Electromyography analysis system for self-sleep test and the method of thereof

Abstract

An electromyogram analysis system and a method for self-sleep testing are disclosed. An electromyogram analysis system for self-sleep testing according to one embodiment of the present invention includes: a data collection unit that collects electromyogram data from a designated wearable device; a data generation unit that generates monitoring data based on the electromyogram data collected from the data collection unit; and a data analysis unit that generates analysis result data based on the monitoring data generated by the monitoring data generation unit.

Description

Technology Field

[0001] The present invention relates to an electromyography analysis system and method for self-sleep testing, and more specifically, to an electromyography analysis system and method for self-sleep testing that monitors whether teeth grinding symptoms occur in a user during sleep using a wearable device. Background Technology

[0002] While there are various ways to maintain and improve health, such as exercise and diet, managing sleep—which accounts for more than 30% of our daily time—is of the utmost importance. However, despite the simple replacement of labor by machines and the leisure of life, modern people are unable to get restful sleep due to irregular eating habits, lifestyles, and stress, and are suffering from sleep disorders such as insomnia, hypersomnia, sleep apnea syndrome, and teeth grinding during sleep.

[0003] When a person falls asleep, they enter either NREM (Non-Rapid Eye Movement) slow-wave sleep or REM (Rapid Eye Movement) sleep. Non-REM sleep (slow-wave sleep) refers to deep sleep, while REM sleep refers to light sleep.

[0004] In REM sleep, eye movements are generally more vigorous than in the waking state, and in non-REM sleep, physiological functions are generally significantly reduced compared to the waking state.

[0005] Typically, REM and non-REM sleep repeat in 90-minute cycles, occurring about 4 to 6 times during a single night's sleep. REM sleep accounts for 20 to 25% of total sleep time, while non-REM sleep accounts for 75 to 80%.

[0006] When you are tired, the proportion of REM sleep may decrease relatively and the proportion of non-REM sleep may increase relatively, and if you get enough rest, conversely, the proportion of REM sleep increases and the proportion of non-REM sleep decreases.

[0007] Meanwhile, since most sleep disorders occur during unconscious sleep, it is difficult for individuals to determine on their own whether they have a sleep disorder, and leaving it untreated for a long time exacerbates the symptoms.

[0008] Among these sleep disorders, in particular, bruxism during sleep is commonly reported as a type of sleep disorder that adversely affects teeth and jaw joints, and it is reported that about 10% of adults and 15 to 30% of children experience bruxism.

[0009] Therefore, there is a need for research on an electromyography analysis system and method for self-sleep testing capable of monitoring a patient's teeth grinding habit during sleep. Prior art literature

[0010] Korean Published Patent Application No. 10-2017-0083483 Korean Published Patent Application No. 10-2024-0041503 The problem to be solved

[0011] The present invention aims to provide an electromyography analysis system and method for self-sleep examination that enables more accurate diagnosis of bruxism symptoms by minimizing the sense of unfamiliarity in the sleep environment and obtaining reliable data through the repeated collection of electromyography data using an electromyograph provided on a wearable device at home by a user.

[0012] The problems that the present invention aims to solve are not limited to those mentioned above, and other problems that the present invention aims to solve that are not mentioned herein will be clearly understood by those skilled in the art to which the present invention belongs from the description below. means of solving the problem

[0013] An electromyography analysis system for self-sleep testing according to one embodiment of the present invention includes a data collection unit that collects electromyography data from a designated wearable device, a data generation unit that generates monitoring data based on the electromyography data collected from the data collection unit, and a data analysis unit that generates analysis result data based on the monitoring data generated by the monitoring data generation unit.

[0014] Additionally, the data collection unit is characterized by including a battery level checking unit that checks the remaining battery level of the wearable device, a measurement start control unit that transmits measurement guide data to a pre-designated user terminal connected to the wearable device and receives a measurement start signal from the user terminal when the remaining battery level of the wearable device collected through the battery level checking unit is greater than or equal to a preset reference capacity, a measurement end control unit that receives a measurement end signal from the user terminal, and a data storage unit that stores electromyography data measured from the time the measurement start signal is received to the time the measurement end signal is received in a memory device provided in the wearable device when the measurement end signal is received.

[0015] In addition, the data storage unit counts the number of times electromyography data is collected through a wearable device, and if the number of times electromyography data is collected is a preset number of measurements, transmits wearable device return guide and hospital reservation guide information to the user terminal.

[0016] In addition, the data generation unit aggregates electromyography data collected from the data collection unit to generate monitoring data, wherein the monitoring data is characterized by including sleep time, the number of times bruxism occurs during sleep, the duration of bruxism, and the intensity of bruxism.

[0017] In addition, the data analysis unit is characterized by including a data parsing unit that scans monitoring data and parses electromyography data to generate parsed data, a data conversion unit that converts the parsed data generated by the data parsing unit from a hexadecimal data format to a digital data format, and a statistical analysis unit that removes noise from the parsed data converted to a digital data format by the data conversion unit and performs statistical analysis to generate analysis result data. Effects of the invention

[0018] The electromyography analysis system and method for self-sleep examination according to the present invention have the effect of enabling more accurate diagnosis of bruxism symptoms by minimizing the sense of unfamiliarity in the sleep environment and obtaining highly reliable data by repeatedly collecting electromyography data using an electromyograph provided on a wearable device at home. Brief explanation of the drawing

[0019] FIG. 1 is a configuration diagram of an electromyography analysis system for self-sleep testing according to an embodiment of the present invention. FIGS. 2 and FIGS. 3 are drawings for explaining the data collection unit of an electromyography analysis system for self-sleep testing according to an embodiment of the present invention. FIGS. 4 to 12 are drawings for explaining the data analysis unit of an electromyography analysis system for self-sleep testing according to an embodiment of the present invention. Specific details for implementing the invention

[0020] Specific details regarding the problem to be solved, the means for solving the problem, and the effects of the invention as described above are included in the embodiments and drawings to be described below. The advantages and features of the present invention, and the methods for achieving them, will become clear by referring to the embodiments described below in detail together with the accompanying drawings.

[0021] Hereinafter, the present invention will be described in more detail with reference to the attached drawings.

[0022] FIG. 1 is a configuration diagram of an electromyography analysis system for self-sleep testing according to an embodiment of the present invention, FIG. 2 and FIG. 3 are drawings for explaining a data collection unit of an electromyography analysis system for self-sleep testing according to an embodiment of the present invention, and FIG. 4 to FIG. 12 are drawings for explaining a data analysis unit of an electromyography analysis system for self-sleep testing according to an embodiment of the present invention.

[0024] Referring to FIG. 1, the electromyography analysis system (100) for self-sleep testing may include a data collection unit (110), a data generation unit (120), and a data analysis unit (130).

[0026] More specifically, the data collection unit (110) collects electromyography data from a designated wearable device (10), the data generation unit (120) generates monitoring data based on the electromyography data collected from the data collection unit (110), and the data analysis unit (130) can generate analysis result data based on the monitoring data generated by the data generation unit (120).

[0028] For example, the wearable device (10) is configured to be attached to both temples of the user to collect temporalis muscle electromyography data, and may include a gel pad on the attachment surface to facilitate attachment to the user's temples.

[0029] In addition, electromyography data of the user can be collected using an electromyograph provided in the wearable device (10), and the electromyography data collected through the electromyograph can be stored in a memory device provided in the wearable device (10).

[0031] For example, the user may visit a hospital to examine symptoms such as teeth grinding during sleep, undergo a consultation regarding sleep and dental conditions, receive the wearable device (10), and return home, and a self-sleep test may be performed by the user sleeping with the wearable device (10) attached during a preset measurement period (e.g., 3 days).

[0032] At this time, the user's electromyogram data during sleep is stored in the memory device of the wearable device (10), and the data generation unit (120) and the data analysis unit (130) can generate monitoring data for determining teeth grinding symptoms based on the electromyogram data stored in the wearable device (10).

[0034] Meanwhile, as illustrated in FIG. 2, the data collection unit (110) may include a battery level checking unit (111) for checking the remaining battery level of the wearable device (10), a measurement start control unit (112) for transmitting measurement guide data to a pre-designated user terminal (20) connected to the wearable device (10) and receiving a measurement start signal from the user terminal (20) when the remaining battery level of the wearable device (10) collected through the battery level checking unit (111) is greater than or equal to a preset reference capacity, a measurement end control unit (113) for receiving a measurement end signal from the user terminal (20), and a data storage unit (114) for storing the electromyography data measured from the time the measurement start signal was received until the time the measurement end signal was received in a memory device provided in the wearable device (10) when the measurement end signal is received.

[0036] For example, when the power of the wearable device (10) is turned on, a pairing process can be performed at least once by running a dedicated application installed on the user terminal (20) to establish a connection between the wearable device (10) and the user terminal (20).

[0037] In addition, when the above pairing process is completed, the battery level checking unit (111) can receive battery level information of the wearable device (10) from the wearable device (10) and display it on the user terminal (20).

[0038] At this time, if the battery remaining amount information received through the battery remaining amount checking unit (111) is less than a preset reference capacity, the battery remaining amount checking unit (111) can transmit a notification signal to the user terminal (20) requesting charging of the wearable device (10).

[0039] For example, the above reference capacity may be determined based on the condition that electromyogram data can be collected for 8 hours through the wearable device (10).

[0041] Additionally, as illustrated in FIG. 3, if the battery remaining capacity information received through the battery remaining capacity checking unit (111) is greater than or equal to the reference capacity, the measurement start control unit (112) can transmit guide information (310) for measuring electromyography data during sleep to the user terminal (20).

[0042] The user may be guided to attach the wearable device (10) to a recommended location (e.g., temples, etc.) based on the guide information (310) and to perform a test to see if electromyography data is collected normally.

[0043] For example, to test whether the above electromyography data is collected normally, the battery level check unit (111) may receive a test request signal (320) from the user terminal (20).

[0044] When a test request signal is received through the user terminal (20), the battery level check unit (111) requests the user terminal (20) to perform teeth clenching for a preset test time (e.g., 3 seconds), and can check whether electromyography data is performed normally without interruption through the electromyograph provided in the wearable device (10) in response to the user's teeth clenching motion during the test time.

[0046] When it is confirmed whether electromyography data is being collected normally through the wearable device (10), the measurement start control unit (112) can receive a measurement start signal through the user terminal (20).

[0047] When the above measurement start signal is received, the measurement start control unit (112) can collect real-time electromyography data collected through the wearable device (10).

[0049] Additionally, when the measurement termination signal is received through the user terminal (10), the measurement termination control unit (113) can terminate the collection of electromyography data through the wearable device (10).

[0050] Accordingly, the data storage unit (114) can store the electromyogram data collected from the time the measurement start signal is received until the time the measurement end signal is received in a memory device provided in the wearable device (10).

[0052] At this time, the data storage unit (114) counts the number of times the electromyogram data is collected through the wearable device (10), and if the number of times the electromyogram data is collected is a preset number of measurements, it can transmit the wearable device return guide and hospital reservation guide information to the user terminal (20).

[0053] For example, if the number of times the electromyography data is collected is 3, the data storage unit (114) can transmit the wearable device return guide and hospital reservation guide information to the user terminal (20).

[0055] That is, electromyography data during sleep of a user wearing the wearable device (10) can be collected through the wearable device for three days, and based on the collected electromyography data, a request to visit a hospital can be made to the user terminal (20) to conduct a medical examination regarding whether the user has teeth grinding symptoms and the results of the teeth grinding symptoms.

[0057] For example, even if the number of times the electromyogram data is collected is 3 times, the data storage unit (114) may transmit a signal requesting additional collection of the electromyogram data to the user terminal (20) if the time at which the electromyogram data is collected is less than a preset minimum measurement time (e.g., 20 hours).

[0059] Meanwhile, the data generation unit (120) generates the monitoring data by combining the electromyogram data collected from the data collection unit (110), wherein the monitoring data may include integrated bruxism monitoring data generated corresponding to the entire measurement period and bruxism monitoring data generated corresponding to the measurement time of the measurement session.

[0060] At this time, the monitoring data may be stored in a memory device provided in the wearable device (10).

[0062] Meanwhile, as illustrated in FIG. 4, the data analysis unit (130) may include a data parsing unit (131) that scans the monitoring data and parses the electromyography data to generate parsed data, a data conversion unit (132) that converts the parsed data generated by the data parsing unit (131) from a hexadecimal data format to a digital data format, and a statistical analysis unit (133) that removes noise from the parsed data converted to the digital data format by the data conversion unit (132) and performs statistical analysis to generate analysis result data.

[0063] For example, as illustrated in FIG. 5, the electromyogram data collected through the wearable device (10) is collected in an ADC 12-bit format and can be stored in the data storage unit (114) in the form of a hexadecimal data protocol including a head and a tail.

[0065] The data parsing unit (131) parses the electromyogram data from the monitoring data, and can parse the counter data included in the hexadecimal monitoring data and the electromyogram data EMG0 to EMG3 (510).

[0066] Additionally, as illustrated in FIG. 6, four electromyogram data are extracted in parallel for one counter data through the data parsing unit (131), and the data conversion unit (132) can input the extracted data into a preset data frame to generate serialized data.

[0067] More specifically, the range of the above electromyogram data, EMG0 to EMG3, is 2 12 It can be represented as a digital signal range of 0 to 4095, from -1.

[0068] Subsequently, as illustrated in FIG. 7, the data conversion unit (132) can generate a timestamp based on the measurement time of the serialized electromyography data.

[0069] At this time, the timestamp can be generated in msec units from the time the measurement of the electromyogram data started until the time the measurement ended.

[0071] Meanwhile, the statistical analysis unit (133) can remove noise included in the electromyography data.

[0072] More specifically, with reference to FIGS. 8 and 9, the statistical analysis unit (133) can calculate the average value, standard deviation, maximum value, minimum value, and data distribution shape (810, 910) of the electromyogram data, and remove noise (820, 920) by replacing outliers included in the electromyogram data with the average value or by cutting out the section containing the outliers.

[0073] For example, the statistical analysis unit (133) can determine the effective frequency range of the electromyogram data based on the following [Equation 1].

[0075] [Mathematical Formula 1]

[0076]

[0077] Here, Frequency Range is the effective frequency range, and EMG avg is the average value of the electromyography data, and EMG std is the standard deviation of the electromyography data, and h represents the filter constant.

[0078] At this time, the filter constant h can be determined by a pre-designated system manager.

[0080] For example, the effective frequency range calculated based on the electromyogram data (810) before the noise is removed can be determined to be in the range of 1211 (2066-50*17.1) to 2921 (2066+50*17.1) when the filter constant h is 50.

[0081] Therefore, sections outside the above effective frequency range are determined to be outlier sections, and noise can be removed by either the moving average method or the average imputation method.

[0082] In addition, the average value, standard deviation, maximum value, minimum value, and data distribution shape can be recalculated (820) for the electromyography data from which the noise has been removed to finally produce the analysis result data.

[0084] Accordingly, the data analysis unit (130) can calculate the sleep time, the number of times teeth grinding occurs during sleep, the duration of teeth grinding, and the intensity of teeth grinding based on the analysis result data.

[0085] For example, the data analysis unit (130) determines whether bruxism symptoms occur by creating a marker in an area where the electromyogram data included in the analysis result data exceeds a preset threshold, and if one or more markers are created, it determines that bruxism symptoms have occurred and can count the number of occurrences of bruxism symptoms based on the number of markers.

[0086] In addition, the intensity of the bruxism symptoms can be determined based on the rate at which the electromyogram data exceeds the threshold, and the duration of the bruxism can be determined based on the time at which the electromyogram data continuously exceeds the threshold.

[0087] Accordingly, as illustrated in FIGS. 10 and 11, the data analysis unit (130) can generate report data (1010, 1020) by compiling analysis results including sleep time, number of times teeth grinding occurs during sleep, duration of teeth grinding, and intensity of teeth grinding, based on the analysis result data.

[0088] For example, the above report data may include user identification information, bedtime, sleep duration, wake-up time, number of muscle activations, and results of electromyography data analysis.

[0090] Referring to FIG. 12, the data analysis process of the data analysis unit (130) can be such that the data parsing unit (131) calls the monitoring data stored in the wearable device (10) (S1211), and selects an analysis target and data (S1212) from the called monitoring data.

[0091] For example, the process of selecting the analysis target and data may involve selecting all or part of the monitoring data collected in the wearable device (10).

[0092] Additionally, when an analysis target and data are selected from the monitoring data by the data parsing unit (131), the protocol of the monitoring data is scanned (S1213), and parsing data including counter data and electromyography data is generated (S1214) from the monitoring data.

[0093] When the parsing data is generated through the data parsing unit (131), the data conversion unit (132) serializes the parsing data and converts it into a digital data format (S1215), and checks the length of the parsing data to generate a time stamp (S1216) based on the time at which the electromyography data included in the parsing data was measured.

[0094] When the parsing data is converted into a preset data frame by the data conversion unit (132), the basic statistical analysis (S127) of the parsing data can be performed through the statistical analysis unit (133).

[0095] Based on the results of the basic statistical analysis of the parsing data, the statistical analysis unit (133) can determine the valid data area to detect outlier sections and remove noise (S1218).

[0096] When the above noise removal is completed, the statistical analysis unit (133) can recalculate basic statistics (S1219) for the parsing data from which the noise has been removed.

[0097] In addition, the statistical analysis unit (133) can generate a muscle activity analysis result (S1220) based on the recalculated basic statistics and generate a visual image (S1221) based on the muscle activity analysis result.

[0098] Accordingly, the statistical analysis unit (133) can generate a report (S1222) including the results of the muscle activity analysis and visual images.

[0100] According to the present invention as described above, by repeatedly collecting electromyogram data using an electromyograph provided in a wearable device at home, the user can minimize the sense of unfamiliarity with the sleep environment and obtain highly reliable data, thereby enabling a more accurate diagnosis of bruxism symptoms. Therefore, an electromyogram analysis system and method for self-sleep examination can be provided.

[0102] In addition, a control method for an electromyography analysis system for self-sleep testing according to an embodiment of the present invention may be recorded on a computer-readable medium containing program instructions for performing operations implemented by various computers. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions on the medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

[0104] As described above, although an embodiment of the present invention has been explained by limited embodiments and drawings, the embodiment of the present invention is not limited to the embodiments described above, and various modifications and variations are possible from this description by those skilled in the art to which the present invention pertains. Accordingly, an embodiment of the present invention should be understood only by the claims described below, and all equivalent or analogous variations thereof shall be considered to be within the scope of the inventive concept. Explanation of the symbols

[0106] 10 : Wearable devices 20 : User terminal 100: Electromyography analysis system for self-sleep testing 110: Data Collection Unit 111 : Battery level check section 112: Measurement start control unit 113: Measurement termination control unit 114 : Data storage unit 120 : Data generation section 130 : Data Analysis Department 131 : Data parsing section 132 : Data conversion section 133 : Statistical Analysis Department

Claims

Claim 1 An electromyography analysis system for self-sleep testing comprising: a data collection unit for collecting electromyography data from a designated wearable device; a data generation unit for generating monitoring data based on the electromyography data collected from the data collection unit; and a data analysis unit for generating analysis result data based on the monitoring data generated by the monitoring data generation unit. Claim 2 The electromyography analysis system for self-sleep testing according to claim 1, wherein the data collection unit comprises: a battery level checking unit that checks the remaining battery level of the wearable device; a measurement start control unit that, when the remaining battery level of the wearable device collected through the battery level checking unit is greater than or equal to a preset reference capacity, transmits measurement guide data to a preset user terminal connected to the wearable device and receives a measurement start signal from the user terminal; a measurement end control unit that receives a measurement end signal from the user terminal; and a data storage unit that, when the measurement end signal is received, stores the electromyography data measured from the time the measurement start signal is received to the time the measurement end signal is received in a memory device provided in the wearable device. Claim 3 An electromyography analysis system for self-sleep testing according to claim 2, wherein the data storage unit counts the number of times the electromyography data is collected through the wearable device, and when the number of times the electromyography data is collected is a preset number of measurements, transmits the wearable device return guide and hospital reservation guide information to the user terminal. Claim 4 An electromyography analysis system for self-sleep testing according to claim 1, wherein the data generation unit aggregates the electromyography data collected from the data collection unit to generate the monitoring data, and the monitoring data includes sleep time, the number of times bruxism occurs during sleep, the duration of bruxism, and the intensity of bruxism. Claim 5 The electromyography analysis system for self-sleep examination according to claim 1, wherein the data analysis unit comprises: a data parsing unit that scans the monitoring data and parses the electromyography data to generate parsed data; a data conversion unit that converts the parsed data generated by the data parsing unit from a hexadecimal data format to a digital data format; and a statistical analysis unit that removes noise from the parsed data converted to the digital data format by the data conversion unit and performs statistical analysis to generate analysis result data.

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