Monitoring system and method for sleep bruxism

Abstract

The present application relates to the technical field of medical system, especially relates to a monitoring system and method for sleep grinding teeth, in signal processing, the present application adopts wavelet transform to carry out noise reduction, can effectively remove high-frequency noise and low-frequency baseline drift, also dynamically distinguishes grinding teeth and snoring sound signals through comparison of historical data, solves the common frequency band interference problem, improves signal quality and analysis accuracy, furthermore, in grinding teeth condition judgment and hazard analysis aspect, multi-dimension comprehensive evaluation grinding teeth abnormality degree and harm, considers various factors of grinding teeth, can accurately reflect the actual influence of grinding teeth on oral health, provides quantitative and objective basis for clinical diagnosis, treatment scheme formulation and curative effect evaluation, finally, in the stimulation judgment link, can accurately identify the grinding teeth position needing intervention, carries out current stimulation to the corresponding part of masticatory muscle in pertinence, avoids unnecessary intervention, makes treatment more accurate and effective.

Description

Technical Field

[0001] This invention relates to the field of medical systems technology, and more particularly to a monitoring system and method for sleep bruxism. Background Technology

[0002] Teeth grinding during sleep can directly damage oral tissues. Long-term teeth grinding can lead to wear and tear on the occlusal surfaces of teeth, potentially exposing the dentin and causing symptoms such as tooth sensitivity and pain. It can even affect the normal chewing function of teeth. Furthermore, the abnormal occlusal forces generated during grinding can cause tooth cracks, further increasing the risk of tooth fracture. In addition, teeth grinding can adversely affect the temporomandibular joint, causing symptoms of temporomandibular joint disorder such as joint pain, clicking sounds, and limited mouth opening, thus impacting the patient's normal oral function and quality of life.

[0003] Currently, monitoring methods for bruxism mainly focus on single-dimensional data collection and analysis. For example, some monitoring methods determine the presence of bruxism solely by monitoring the electromyographic activity of the masticatory muscles. However, the electromyographic activity of the masticatory muscles can be interfered with by various factors, such as swallowing and changes in sleep posture, which affects the accuracy of the monitoring results. Some methods only focus on tooth contact, ignoring the influence of other related factors during bruxism, making it impossible to comprehensively and accurately assess the severity and harm of bruxism. In addition, in terms of signal processing, existing technologies struggle to effectively address complex signal interference problems. During sleep, snoring signals and bruxism signals overlap in certain frequency bands, and traditional filtering methods easily cause the loss of useful signals or fail to completely remove interference signals, thus affecting the accurate analysis of bruxism signals. Summary of the Invention

[0004] In order to overcome the defects and shortcomings of the existing technology, the present invention provides a monitoring system and method for sleep bruxism.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for monitoring sleep bruxism, comprising the following steps:

[0007] Step S1: Acquire the patient's tooth contact status, electromyographic changes of masticatory muscles, and contact status of the patient's proximal teeth through sensors, and at the same time acquire the vibration and sound generated during snoring during sleep.

[0008] Step S2: Noise reduction of sensor received signals based on vibrations and sounds generated during the patient's snoring during sleep;

[0009] Step S3: Determine the condition of teeth grinding based on the noise-reduced sensor received signal, and analyze the degree of teeth grinding abnormality;

[0010] Step S4: Analyze the harm of molars based on the analysis results of the patient's tooth contact conditions and the degree of molar abnormality;

[0011] Step S5: Determine whether the irritation is caused by the analysis results of the harm of bruxism.

[0012] In one implementation of the present invention, step S1 includes the following specific contents: a flexible pressure sensor is embedded in the braces to monitor the contact frequency, force and duration of the upper and lower teeth in real time, thereby recording the spatiotemporal distribution of occlusal events; surface electromyography electrodes are attached to the masticatory muscles such as the masseter and temporalis muscles to monitor the intensity, burst frequency and duration of muscle electrical activity, in order to distinguish between resting periods, mild activity and high-intensity molar activity; the tooth alignment, degree of wear on the occlusal surface or the force on the restoration is analyzed by dental scanning or the micro-deformation sensor built into the smart braces; the vibration spectrum of snoring is collected using a bone conduction microphone or a laryngeal accelerometer, and the acoustic characteristics are recorded simultaneously through an environmental microphone, the intensity, frequency and periodicity of snoring are marked, and stored in the corresponding storage component.

[0013] In one implementation of the present invention, the noise reduction of the sensor received signal in step S2 includes the following specific steps:

[0014] S21. Wavelet transform is used to decompose the obtained electromyographic and vibration signals to separate components of different frequency bands; high-frequency noise and low-frequency drift are clearly separated, providing precise targets for subsequent noise reduction.

[0015] S22. Obtain the safe range of historical molar characteristic frequency bands, remove high-frequency noise bands and low-frequency baseline drift bands from the signal to obtain a noise-reduced signal. However, there is still snoring signal interference from the patient within the regional signal, so it needs to be removed. Through hard truncation of frequency bands, known interference is efficiently removed, and the molar-related frequency bands are completely preserved, avoiding the edge effect of traditional filters.

[0016] S23. Simultaneously acquire the intensity and frequency of each frequency band signal in the noise-reduced signal, as well as the average intensity and average frequency of the patient's historical snoring. Acquire the similarity between each frequency band signal in the noise-reduced signal and the patient's historical snoring. Simultaneously acquire the similarity between each frequency band signal in the noise-reduced signal and the historical molar situation signal. The similarity here can be calculated using the cosine similarity calculation formula. The corresponding frequency band signal whose similarity to the patient's historical snoring is less than that to the similarity to the historical molar situation signal is set as snoring interference signal and removed. The corresponding frequency band signal whose similarity to the patient's historical snoring is greater than or equal to that to the similarity to the historical molar situation signal is set as the real molar signal. Calculate the intensity and center frequency of each frequency band of the noise-reduced signal, extract the template features of the patient's historical snoring and molar signals, compare the vector similarity between the current frequency band signal and the historical snoring / molar template, and dynamically distinguish molar and snoring using the patient's historical data to solve the problem of co-frequency interference.

[0017] S24. Obtain the intensity and frequency of the actual molar signal in the muscle electrical activity signal and vibration signal.

[0018] In one implementation of the present invention, the analysis of the degree of molar abnormality in step S3 includes the following specific contents:

[0019] This method involves acquiring information on the force, displacement, and frequency of molars at various locations based on real molar signals. It also involves obtaining the ratio of these parameters to the corresponding safe values ​​for each data type, thus determining the risk value for that specific molar type. Finally, a weighted sum of the risk values ​​for all molar types yields the degree of abnormality at the corresponding location. This process of acquiring key parameters of the molar signal, calculating risk values, and weighted summation to determine the degree of abnormality has significant advantages and provides a solid basis. Furthermore, the method of obtaining safe values ​​is diverse. Its advantage lies in its ability to comprehensively and accurately assess molar conditions through multi-dimensional data analysis, avoiding the limitations of single-indicator assessments. This enables early detection of potential molar problems and provides a quantitative and objective basis for clinical diagnosis, treatment planning, and efficacy evaluation. Based on biomechanical and neuroscience theories, the force, displacement, and frequency of teeth grinding are closely related to oral tissue damage and nervous system function. Taking these parameters into account can better reflect the degree of harm caused by teeth grinding. There are various ways to obtain safe values ​​for the corresponding data types. Large-sample clinical studies can be used to statistically analyze the relevant parameter ranges of healthy individuals as a reference. The force, displacement, and frequency of teeth grinding are not isolated; they influence each other. When the force of teeth grinding is large, even if the displacement is small or the frequency is not high, it may still cause significant damage to the teeth and joints. Conversely, if the frequency of teeth grinding is very high, even if the force and displacement are small each time, the cumulative effect may lead to serious problems.

[0020] In one implementation of the present invention, the analysis of molar hazards in step S4 includes the following specific contents:

[0021] S41. Obtain the tooth arrangement, occlusal surface wear, or stress on the restoration at each location, and also obtain information on cracks caused by tooth occlusion.

[0022] S42. Obtain information on cracks caused by tooth occlusion, including crack length, crack width, and crack depth. Simultaneously, obtain the average distance between the crack and the corresponding molar region. Obtain the crack volume using the crack length, crack width, and crack depth. This volume can be roughly obtained using data processing software. Divide the safe distance between the crack and the corresponding molar region by the average distance between the crack and the corresponding molar region to obtain the distance hazard coefficient. Divide the crack volume by the safe volume to obtain the volume hazard coefficient. Multiply the distance hazard coefficient and the volume hazard coefficient of the corresponding crack to obtain the crack hazard coefficient.

[0023] S43. By considering the safe force conditions of the teeth or restorations at the corresponding positions of the patient, and the actual force conditions at the corresponding positions, the force tolerance risk is obtained by dividing the actual force conditions at the corresponding positions by the safe force conditions. The safe force conditions are the maximum force that a normal person can withstand at the corresponding positions without damaging the teeth.

[0024] S44. The wear anomaly value at the corresponding location is obtained by weighted summation of the stress-bearing risk and crack risk. The wear anomaly value is multiplied by the wear degree of the occlusal surface to obtain the wear risk coefficient. The wear anomaly value is obtained by weighted summation of the stress-bearing risk and crack risk, which comprehensively considers the influence of tooth stress and crack on tooth wear, making the assessment more comprehensive and accurate. The wear anomaly value is then multiplied by the wear degree of the occlusal surface to obtain the wear risk coefficient, which further combines the actual wear performance and can more accurately reflect the wear risk of the tooth at that location due to molarity.

[0025] S45. The wear risk coefficient of each position is weighted and summed with the degree of molar abnormality at the corresponding position to obtain the hazard analysis value of the molar at the corresponding position. The impact on teeth at different positions during the grinding process may be different, and their wear risk coefficients will also be different. At the same time, the degree of molar abnormality will also vary depending on the position. By weighting and summing, the combined effect of these factors can be comprehensively considered.

[0026] In one implementation of the present invention, the determination of whether there is irritation based on the analysis results of the harm to molars in step S5 includes the following specific contents:

[0027] Compare the hazard analysis values ​​of all molars with the hazard threshold. If the hazard analysis values ​​of all molars are less than or equal to the hazard threshold, no stimulation is required. If there is a hazard analysis value greater than or equal to the hazard threshold at one location, stimulation of the corresponding location of the masticatory muscle is required. The stimulation process is as follows: trigger the electrodes attached to the surface of the masticatory muscle to apply current stimulation. The initial current intensity can be set to 0.5-1mA, the frequency to 10-50 Hz, and the pulse width to 0.1-0.5ms. After receiving the trigger signal, the electrical stimulation device applies current stimulation to the electrodes attached to the surface of the masticatory muscle according to the preset parameters.

[0028] Secondly, the present invention also provides a monitoring system for sleep bruxism, comprising:

[0029] The data acquisition module uses sensors to acquire information about the patient's tooth contact, electromyographic changes of the masticatory muscles, and contact between the proximal surfaces of the patient's teeth. It also acquires information about vibrations and sounds generated during snoring during sleep.

[0030] The noise reduction module reduces the noise of the sensor-received signals based on the vibrations and sounds generated during the patient's snoring during sleep.

[0031] The molar abnormality analysis module judges the molar condition based on the noise-reduced sensor received signal and analyzes the degree of molar abnormality.

[0032] The bruxism hazard analysis module analyzes the bruxism hazards based on the analysis results of the patient's tooth contact conditions and the degree of bruxism abnormalities.

[0033] The stimulation control module determines whether stimulation is necessary based on the analysis results of the harm caused by bruxism.

[0034] Thirdly, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a monitoring method for sleep bruxism by calling the computer program stored in the memory.

[0035] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a monitoring method for sleep bruxism.

[0036] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0037] This study utilizes a multi-dimensional approach to assess the impact of bruxism on oral health. The noise reduction process is based on the vibrations and sounds generated during snoring during sleep. The resulting signals are then used to determine the extent of bruxism and analyze its severity. Furthermore, the analysis of the contact patterns between adjacent teeth and the degree of bruxism abnormality is used to assess the potential harm of bruxism. In signal processing, wavelet transform is employed for noise reduction, effectively removing high-frequency noise and low-frequency baseline drift. Historical data is compared to dynamically distinguish between bruxism and snoring signals, resolving co-frequency interference and improving signal quality and analysis accuracy. Moreover, the assessment of bruxism and its potential harm is multi-dimensional, considering various factors to accurately reflect the actual impact of bruxism on oral health. This provides a quantitative and objective basis for clinical diagnosis, treatment planning, and efficacy evaluation. Finally, the stimulation assessment precisely identifies the location of the bruxism requiring intervention, allowing for targeted electrical stimulation of the corresponding masticatory muscles. This avoids unnecessary intervention, making treatment more precise and effective. By relieving muscle tension, the treatment directly targets the root of the bruxism, thereby reducing bruxism. Attached Figure Description

[0038] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0039] Figure 1 This is a schematic diagram of the overall process of Embodiment 1 of the method of the present invention;

[0040] Figure 2 This is a schematic diagram of step S2 of embodiment 1 of the method of the present invention;

[0041] Figure 3 This is a schematic diagram of step S3 in Embodiment 1 of the method of the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of embodiment 2 of the system of the present invention. Detailed Implementation

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0046] Example 1

[0047] like Figures 1 to 3 As shown, this embodiment provides a method for monitoring sleep bruxism, specifically including the following steps:

[0048] Step S1: Acquire the patient's tooth contact status, electromyographic changes of masticatory muscles, and contact status of the patient's proximal teeth through sensors, and at the same time acquire the vibration and sound generated during snoring during sleep.

[0049] It should be noted that step S1 includes the following specific contents: A flexible pressure sensor (such as a piezoresistive film or MEMS pressure sensor) is embedded in the brace to monitor the contact frequency, force, and duration of the upper and lower teeth in real time, thereby recording the spatiotemporal distribution of occlusal events (such as clenching and grinding); surface electromyography electrodes are attached to the masseter muscle, temporalis muscle, etc., to monitor the intensity, burst frequency, and duration of muscle electrical activity, used to distinguish between resting periods, mild activities (such as swallowing), and high-intensity molar activity; the tooth alignment, occlusal surface wear degree, or the force on restorations (such as crowns and implants) is analyzed through dental scanning or the micro-deformation sensor built into the smart brace; the vibration spectrum of snoring is collected using a bone conduction microphone or laryngeal accelerometer, and the acoustic characteristics are recorded simultaneously through an environmental microphone, marking the intensity, frequency (usually 20–300 Hz), and periodicity of snoring, and stored in the corresponding storage component. The occlusal surface wear degree is obtained by comparing it with normal teeth to obtain the ratio of its wear volume to that of normal teeth.

[0050] Step S2: Noise reduction of sensor received signals based on vibrations and sounds generated during the patient's snoring during sleep;

[0051] It should be noted that the noise reduction of the sensor received signal in step S2 includes the following specific steps:

[0052] S21. Use wavelet transform to perform wavelet decomposition on the acquired electromuscular activity signal and vibration signal to separate components of different frequency bands; typically, decompose into 5 to 6 layers. Example of frequency band division (sampling rate 1kHz):

[0053] D1-D2 (250~500Hz, 125~250Hz): High-frequency noise (EMG interference, sensor noise); D3-D5 (62.5~125Hz, 31.25~62.5Hz, 15.625~31.25Hz): Molar characteristic frequency band;

[0054] D6 (0-15.625Hz): Baseline drift. When selecting wavelet transforms: The Daubechies 4 (Db4) wavelet has tight support and moderate regularity, making it suitable for analyzing non-stationary EMG / vibration signals. The frequency band division of the Db4 wavelet is highly matched with the physiological characteristics of molar signals (10-100Hz), avoiding the loss of useful signals. High-frequency noise and low-frequency drift are clearly separated, providing precise targets for subsequent noise reduction.

[0055] S22. Obtain the safe range of historical molar characteristic frequency bands, remove high-frequency noise bands and low-frequency baseline drift bands from the signal to obtain a denoised signal, i.e., the signal within the range of (62.5~125Hz, 31.25~62.5Hz, 15.625~31.25Hz). However, there is still snoring signal interference within the signal area, so it needs to be removed. Through hard truncation of frequency bands, known interference is efficiently removed, while the molar-related frequency bands are completely preserved, avoiding the edge effect of traditional filters. The safe range of historical molar characteristic frequency bands is obtained through historical statistics, obtaining the range of 80% of the molar characteristic frequency bands.

[0056] S23. Simultaneously acquire the intensity and frequency of each frequency band signal in the noise-reduced signal, as well as the average intensity and average frequency of the patient's historical snoring. Acquire the similarity between each frequency band signal in the noise-reduced signal and the patient's historical snoring. Simultaneously acquire the similarity between each frequency band signal in the noise-reduced signal and the historical molar situation signal. The similarity here can be calculated using the cosine similarity calculation formula. The corresponding frequency band signal whose similarity to the patient's historical snoring is less than that to the similarity to the historical molar situation signal is set as snoring interference signal and removed. The corresponding frequency band signal whose similarity to the patient's historical snoring is greater than or equal to that to the similarity to the historical molar situation signal is set as the real molar signal. Calculate the intensity (root mean square energy) and center frequency of each frequency band of the noise-reduced signal (D3-D5). Extract the template features (mean intensity, frequency distribution) of the patient's historical snoring and molar signals. Compare the vector similarity between the current frequency band signal and the historical snoring / molar template. Use the patient's historical data to dynamically distinguish molar and snoring, and solve the problem of co-frequency interference.

[0057] S24. Obtain the intensity and frequency of the actual molar signal in the muscle electrical activity signal and vibration signal;

[0058] Step S3: Determine the condition of teeth grinding based on the noise-reduced sensor received signal, and analyze the degree of teeth grinding abnormality;

[0059] In this embodiment, the analysis of the degree of molar abnormality in step S3 includes the following specific contents:

[0060] This method involves acquiring information on the force, displacement, and frequency of molars at various locations based on real molar signals. It also involves obtaining the ratio of these parameters to the corresponding safe values ​​for each data type, thus determining the risk value for that specific molar type. Finally, a weighted sum of the risk values ​​for all molar types yields the degree of abnormality at the corresponding location. This process of acquiring key parameters of the molar signal, calculating risk values, and weighted summation to determine the degree of abnormality has significant advantages and provides a solid basis. Furthermore, the method of obtaining safe values ​​is diverse. Its advantage lies in its ability to comprehensively and accurately assess molar conditions through multi-dimensional data analysis, avoiding the limitations of single-indicator assessments. This enables early detection of potential molar problems and provides a quantitative and objective basis for clinical diagnosis, treatment planning, and efficacy evaluation. Based on biomechanical and neuroscience theories, the force, displacement, and frequency of tooth grinding are closely related to oral tissue damage and nervous system function. A comprehensive consideration of these parameters can better reflect the degree of harm caused by tooth grinding. There are various ways to obtain safe values ​​for the corresponding data types, such as using large-sample clinical studies to statistically analyze the relevant parameter ranges in healthy individuals as a reference. Tooth grinding force, displacement, and frequency are not isolated; they influence each other. When the force of tooth grinding is large, even if the displacement is small or the frequency is low, it can still cause significant damage to teeth and joints. Conversely, if the frequency of tooth grinding is high, even if the force and displacement are small each time, the cumulative effect can lead to serious problems. A weighted summation can comprehensively consider these parameters. The synergistic effect between them more accurately reflects the actual impact of molars on oral health. Here, it is necessary to divide the upper and lower teeth into corresponding positions, and not to analyze all teeth as a whole. This is because the more abnormal the corresponding tooth is, the more easily the corresponding tooth is damaged. A large amount of clinical data from molar patients is collected, including molar force, displacement, frequency, and related indicators of oral tissue damage (such as the degree of tooth wear, temporomandibular joint function indicators, etc.). Statistical methods (such as Pearson correlation coefficient, Spearman correlation coefficient, etc.) are used to analyze the correlation between each parameter and oral tissue damage indicators. The higher the correlation of a parameter, the greater its influence on the degree of molar abnormality, and the higher the weight it is assigned.

[0061] Step S4: Analyze the harm of molars based on the analysis results of the patient's tooth contact conditions and the degree of molar abnormality;

[0062] In this embodiment, the analysis of the harm caused by molars in step S4 includes the following specific contents:

[0063] S41. Obtain the tooth arrangement, occlusal surface wear, or stress on the restoration at each location, and also obtain information on cracks caused by tooth occlusion.

[0064] S42. Obtain information on cracks caused by tooth occlusion, including crack length, crack width, and crack depth. Simultaneously, obtain the average distance of the crack relative to the corresponding molar region. Calculate the crack volume using the crack length, width, and depth. This can be roughly obtained using data processing software. Divide the safe distance of the crack relative to the corresponding molar region by the average distance of the crack relative to the corresponding molar region to obtain a distance hazard coefficient. Divide the crack volume by the safe volume to obtain a volume hazard coefficient. Multiply the distance hazard coefficient and the volume hazard coefficient for the corresponding crack to obtain the crack hazard coefficient. Sum the crack hazard coefficients of all cracks at the corresponding location to obtain the crack hazard at that location. This is achieved by quantifying various characteristics of the crack (length, width, ...). The crack volume is obtained by calculating the crack depth, and distance and volume hazard factors are introduced to calculate the crack hazard. This allows for a scientific and accurate assessment of the potential danger of cracks to teeth. Furthermore, by summing the crack hazard factors of all cracks at a given location, the overall impact of cracks at that location can be comprehensively considered, providing a more complete picture of the overall risk to the tooth caused by the crack. The size (volume) of the crack and its relative position (average distance) to the molar region are important factors affecting tooth safety. The larger the crack volume, the more severe the damage to the tooth structure. The closer the crack is to the molar region, the easier it is to extend further during molar movement, leading to increased tooth damage. Therefore, calculating and multiplying the hazard factors based on these two key factors can reasonably reflect the degree of danger posed by the crack.

[0065] S43. By analyzing the safe force-bearing conditions of the patient's teeth or restorations at the corresponding positions, and the actual force-bearing conditions at those positions, the force-bearing risk is obtained by dividing the actual force-bearing conditions by the safe force-bearing conditions. The safe force-bearing conditions represent the maximum force a normal person can withstand at the corresponding position without damaging the teeth. Calculating the force-bearing risk provides a clear understanding of the safety status of the patient's teeth or restorations under molar force conditions. Comparing the actual force-bearing conditions with the safe force-bearing conditions quantitatively and intuitively demonstrates the degree of risk the patient's teeth or restorations face under molar force, providing an important force-related indicator for subsequent assessment of molar damage. In oral medicine, teeth and restorations have their safe force-bearing ranges. When the force during molar grinding exceeds this safe range, the risk of tooth damage increases. The standard force-bearing conditions represent the safe force-bearing conditions for the corresponding positions in normal individuals. By comparing these conditions with the patient's own safe force-bearing conditions, it can be determined whether the patient's teeth are in a dangerous state under the current molar force conditions, which is more in line with the principles of oral biomechanics and dental health assessment.

[0066] S44. The wear anomaly value at the corresponding location is obtained by weighted summation of the stress-bearing risk and crack risk. This wear anomaly value is then multiplied by the wear degree of the occlusal surface to obtain the wear risk coefficient. This weighted summation of stress-bearing risk and crack risk comprehensively considers the impact of both stress and crack on tooth wear, making the assessment more comprehensive and accurate. Further multiplying the wear anomaly value by the wear degree of the occlusal surface to obtain the wear risk coefficient, which further incorporates the actual wear performance, can more accurately reflect the degree of wear risk caused by molarization at that location. This provides a more targeted indicator for subsequent assessment of molar damage. Tooth wear is a complex process, influenced by both stress and crack. The impact of stress conditions is also closely related to the existence and development of cracks. Stress tolerance risk reflects the potential danger of teeth under stress, while crack risk reflects the risk of damage to the tooth structure from cracks. Weighted summation of these two factors comprehensively considers these two important factors. Occlusal surface wear is a direct manifestation of molar damage; multiplying it by the wear anomaly value more accurately reflects the actual wear risk. The weighting of stress tolerance risk and crack risk in calculating wear anomaly values ​​can be determined through extensive clinical research and data analysis. Researchers collected case data from numerous patients with bruxism, including information on tooth stress conditions, crack conditions, and the final degree of tooth damage. Then, statistical methods, such as multiple linear regression analysis, were used to determine the degree of influence of stress tolerance risk and crack risk on abnormal tooth wear, thereby obtaining the corresponding weighting.

[0067] S45. The wear risk coefficient at each location is weighted and summed with the corresponding degree of molar abnormality to obtain the molar hazard analysis value for that location. Teeth at different locations may be affected differently during molarization, resulting in varying wear risk coefficients. Furthermore, the degree of molar abnormality also varies depending on location. Weighted summation allows for a comprehensive consideration of these factors, more accurately reflecting the degree of harm molars cause to the entire oral cavity, thus meeting the comprehensive and systematic requirements for molar hazard assessment in oral medicine. The weights are determined through large-scale clinical trials, tracking the wear risk coefficient, degree of molar abnormality, and ultimate harm to oral health in different patients. Statistical analysis methods, such as principal component analysis, are used to determine the weights of these factors on the molar hazard analysis value.

[0068] Step S5: Determine whether the irritation is caused by the analysis results of the harm of bruxism.

[0069] In this embodiment, step S5, which determines whether there is irritation based on the analysis results of the harm to molars, includes the following specific content:

[0070] The hazard analysis values ​​of molars at all locations were compared with the hazard threshold. If the hazard analysis values ​​at all locations were less than or equal to the hazard threshold, no stimulation was required. If the hazard analysis value at any one location was greater than or equal to the hazard threshold, stimulation was required at that location. A large number of case data of patients with bruxism were collected, and the hazard analysis value for each patient was calculated. The progression of tooth damage was tracked. Patients were divided into a damaged group and a non-damaged group based on whether they had significant tooth damage (such as tooth fractures, severe malocclusion, etc.). Through statistical analysis, a suitable threshold was determined to maximize the differentiation between the injured and uninjured groups. The stimulation process was as follows: electrical stimulation was applied to electrodes attached to the surface of the masticatory muscles by triggering the stimulation. The initial current intensity was set at 0.5–1 mA, the frequency at 10–50 Hz, and the pulse width at 0.1–0.5 ms. After receiving the trigger signal, the electrical stimulation device applied electrical stimulation to the electrodes attached to the surface of the masticatory muscles according to the preset parameters. During the stimulation process, the subject's reaction was closely observed, and their sensations were inquired about, such as whether there were any abnormal sensations like tingling or numbness. The electrical stimulation parameters could be adjusted appropriately based on the subject's tolerance and the treatment effect. For example, if the subject felt the stimulation intensity was insufficient, the current intensity could be gradually increased, but the increase should not be too large each time, generally not exceeding 0.5 ms. mA controls the duration of each electrical stimulation, typically lasting 10-30 seconds. The frequency of stimulation is determined by the frequency of bruxism, such as stimulating once every time bruxism occurs, or performing multiple intermittent stimulations over a period of time. By analyzing and comparing the damage caused by bruxism in different locations individually, it is possible to accurately identify which specific locations have reached the level requiring intervention. This allows for targeted electrical stimulation of the corresponding locations of the masticatory muscles, avoiding unnecessary intervention in areas that do not require stimulation. This makes the treatment more precise and effective. Precise localization stimulation can directly act on the root cause of the bruxism problem, that is, by specifically stimulating the masticatory muscles, relieving muscle tension, reducing bruxism in that location, and improving the targeting and effectiveness of the treatment.

[0071] In this embodiment, it should be noted that it has the following advantages: Noise reduction of sensor received signals is performed based on the vibrations and sounds generated during the patient's snoring during sleep; the denoised sensor received signals are used to determine the degree of bruxism and analyze its abnormality; the harm of bruxism is analyzed based on the contact between the patient's teeth and the analysis results of the degree of bruxism abnormality; in signal processing, wavelet transform is used for noise reduction, which can effectively remove high-frequency noise and low-frequency baseline drift; and bruxism signals are dynamically distinguished from snoring signals by comparing historical data, solving the problem of co-frequency interference. This approach improves signal quality and analysis accuracy. Furthermore, in assessing bruxism and analyzing its impact, it comprehensively evaluates the degree and severity of bruxism abnormalities from multiple dimensions, considering various factors. This accurately reflects the actual impact of bruxism on oral health, providing quantitative and objective evidence for clinical diagnosis, treatment planning, and efficacy evaluation. Finally, in the stimulation assessment stage, it precisely identifies the location of bruxism requiring intervention, targeting the corresponding areas of the masticatory muscles with electrical stimulation to avoid unnecessary intervention and make treatment more precise and effective. By relieving muscle tension, it directly targets the root of the bruxism, thereby reducing bruxism behavior.

[0072] Example 2

[0073] like Figure 4 As shown, this embodiment provides a monitoring system for sleep bruxism, implemented based on the monitoring method for sleep bruxism in Embodiment 1. It includes: a data acquisition module, which acquires information such as the patient's tooth contact, electromyographic changes of the masticatory muscles, and the contact between the patient's adjacent teeth via sensors, while also acquiring vibrations and sounds generated during snoring during sleep; a noise reduction module, which reduces the noise of the sensor-received signals based on the vibrations and sounds generated during snoring; a bruxism abnormality analysis module, which judges the bruxism condition based on the noise-reduced sensor-received signals and analyzes the degree of bruxism abnormality; a bruxism hazard analysis module, which analyzes the bruxism hazard based on the analysis results of the patient's adjacent tooth contact and the degree of bruxism abnormality; and a stimulation control module, which determines whether stimulation is necessary based on the analysis results of the bruxism hazard. The specific steps of each module in this embodiment are the same as those in the method embodiment of Embodiment 1, and will not be repeated here.

[0074] Example 3

[0075] An electronic device according to an embodiment of the present invention includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a monitoring method for sleep bruxism by calling the computer program stored in the memory. It should be noted that all computer programs for the sleep bruxism monitoring method are implemented using the C programming language.

[0076] Example 4

[0077] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0078] When the computer program runs on the computer device, it causes the computer device to perform the above-described monitoring method for sleep bruxism.

[0079] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0080] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0082] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0083] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0085] In the description of this specification, references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0086] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring sleep bruxism, characterized in that, Includes the following steps: Step S1: The sensor acquires information on the patient's tooth contact, electromyographic changes of the masticatory muscles, and contact between the patient's adjacent teeth, while also acquiring information on vibrations and sounds generated during snoring during sleep. Step S2: Noise reduction of sensor received signals based on vibrations and sounds generated during the patient's snoring during sleep; Step S3: Determine the condition of teeth grinding based on the noise-reduced sensor received signal, and analyze the degree of teeth grinding abnormality; Step S4: Analyze the harm of molars based on the analysis results of the patient's tooth contact conditions and the degree of molar abnormality; The analysis of the hazards of molars includes the following specific contents: To obtain information on tooth alignment, occlusal surface wear, and stress on restorations at various locations, as well as information on cracks caused by tooth occlusion. The system obtains information on cracks caused by tooth occlusion, including crack length, crack width, and crack depth. It also obtains the average distance of the crack relative to the corresponding molar region. The volume of the crack is obtained from the crack length, crack width, and crack depth. The distance hazard coefficient is obtained by dividing the safe distance of the crack relative to the corresponding molar region by the average distance of the crack relative to the corresponding molar region. The volume hazard coefficient is obtained by dividing the crack volume by the safe volume. The crack hazard coefficient is obtained by multiplying the distance hazard coefficient and the volume hazard coefficient of the corresponding crack. The crack hazard coefficients of all cracks at the corresponding location are added together to obtain the crack hazard at the corresponding location. Obtain the safe force conditions of the teeth or restorations at the corresponding positions of the patient, as well as the actual force conditions at the corresponding positions. Divide the actual force conditions at the corresponding positions by the safe force conditions to obtain the force tolerance risk. The safe force conditions are the maximum force that a normal person can withstand at the corresponding positions without damaging the teeth. The wear anomaly value at the corresponding location is obtained by weighted summation of the stress bearing risk and crack risk, and the wear anomaly value is multiplied by the wear degree of the mating surface to obtain the wear risk coefficient; The wear risk coefficient at each location is weighted and summed with the degree of molar abnormality at the corresponding location to obtain the hazard analysis value of the molar at the corresponding location; Step S5: Determine whether the irritation is caused by the analysis results of the harm of bruxism.

2. The method for monitoring sleep bruxism according to claim 1, characterized in that, The noise reduction of the sensor-received signal includes the following specific steps: Wavelet transform was used to decompose the acquired muscle electrophysiological activity signal and vibration signal to separate components of different frequency bands; The safe range of historical molar characteristic frequency bands is obtained, and high-frequency noise bands and low-frequency baseline drift bands are removed from the signal to obtain a noise-reduced signal. The intensity and frequency of each frequency band signal in the noise reduction signal are obtained, as well as the average intensity and average frequency of the patient's historical snoring. The similarity between each frequency band signal in the noise reduction signal and the patient's historical snoring is obtained. At the same time, the similarity between each frequency band signal in the noise reduction signal and the historical molar situation signal is obtained. The corresponding frequency band signal whose similarity between the frequency band signal and the patient's historical snoring is less than that between the frequency band signal and the historical molar situation signal is set as snoring interference signal and removed. The corresponding frequency band signal whose similarity between the frequency band signal and the patient's historical snoring is greater than or equal to that between the frequency band signal and the historical molar situation signal is set as the real molar signal. The intensity and frequency of the actual molar signal in the muscle electrical activity signal and vibration signal were obtained.

3. The method for monitoring sleep bruxism according to claim 1, characterized in that, The analysis of the degree of molar abnormality includes the following specific contents: The system acquires information on the grinding force, displacement, and frequency of the real grinding signal at various locations. It then obtains the ratio of the grinding force, displacement, and frequency at each location to the safety value of the corresponding data type to obtain the risk value of the corresponding grinding data type. Finally, it performs a weighted summation of the risk values ​​of all grinding data types to obtain the degree of grinding abnormality at the corresponding location.

4. The method for monitoring sleep bruxism according to claim 3, characterized in that, The determination of whether or not there is irritation based on the analysis results of the harm to molars in step S5 includes the following specific contents: Compare the hazard analysis values ​​of all molars with the hazard threshold. If the hazard analysis values ​​of all molars are less than or equal to the hazard threshold, no stimulation is required. If there is a hazard analysis value greater than or equal to the hazard threshold at one location, the corresponding location of the masticatory muscle needs to be stimulated.

5. The method for monitoring sleep bruxism according to claim 4, characterized in that, The stimulation process is as follows: an electric current is applied to the electrodes attached to the surface of the masticatory muscle by triggering the electrodes. After receiving the trigger signal, the electric stimulation device applies an electric current to the electrodes attached to the surface of the masticatory muscle according to preset parameters.

6. The method for monitoring sleep bruxism according to claim 1, characterized in that, Step S1 includes the following specific contents: a flexible pressure sensor is embedded in the braces to monitor the contact frequency, force and duration of the upper and lower teeth in real time, thereby recording the spatiotemporal distribution of occlusal events; surface electromyography electrodes are attached to the masseter and temporalis muscles to monitor the intensity, burst frequency and duration of muscle electrical activity, in order to distinguish between resting periods, mild activity and high-intensity molar activity; the tooth alignment, degree of wear on the occlusal surface or the force on the restoration is analyzed by dental scanning or the micro-deformation sensor built into the smart braces; the vibration spectrum of snoring is collected using a bone conduction microphone or a laryngeal accelerometer, and the acoustic characteristics are recorded simultaneously through an environmental microphone, the intensity, frequency and periodicity of snoring are labeled, and stored in the corresponding storage components.

7. A monitoring system for sleep bruxism, used to implement the monitoring method for sleep bruxism according to any one of claims 1-6, characterized in that, The system includes: The data acquisition module uses sensors to acquire information about the patient's tooth contact, electromyographic changes of the masticatory muscles, and contact between the proximal surfaces of the patient's teeth. It also acquires information about vibrations and sounds generated during snoring during sleep. The noise reduction module reduces the noise of the sensor-received signals based on the vibrations and sounds generated during the patient's snoring during sleep. The molar abnormality analysis module judges the molar condition based on the noise-reduced sensor received signal and analyzes the degree of molar abnormality. The bruxism hazard analysis module analyzes the bruxism hazards based on the analysis results of the patient's tooth contact conditions and the degree of bruxism abnormalities. The stimulation control module determines whether stimulation is necessary based on the analysis results of the harm caused by bruxism.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the monitoring method for sleep bruxism as described in any one of claims 1-6 by calling the computer program stored in the memory.

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