a threshold value. Specifically, referring to step S33A, when the variation of the extracted acceleration data is greater than the threshold, step S4C is executed, otherwise, the suspected molar event is determined as a molar event (determined to belong to a molar action);
S34C, extracting the sound data corresponding to the time information, which may refer to step S32A;
and S35C, judging whether the characteristics of the sound data accord with the teeth grinding sound characteristics or not. Specifically, referring to step S33B, when the feature of the sound data does not match the tooth grinding sound feature, step S6C is performed, otherwise, the suspected tooth grinding event is determined as a tooth grinding event (determined to belong to a tooth grinding action);
at S36C, it is determined as a non-molar event (artifact).
The present embodiment may be regarded as a combination of the first two embodiments, and the present embodiment may further improve the accuracy of the determination result by comprehensively analyzing the suspected molar event using two types of auxiliary information.
According to the molar data processing method provided by the embodiment of the invention, the wearable recorder collects the facial electromyogram data and auxiliary data at the same time, when a molar event is analyzed, the facial electromyogram data and a molar threshold are firstly utilized to preliminarily screen out suspected molar events, and then the auxiliary data are combined to analyze the suspected molar events one by one so as to verify the actual molar event, so that artifacts in the electromyogram data can be removed, and the accuracy of identification of the molar event is improved.
In an alternative embodiment, the present scheme distinguishes between two types of suspected bruxism events, a sustained episode and a phased episode, respectively. A sustained (tonic) episode defined as one bite activity with electromyographic data exceeding a molar threshold and of longer duration (e.g., 1~3 seconds); phasic episodes are defined as at least N consecutive electromyographic activities with short duration (e.g. 0.1 to 0.5 seconds) and short intervals, with electromyographic data exceeding a molar threshold.
The step S2 specifically includes:
and screening all data sections exceeding the molar threshold value from the facial electromyography data.
And identifying at least two types of suspected bruxism events according to the time length of each screened data segment, wherein the types comprise a sustained attack and a phased attack, and the length of the data segment of the sustained attack is larger than that of the data segment of the phased attack.
Two time thresholds t may be set tonic And t phasic To distinguish between two types of suspected bruxism events, t tonic ＞t phasic . For each of all the data segments, the duration t of the data segment is respectively judged 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 corresponding point in time, t, of 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 suspected bruxism event of a phasic attack for satisfying t 2 -t 1 ≥t tonic The data field of (a) is recorded as a suspected bruxism event of a sustained attack.
Figure 5 shows typical electromyographic data segments for two events, one for a sustained episode being identified, and a plurality of phased episodes for a molar event, according to the procedure described above. Directly judging the data segment of the identified sustained attack type as a suspected sustained attack; for the data segment of the phasic seizure type, the number, interval, etc. may be counted as a result, and further processing may be performed.
As an alternative embodiment, the following processing may be further performed:
for the suspected molars of the identified phasic episode type, judging whether the time interval of every two adjacent suspected molars is lower than an interval threshold value; merging two adjacent suspected molars when the time interval of the two adjacent suspected molars is below an interval threshold. In the scheme, two events are merged, and two data segments are regarded as one event instead of performing fusion calculation on two data segments to form one data segment.
Specifically, first, two initial data segments of suspected phasic episode (referred to simply as phasic data segments) 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, the two phasic data segments are merged into one phasic action record, and then delta is continuously carried out 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 a merged phasic action record. The merging process is executed iteratively, and the time interval of the adjacent phasic data segments after merging is higher than the interval threshold.
On the basis, the following processing can be further executed:
and acquiring the number of the merged events of the molars aiming at each suspected event of the molars obtained by merging. In the phasic data segment merging process, the merging number of each new phasic data segment is recorded. And judging the combined result of which the combined number is greater than the number threshold value as the suspected phasic episode. Taking the electromyogram data shown in fig. 5 as an example, 5 pieces of phasic data can be screened out initially, and after merging, they are merged into 1 phasic event, and the merging number is 5. Assuming that the number threshold is 3, there are 1 suspected phased episode events in the example of fig. 5; assuming there are combined results with a combined number less than 3, the combined result is not a suspected 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.
After the above series of processing, two suspected bruxism events are identified in step S2, and the two suspected bruxism events are analyzed one by one in step S3. It should be noted that, in step S3, for the suspected tonic event, the time information thereof is t shown in fig. 5 tonic And for the suspected phasic event obtained by merging, the time information of the suspected phasic event is from the starting time of the first phasic data segment before merging to the ending time of the last phasic data segment before merging.
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 (12)
1. A method of processing molar data, comprising:
acquiring facial electromyographic data and auxiliary data acquired by a wearable recorder worn on the face of a detection object;
determining a suspected molar event according to the facial electromyography data and a molar threshold;
verifying the suspected molars event using the assistance data to determine a molars event.
2. The method according to claim 1, characterized in that the assistance data comprises acceleration data and/or sound data.
3. The method of claim 2, wherein the suspected bruxism event is validated using the assistance data to determine a bruxism event, further comprising:
determining time information of the suspected bruxism event;
extracting acceleration data corresponding to the time information;
and determining whether the suspected molar event is a molar event according to the extracted variation of the acceleration data.
4. The method of claim 2, wherein the suspected bruxism event is validated using the assistance data to determine a bruxism event, further comprising:
determining time information of the suspected bruxism event;
extracting sound data corresponding to the time information;
and determining whether the suspected molar event is a molar event according to the extracted characteristics of the sound data.
5. The method of claim 2, wherein the suspected bruxism event is validated using the assistance data to determine a bruxism event, further comprising:
determining time information of the suspected bruxism event;
extracting acceleration data corresponding to the time information;
judging whether the variation of the extracted acceleration data is larger than a threshold value;
extracting sound data corresponding to the time information when the variation of the extracted acceleration data is greater than a threshold value;
judging whether the characteristics of the sound data accord with the characteristics of the teeth grinding sound;
when the characteristics of the sound data do not match the bruxism sound characteristics, a non-bruxism event is determined.
6. The method according to any one of claims 3-5, wherein the time information comprises a start time and an end time of the suspected bruxism event; in the step of verifying the suspected bruxism event with the assistance data to determine a bruxism event, the assistance data between the start time and the end time is extracted.
7. The method of claim 1, wherein determining a suspected bruxism event based on the facial electromyography data and a bruxism threshold, further comprises:
screening all data sections exceeding a molar threshold value from the facial electromyographic data;
and identifying at least two types of suspected grinding events 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 data segment of the sustained attack is larger than that of the data segment of the phasic attack.
8. The method of claim 7, wherein identifying at least two types of suspected bruxism events based on the time length of each of the screened out data segments further comprises:
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 suspected bruxism event of a phasic attack for satisfying t 2 -t 1 ≥t tonic The data field of (a) is recorded as a suspected bruxism event of a sustained attack.
9. The method of claim 7, further comprising, after identifying at least two types of suspected bruxism events based on the time length of each of the screened out data segments:
for the suspected molars of the identified phasic episode type, judging whether the time interval of every two adjacent suspected molars is lower than an interval threshold value;
merging two adjacent suspected molars when the time interval of the two adjacent suspected molars is below an interval threshold.
10. The method of claim 9, wherein the combined processing is performed iteratively until the time interval of all every two adjacent suspected bruxism events is above an interval threshold;
after the merging process, the method further comprises the following steps:
aiming at each suspected molar event obtained by merging, acquiring the number of the merged suspected molar events;
and deleting the merging results of which the merging quantity is smaller than the quantity threshold value.
11. A molar data processing device, comprising: a processor and a memory coupled to the processor; wherein the memory stores instructions executable by the processor to cause the processor to perform a method of processing molar data according to any one of claims 1-10.
12. A system for detecting a bruxism event, comprising: a host and a wearable recorder, wherein
The wearable recorder is used for collecting facial myoelectric data and auxiliary data;
the host computer is used for executing the molar data processing method according to any one of claims 1 to 10.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310423587.6A CN116350189A (en) | 2023-02-20 | 2023-02-20 | Tooth grinding event detection system and tooth grinding data processing method |
| CN202310135252.4A CN115844337B (en) | 2023-02-20 | 2023-02-20 | Tooth grinding event detection system and tooth grinding data processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310135252.4A CN115844337B (en) | 2023-02-20 | 2023-02-20 | Tooth grinding event detection system and tooth grinding data processing method |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310423587.6A Division CN116350189A (en) | 2023-02-20 | 2023-02-20 | Tooth grinding event detection system and tooth grinding data processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115844337A true CN115844337A (en) | 2023-03-28 |
| CN115844337B CN115844337B (en) | 2023-05-16 |
Family
ID=85658431
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310423587.6A Pending CN116350189A (en) | 2023-02-20 | 2023-02-20 | Tooth grinding event detection system and tooth grinding data processing method |
| CN202310135252.4A Active CN115844337B (en) | 2023-02-20 | 2023-02-20 | Tooth grinding event detection system and tooth grinding data processing method |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310423587.6A Pending CN116350189A (en) | 2023-02-20 | 2023-02-20 | Tooth grinding event detection system and tooth grinding data processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN116350189A (en) |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3635188A1 (en) * | 1986-10-16 | 1988-04-21 | Mueller Marie Elise | Device for recording nocturnal grinding of teeth and snoring for diagnostic and research purposes, as well as for the biofeedback treatment of affected patients |
| US20120271190A1 (en) * | 2009-03-04 | 2012-10-25 | Medotech A/S | Apparatus for electrical stimulation, in particular for bruxism |
| CN104866575A (en) * | 2015-05-25 | 2015-08-26 | 深圳创维－Rgb电子有限公司 | Teeth grinding detection method and detection system based on smart device |
| US20160095570A1 (en) * | 2014-10-02 | 2016-04-07 | Michiel Allessie | Method for detecting teeth grinding |
| JP2016101349A (en) * | 2014-11-28 | 2016-06-02 | ダイキン工業株式会社 | Bruxism detection device |
| US20170035350A1 (en) * | 2015-08-04 | 2017-02-09 | Bruxlab BV | System and method for detecting bruxism |
| CN106455989A (en) * | 2014-05-07 | 2017-02-22 | 太阳星瑞士有限公司 | Automatic detection of teeth clenching and/or teeth grinding |
| US20170265801A1 (en) * | 2016-03-15 | 2017-09-21 | Aalok Nital Patwa | Bruxism Detection System With Chin-Mounted Accelerometer Sensor |
| CN107397548A (en) * | 2017-06-16 | 2017-11-28 | 南京航空航天大学 | The surface electromyogram signal Feature Recognition System and method of a kind of bruxism |
| US20180220956A1 (en) * | 2018-04-05 | 2018-08-09 | Peter Kuhar | Bruxism tracking and reduction device and methods |
| CN109528165A (en) * | 2018-11-26 | 2019-03-29 | 广东小天才科技有限公司 | A kind of system for prompting based on smart machine, method and device |
| CN113384248A (en) * | 2020-03-13 | 2021-09-14 | 精工控股株式会社 | Intraoral sensing system and intraoral sensing method |
| CN113677260A (en) * | 2019-03-28 | 2021-11-19 | 旭日有限公司 | System comprising a sensing unit and a device for processing data related to disturbances that may occur during sleep of a subject |
| WO2022071626A1 (en) * | 2020-09-29 | 2022-04-07 | 김한림 | Personalized bruxism management system and management method |
| US11298073B1 (en) * | 2021-08-13 | 2022-04-12 | Delta Neuro Inc. | Device and method for detection and treatment of bruxism |
| US20220313153A1 (en) * | 2021-03-30 | 2022-10-06 | Apple Inc. | Diagnosis and monitoring of bruxism using earbud motion sensors |
| US20230047226A1 (en) * | 2021-08-16 | 2023-02-16 | Koninklijke Philips N.V. | Diagnosis and treatment for osa-related bruxism |
-
2023
- 2023-02-20 CN CN202310423587.6A patent/CN116350189A/en active Pending
- 2023-02-20 CN CN202310135252.4A patent/CN115844337B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3635188A1 (en) * | 1986-10-16 | 1988-04-21 | Mueller Marie Elise | Device for recording nocturnal grinding of teeth and snoring for diagnostic and research purposes, as well as for the biofeedback treatment of affected patients |
| US20120271190A1 (en) * | 2009-03-04 | 2012-10-25 | Medotech A/S | Apparatus for electrical stimulation, in particular for bruxism |
| CN106455989A (en) * | 2014-05-07 | 2017-02-22 | 太阳星瑞士有限公司 | Automatic detection of teeth clenching and/or teeth grinding |
| US20170071529A1 (en) * | 2014-05-07 | 2017-03-16 | Sunstar Suisse Sa | Automatic detection of teeth clenching and/or teeth grinding |
| US20160095570A1 (en) * | 2014-10-02 | 2016-04-07 | Michiel Allessie | Method for detecting teeth grinding |
| JP2016101349A (en) * | 2014-11-28 | 2016-06-02 | ダイキン工業株式会社 | Bruxism detection device |
| CN104866575A (en) * | 2015-05-25 | 2015-08-26 | 深圳创维－Rgb电子有限公司 | Teeth grinding detection method and detection system based on smart device |
| US20170035350A1 (en) * | 2015-08-04 | 2017-02-09 | Bruxlab BV | System and method for detecting bruxism |
| US20170265801A1 (en) * | 2016-03-15 | 2017-09-21 | Aalok Nital Patwa | Bruxism Detection System With Chin-Mounted Accelerometer Sensor |
| CN107397548A (en) * | 2017-06-16 | 2017-11-28 | 南京航空航天大学 | The surface electromyogram signal Feature Recognition System and method of a kind of bruxism |
| US20180220956A1 (en) * | 2018-04-05 | 2018-08-09 | Peter Kuhar | Bruxism tracking and reduction device and methods |
| CN109528165A (en) * | 2018-11-26 | 2019-03-29 | 广东小天才科技有限公司 | A kind of system for prompting based on smart machine, method and device |
| CN113677260A (en) * | 2019-03-28 | 2021-11-19 | 旭日有限公司 | System comprising a sensing unit and a device for processing data related to disturbances that may occur during sleep of a subject |
| CN113384248A (en) * | 2020-03-13 | 2021-09-14 | 精工控股株式会社 | Intraoral sensing system and intraoral sensing method |
| WO2022071626A1 (en) * | 2020-09-29 | 2022-04-07 | 김한림 | Personalized bruxism management system and management method |
| US20220313153A1 (en) * | 2021-03-30 | 2022-10-06 | Apple Inc. | Diagnosis and monitoring of bruxism using earbud motion sensors |
| US11298073B1 (en) * | 2021-08-13 | 2022-04-12 | Delta Neuro Inc. | Device and method for detection and treatment of bruxism |
| US20230047226A1 (en) * | 2021-08-16 | 2023-02-16 | Koninklijke Philips N.V. | Diagnosis and treatment for osa-related bruxism |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116350189A (en) | 2023-06-30 |
| CN115844337B (en) | 2023-05-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7406001B2 (en) | Non-invasive cardiac monitors and how to use recorded cardiac data to infer patient physiological characteristics | |
| JP5860894B2 (en) | EEG signal analysis to detect hypoglycemia | |
| CN112370049B (en) | Freezing gait acquisition and analysis system and method based on multi-mode signal synchronization | |
| JP4851847B2 (en) | Biological information measuring device | |
| JP5395954B2 (en) | EEG monitoring system and method for monitoring EEG | |
| WO2007098228A3 (en) | System and method of monitoring physiological signals | |
| CN109620158B (en) | Sleep monitoring method, intelligent terminal and storage device | |
| US20170265801A1 (en) | Bruxism Detection System With Chin-Mounted Accelerometer Sensor | |
| CN106236083A (en) | Sleep state removes the equipment of eye electricity artefact in analyzing | |
| CN115844337B (en) | Tooth grinding event detection system and tooth grinding data processing method | |
| CN110710962A (en) | Sleep state detection method and device | |
| CN116712055A (en) | Arrhythmia detection device and readable storage medium | |
| US20090131766A1 (en) | Method for monitoring a patient and system for implementing said method | |
| KR102536544B1 (en) | Wearable device for monitoring heart disease based on combined measurement of heart sound and electrocardiogram and operation method thereof | |
| CN115813351B (en) | Tooth grinding action detection method and system | |
| Roj et al. | Control and signal processing software embedded in smart wristband monitor of silent atrial fibrillation | |
| JP7337631B2 (en) | Myoelectric potential measurement data analysis program | |
| Guul et al. | Portable prescreening system for sleep apnea | |
| CN113208627A (en) | Dreaming environment discrimination method and system based on electroencephalogram EEG signals | |
| Datta et al. | Actigraphy: Indications, Procedural Do's and Don'ts, Sample Report | |
| Hanke et al. | A wearable device for realtime assessment of vigilance | |
| KR20230043435A (en) | Method for detecting respiration rate using piezoelectric sensor | |
| CN117426755A (en) | Sleep monitoring method and device and intelligent equipment | |
| JP2022030619A (en) | Information processing system, information processing method and program | |
| CN115633965A (en) | Electrocardiosignal detection device and equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |