Electronic device, method, and non-transitory storage medium for identifying sleep disorder

Abstract

The present document relates to an electronic device, a method, and a non-transitory storage medium for identifying a sleep disorder. According to one embodiment, the electronic device may: acquire first peak signals having a user motion component on the basis of sensing information acquired by an acceleration sensor included in a sensor circuit of the electronic device in relation to periodic limb movements of sleep (PLMS) while a user is sleeping; acquire second peak signals on the basis of first peak signals which satisfy a designated PLMS condition from among the first peak signals; acquire third peak signals by performing interpolation between the second peak signals acquired at designated intervals during a total sleep time; and identify whether there is a PLMS disorder on the basis of third peak signals identified in designated intervals other than a designated interval corresponding to a REM sleep stage from among the third peak signals acquired during the total sleep time. Other embodiments are also possible.

Description

Electronic devices, methods and non-transitory storage media for identifying sleep disorders

[0001] The present disclosure relates to an electronic device, method and non-transitory storage medium for identifying sleep disorders.

[0002] Recently, electronic devices have been developed in various forms for the convenience of users and are becoming smaller so that users can conveniently carry them.

[0003] Interest in health has been growing recently, and exercise as a means of maintaining good health is also on the rise. Accordingly, electronic devices are evolving into diverse forms capable of measuring and utilizing various biosignals and movements of the human body. They are also providing various services for sleep management by monitoring sleep and physiological conditions through at least one sensor.

[0004] People spend a third of their lives sleeping. Sleep deprivation can lead to decreased concentration and memory, and prolonged periods of insomnia can lead to emotional instability and hallucinations. Therefore, sleep plays a crucial role in a healthy lifestyle, and managing it is crucial.

[0005] Various sleep disorders can occur during sleep, including periodic limb movement disorder (PLMS). PLMS used to require a visit to a hospital or sleep lab to be diagnosed. However, despite having a sleep disorder like PLMS, some individuals are unable to enter a sleep lab for various reasons, making it difficult to undergo testing. This has made it difficult to easily diagnose PLMS in the past.

[0006] According to one embodiment of the present disclosure, an electronic device may include a sensor circuit including an acceleration sensor, a memory storing instructions, and at least one processor.

[0007] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to acquire first peak signals having a user movement component based on sensing information acquired by the acceleration sensor related to periodic limb movement of sleep (PLMS) while the user is sleeping.

[0008] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to obtain second peak signals based on first peak signals that satisfy a specified PLMS condition among the first peak signals.

[0009] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to perform interpolation between second peak signals acquired at designated intervals during the total sleep time to acquire third peak signals.

[0010] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to determine whether a PLMS disease is present based on third peak signals identified in designated intervals remaining from among the third peak signals acquired during the total sleep time, excluding a designated interval corresponding to a REM sleep stage.

[0011] According to one embodiment, a method of operating in an electronic device may include obtaining sensing information related to periodic limb movement of sleep (PLMS) by an acceleration sensor included in a sensor circuit of the electronic device while the user is sleeping.

[0012] According to one embodiment, the method may include an operation of obtaining first peak signals having a user motion component based on the sensing information.

[0013] According to one embodiment, the method may include an operation of obtaining second peak signals based on first peak signals that satisfy a specified PLMS condition among the first peak signals.

[0014] According to one embodiment, the method may include an operation of obtaining third peak signals by performing interpolation between second peak signals obtained at specified intervals during the total sleep time.

[0015] According to one embodiment, the method may include an operation of determining whether a PLMS disease is present based on third peak signals identified in designated sections remaining from among the third peak signals acquired during the total sleep time, excluding a designated section corresponding to the REM sleep stage.

[0016] According to one embodiment, in a non-transitory storage medium storing a program, the program may include executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform an operation of acquiring sensing information related to periodic limb movement of sleep (PLMS) by the acceleration sensor while the user sleeps, an operation of acquiring first peak signals having a user movement component based on the sensing information, an operation of acquiring second peak signals based on first peak signals satisfying a specified PLMS condition among the first peak signals, an operation of performing interpolation between second peak signals acquired at each specified section during a total sleep time to acquire third peak signals, and an operation of determining whether a PLMS disease exists based on third peak signals identified in designated sections remaining except for a designated section corresponding to a REM sleep stage among the third peak signals acquired during a total sleep time.

[0017] FIG. 1 is a block diagram of an electronic device within a network environment according to various embodiments.

[0018] FIG. 2 is a drawing showing an example configuration of an electronic device according to one embodiment.

[0019] FIGS. 3A and 3B are diagrams showing an example configuration of an electronic device according to one embodiment.

[0020] FIGS. 4A and 4B are diagrams illustrating examples for identifying a disease for periodic limb movement disorder in an electronic device according to one embodiment.

[0021] FIG. 5 is a diagram illustrating an example for identifying a disease for periodic limb movement disorder in an electronic device according to one embodiment.

[0022] FIG. 6 is a diagram illustrating an example for identifying a disease for periodic limb movement disorder in an electronic device according to one embodiment.

[0023] FIG. 7 is a diagram illustrating an example for identifying a disease for periodic limb movement disorder in an electronic device according to one embodiment.

[0024] FIG. 8 is a drawing showing an example of an operating method in an electronic device according to one embodiment.

[0025] FIG. 9 is a drawing showing an example of an operating method in an electronic device according to one embodiment.

[0026] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0027] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components. In addition, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness. The term "user" used in the embodiments of the present disclosure may refer to a person using an electronic device or a device (e.g., an artificial intelligence electronic device) using an electronic device.

[0028] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments.

[0029] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0030] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor)) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0031] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0032] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0033] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0034] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0035] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0036] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. In one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0037] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0038] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0039] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0040] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0041] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0042] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0043] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least a part of a power management integrated circuit (PMIC).

[0044] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0045] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0046] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for realizing 1eMBB, a loss coverage (e.g., 164 dB or less) for realizing mMTC, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for realizing URLLC.

[0047] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas by, for example, the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0048] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0049] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0050] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0051] FIG. 2 is a diagram showing an example of a configuration of an electronic device according to one embodiment, and FIGS. 3a and 3b are diagrams showing an example of a configuration of an electronic device according to one embodiment.

[0052] Referring to FIGS. 2, 3A, and 3B, an electronic device (201) according to an embodiment (e.g., the electronic device (101) or the electronic device (102 or 104) of FIG. 1) may be configured to include at least one processor (210), a memory (220), a sensor circuit (230), a communication circuit (240), a display (250), and an electrode circuit (260). The electronic device (201) is not limited thereto and may further include various components or may be configured by excluding some of the components. The electronic device (201) according to an embodiment may be, for example, a mobile device carried by a user, a wearable device that can be worn on a user's body (e.g., a smart watch, a smart band, or a smart ring).

[0053] Referring to FIGS. 3A and 3B , according to one embodiment, the electronic device (201) may be, for example, a wearable device in the form of a wristwatch that can be worn on a user's wrist, or a wearable device (e.g., a ring-shaped device) that can be worn on another part of the human body (e.g., the head, hand, forearm, thigh, or another part of the human body that can measure bio-signals related to sleep). For another example, the electronic device (201) may be another type of user device.

[0054] Referring to FIG. 3A, an electronic device (201) according to one embodiment may have an electrode circuit (260) including at least one electrode disposed on a first surface (e.g., a back surface) and a third surface (e.g., a side surface) of a housing (301). The electronic device (201) may have a display (250) disposed on a second surface (e.g., a front surface) of the other surface of the housing (301). The electronic device (201) may be configured such that at least one sensor is disposed on the first surface to be in contact with or in proximity to the skin of a human body. The at least one sensor may be included in a sensor circuit (230). For example, the at least one sensor (231) may be a sensor capable of measuring a biosignal.

[0055] Referring to FIG. 3B, an electronic device (201) according to one embodiment may be a ring-shaped wearable device, and may include an outer ring member (311), an inner ring member (313) disposed along an inner surface of the outer ring member (311), a cover member (315) disposed along an outer surface of the outer ring member (311), and a circuit unit (317) installed in the outer ring member (311). The circuit unit (317) may include an electrode circuit (e.g., the electrode circuit (260) of FIG. 2) composed of at least three electrodes (317a, 317b, and 317c). The outer ring member (311) may have an inner diameter that can be fitted onto a user's finger. The outer ring member (311) may be manufactured in various sizes to correspond to various ages, genders, etc., taking into account various finger thicknesses of multiple users.

[0056] The electronic device (201) according to the present embodiments may be configured in various forms in addition to the forms illustrated in FIGS. 3a and 3b.

[0057] FIGS. 4A and 4B are diagrams illustrating examples of a method for identifying a disease for periodic limb movement disorder in an electronic device according to an embodiment. FIGS. 5 to 7 are diagrams illustrating examples of a method for identifying a disease for periodic limb movement disorder in an electronic device according to an embodiment.

[0058] Referring to FIGS. 2 to 7, according to one embodiment, the processor (210) of the electronic device (201) (e.g., the processor (120) of FIG. 1) may obtain sleep-related information using at least one sensor included in the sensor circuit (230). According to one embodiment, the processor (120) may obtain sleep-related information while the user sleeps and store the obtained sleep-related information in the memory (220). According to one embodiment, the processor (210) may obtain sensing information (401) related to periodic limb movement of sleep (PLMS) using the acceleration sensor included in the sensor circuit (230) while the user sleeps. The sensing information (401) related to PLMS may be sleep-related information related to movements (e.g., regular and brief muscle activation) of the user's limbs (e.g., arms and / or legs). For another example, the processor (210) may receive information detected from an external electronic device as sleep-related information via the communication circuit (240). The sleep-related information may include user movement information acquired while the user is sleeping and biometric information detected from the user. Here, the biometric information may include heart rate (HR) and heart rate variability (HRV). In addition, the biometric information may include electrocardiogram (ECG) information and / or various biometric signals that can determine the user's sleep state.

[0059] According to one embodiment, the processor (210) may obtain movement information of the user before the user falls asleep, and automatically detect whether the user is in the sleep phase (e.g., sleep onset) based on the obtained movement information. The processor (210) may automatically detect whether the user is awake (e.g., sleep termination) based on movement information of the user obtained while the user is sleeping. According to one embodiment, the processor (210) may continuously obtain sensing information (e.g., ACC_raw) (401) related to periodic limb movement disorder during the total sleep time between the start and end of sleep. Sleep may be repeated as one sleep cycle classified into four stages during the entire sleep time. Sleep stages during a sleep cycle can be classified into, for example, the first sleep stage (awake stage) representing wakefulness during sleep, the second sleep stage representing light non-REM (non-rapid-eye-movement) sleep, the third sleep stage representing deep non-REM (non-REM) sleep, and the fourth sleep stage (slow wave sleep stage) representing rapid-eye-movement (REM) sleep.

[0060] According to one embodiment, the processor (210) may obtain first peak signals (ACC_or) having a user movement component based on sensing information (ACC_raw) detected using an acceleration sensor. According to one embodiment, when the processor (210) identifies the onset of sleep, it may obtain sensing information (ACC_raw) detected using a speed sensor, and shift the first peak signals (ACC_or) having a user movement component by, for example, 1 second every first time (for example, 10 seconds) in a specified section (for example, approximately 10 minutes as a section length). As illustrated in FIG. 4B, the sensing information (401) may include first raw data (ACC_raw_x) (410) of a first axis (e.g., x-axis), second raw data (ACC_raw_y) (420) of a second axis (e.g., y-axis), and third raw data (ACC_raw_z) (430) of a third axis (e.g., z-axis). Here, the designated section is a section length for acquiring first peak signals (ACC_or) having a user motion component in sections where PLMS occurs intensively, and the section length may be designated as a pre-designated time (e.g., approximately 10 minutes). For example, the sections where PLMS occurs intensively may be represented as sections 1 to 3 of the graph illustrated in FIG. 7, which may be examples configured for convenience of explanation, and may be represented in other forms (or methods).

[0061] According to one embodiment, the processor (210) may classify the sensing information (ACC_raw) acquired using the acceleration sensor into first raw data (ACC_raw_x) (410) of a first axis (e.g., x-axis), second raw data (ACC_raw_y) (420) of a second axis (e.g., y-axis), and third raw data (ACC_raw_z) (430) of a third axis (e.g., z-axis). The processor (210) obtains filtered raw data (ACC_filt_x, ACC_filt_y, ACC_filt_z) of each axis by passing the raw data (410, 420, 430) of each axis through a band pass filter (e.g., a band pass filter having a cutoff frequency of 0.5 to 5 Hz) (501), and obtains an RMS value (ACC_rms value) by applying a root mean square (RMS) (503) to the obtained filtered raw data of each axis (e.g., calculating the filtered raw data of 1 second in length by shifting it by 0.04 seconds).

[0062] According to one embodiment, the processor (210) can check the values ​​of the motion components (ACC_move_x, ACC_move_y, and ACC_move_z) of each axis based on a pre-specified condition for PLMS in raw data included in the sensing information (e.g., root mean square values ​​(ACC_rms_x, ACC_rms_y, and ACC_rms_z) of each axis). The processor (210) can check the values ​​of the motion components of each axis by shifting the root mean square values ​​(ACC_rms_x, ACC_rms_y, and ACC_rms_z) of each axis by 1 second every first time (e.g., 10 seconds), and can obtain (e.g., extract or select) the first peak signals (ACC_or) (510) in a specified section based on the values ​​of the motion components of each axis that have been confirmed.

[0063] According to one embodiment, the processor (210) may calculate (e.g., obtain) a variance for the root mean square values ​​(ACC_rms_x, ACC_rms_y, and ACC_rms_z) of each axis acquired during a first period of time (e.g., 10 seconds), and determine whether the calculated variance is greater than a variance threshold value (variance_threshold) (505). If the calculated variance is greater than the variance threshold value, the processor (210) may maintain a previously set resting level value, and if the calculated variance is less than or equal to the variance threshold value, the processor (210) may set the resting level value as an average of data detected during the first period of time (e.g., 10 seconds). For example, the processor (210) may repeatedly perform the setting of the resting level value by shifting the time axis by 1 second. The processor (210) may set a threshold for a first condition for identifying a motion component using the resting level.

[0064] According to one embodiment, the processor (210) can check (e.g., detect, acquire, or identify) the value (acc_move = 1) of the motion components (ACC_move_x, ACC_move_y, and ACC_move_z) of each axis that satisfy a predefined condition for PLMS in the raw data (e.g., the root mean square values ​​(ACC_rms_x, ACC_rms_y, and ACC_rms_z) of each axis) included in the sensing information. According to one embodiment, the predefined condition for PLMS is a first condition in which the data (e.g., the root mean square values ​​(ACC_rms_x, ACC_rms_y, and ACC_rms_z) of each axis) detected for a first time (e.g., 10 seconds) is greater than a threshold value obtained by adding a threshold margin (e.g., a pre-calculated value of 0.0145) to the resting level, a second condition in which the sleep stage is not wake, and the sleep state is normal. A third condition may be included, which is a state other than sleep apnea or sleep hypopnea.

[0065] According to one embodiment, the processor (210) can confirm (e.g., identify) that the raw data (410, 420, 430) included in the sensing information has a motion component (acc_move = 1) if the raw data (410, 420, 430) included in the sensing information satisfies a pre-specified condition for PLMS. The processor (210) can set the values ​​of the motion components (ACC_move_x, ACC_move_y, and ACC_move_z) of each axis to a value of 1, indicating that the motion components are related to PLMS, if the first condition, the second condition, and the third condition are all satisfied for the root mean square values ​​(ACC_rms_x, ACC_rms_y, and ACC_rms_z) of each axis, respectively.

[0066] According to one embodiment, the processor (210) may determine that the detected raw data included in the sensing information (e.g., the root mean square values ​​of each axis (ACC_rms_x, ACC_rms_y, and ACC_rms_z)) does not have a motion component (acc_move = 0) if the pre-specified condition for PLMS is not satisfied.

[0067] According to one embodiment, if the root mean square values ​​(ACC_rms_x, ACC_rms_y and ACC_rms_z) of each axis do not satisfy any one of the first condition, the second condition or the third condition, the processor (210) may set the values ​​of the motion components (ACC_move_x, ACC_move_y and ACC_move_z) of each axis to a value of 0 indicating that the motion components are not related to PLMS.

[0068] According to one embodiment, the processor (210) may obtain a value (e.g., 1) of raw data for obtaining a first peak signal (ACC_or) (510) by shifting the values ​​of the motion components (ACC_move_x, ACC_move_y, and ACC_move_z) of each axis by 1 second for a first time period using an OR gate (507), and may obtain (e.g., generate) the first peak signal (ACC_or) (510) having a motion component based on the value of the obtained raw data. The processor (210) may obtain first peak signals having a motion component during a specified period by repeatedly performing an operation for obtaining the first peak signal (ACC_or) (510) as illustrated in FIG. 5 every first time period (e.g., 10 seconds).

[0069] According to one embodiment, the processor (210) checks the interval between the first peak signals (ACC_or) acquired during a specified period, and if there is an interval shorter than a specified interval (e.g., 1.5 seconds) among the intervals confirmed in the specified period, the processor (210) can merge the first peak signals (611, 613) between the short intervals into one signal to acquire corrected first peak signals (ACC_merge) (620) (e.g., correct the short interval to 1).

[0070] According to one embodiment, the processor (210) can identify (e.g., confirm, detect, or select) first peak signals corresponding to a specified condition of PLMS among the first peak signals, and obtain (e.g., set, designate, or generate) second peak signals (ACC_plms) (440) in a specified section based on the identified first peak signals.

[0071] According to one embodiment, the processor (210) can select first peak signals that satisfy a specified condition from among the corrected first peak signals (ACC_merge) (620) of a specified section. The processor (210) can maintain (e.g., maintain a value of 1) at least one first peak signal that satisfies a condition that a signal generation time (e.g., time length (x)) is within a first specified time range (0.5 seconds to 10 seconds) (e.g., longer than 0.5 seconds and shorter than 10 seconds) from among the corrected first peak signals (ACC_merge) (620), and remove (e.g., set a value of 0) at least one first peak signal that does not satisfy the condition that it is within the first specified time range (0.5 seconds to 10 seconds). The processor (210) can select first peak signals that satisfy a condition that an interval of the first peak signals (ACC_cut) maintained in the specified section is less than or equal to a second specified time range (5 seconds to 90 seconds). The processor (210) can obtain the selected first peak signals that are repeated consecutively a specified number of times (e.g., 4 times) or more in a specified section as second peak signals (ACC_plms) (440) having a value of 1. The processor (210) can remove (e.g., set to a value of 0) at least one remaining selected first peak signal that is not repeated consecutively a specified number of times (e.g., 4 times) or more in a specified section.

[0072] According to one embodiment, the processor (210) may identify signals related to apneas and / or hypopneas as false detection signals based on information related to sleep, and may obtain (e.g., count) the number of occurrences of false detection signals in a designated section. The processor (210) may identify false detection signals in each designated section. The processor (210) may remove second peak signals in the designated section (e.g., set ACC_plms_RRLM = 0) based on the occurrence of false detection signals exceeding a designated number of times (e.g., a count value of 5). The signals related to apneas and / or hypopneas may occur when a respiratory event occurs in which blood oxygen saturation decreases to a certain level (e.g., 3% or more), and may be counted, for example, when the event lasts for 10 seconds.

[0073] According to one embodiment, the processor (210) may perform interpolation between second peak signals for all sections in units of designated sections from the total sleep time, and then obtain (e.g., set, designate, or generate) final third peak signals for determining whether a PLMS disease exists. According to one embodiment, the processor (210) may repeatedly perform an operation of obtaining first peak signals in units of designated sections, obtaining second peak signals based on the first peak signals, for the total sleep time, and identifying second peak signals suitable for determining whether a PLMS disease exists among the second peak signals to obtain final third peak signals, for each designated section.

[0074] According to one embodiment, the processor (210) can remove false detection signals from a designated section, check the intervals between the remaining second peak signals, and, based on some of the identified intervals being greater than a designated interval (1.5 times the median value), add virtual peak signals to the some of the intervals to perform interpolation between the second peak signals. The processor (210) can check the number of intervals (N_interp=round(plms_inverval[i] / median(plms_interval))+1)) that are identified as being greater than a designated interval (1.5 times the median value), and generate virtual peak signals (e.g., virtual PLMS data) as many as the identified number (N_interp). The processor (210) can maintain the intervals of the second peak signals identified as being less than a designated interval (1.5 times the median value).

[0075] According to one embodiment, the processor (210) can determine whether the interval (e.g., time interval) between second peak signals in all intervals including second peak signals during the total sleep time is outside a third designated range time (12 seconds to 90 seconds), which is a PLMS interval condition. If the interval between second peak signals is less than or equal to the third designated range time (12 seconds to 90 seconds), the processor (210) can maintain the second peak signals, and based on the interval between second peak signals being greater than the third designated range time (12 seconds to 90 seconds), can remove one of the second peak signals existing before and after the interval.

[0076] According to one embodiment, the processor (210) can identify a section corresponding to the REM sleep stage among all sections during the total sleep time, and obtain third peak signals (e.g., final peak signals (PLMS signals)) included in the remaining sections excluding the identified section. The processor (210) can determine whether PLMS disease exists based on the third peak signals included in the remaining sections. The processor (210) can determine whether PLMS disease exists using a measure of severity (e.g., periodic limb movement index (PLMI) defined as the average number of limb movements per sleep hour). The processor (210) can determine the number of third peak signals acquired during the total sleep time (e.g., a count value), calculate the PLMI by dividing the identified number by the total sleep time, and determine whether PLMS disease exists or the PLMS stage based on the calculated PLMI. For example, if the calculated PLMI is less than a designated first PLMI index (e.g., 5), the processor (210) can determine that the condition is normal as a first PLMS stage. If the calculated PLMI is less than a designated second PLMI index (e.g., 5 to 25), the processor (210) can determine that there is a mild PLMS disease as a second PLMS stage. If the calculated PLMI is greater than a designated third PLMI index (e.g., 25 to 50), the processor (210) can determine that there is a moderate PLMS disease as a third PLMS stage. If the calculated PLMI is greater than or equal to a designated fourth PLMI index (e.g., 50), the processor (210) can determine that there is a severe PLMS disease as a fourth PLMS stage.

[0077] According to one embodiment, the processor (210) may obtain result information for confirming whether a PLMS disease is present and control the display (250) to display the result information. The processor (210) may control the communication circuit (240) to transmit the result information to an external electronic device.

[0078] In one embodiment, the processor (210) can more accurately determine whether the user has PLMS disease by using the results obtained over a certain period of time (e.g., three days, one week, or one month). For example, if the user is confirmed to have a second PLMS condition or higher continuously or at a specified number of times over a certain period of time, the processor (210) can determine that the user has PLMS disease and provide (e.g., display, output, or transmit) guidance information related to PLMS disease and / or guidance information related to treatment.

[0079] According to one embodiment, the processor (210) may be a hardware component (function) or a software element (program) including at least one of various sensors, data measurement modules, input / output interfaces, modules for managing the status or environment of the electronic device (201), or communication modules, as a hardware module or a software module (e.g., an application program) provided in the electronic device (201).

[0080] According to one embodiment, the processor (210) may include, for example, one or a combination of two or more of hardware, software, or firmware. The processor (210) may omit at least some of the above components, or may be configured to further include other components for performing image processing operations in addition to the above components.

[0081] According to one embodiment, the memory (220) (e.g., the memory (130) of FIG. 1) can store applications. For example, the memory (220) can store sleep-related applications (functions or programs), exercise applications, or health management applications. The memory (220) can store sleep-related information (e.g., user movement information and / or biometric information) acquired through an external electronic device or at least one sensor, sleep state information, sensing information detected by an acceleration sensor related to PLMS, peak signals acquired to determine whether a PLMS disease exists, and result information for determining whether a PLMS disease exists.

[0082] According to one embodiment, the memory (220) can store various data generated during execution of the program (140), including a program used for functional operation (e.g., the program (140) of FIG. 1). The memory (220) may largely include a program area (140) and a data area (not shown). The program area (140) may store related program information for driving the electronic device (201), such as an operating system (OS) (e.g., the operating system (142) of FIG. 1) that boots the electronic device (201). The data area (not shown) may store transmitted and / or received data and generated data according to various embodiments. In addition, the memory (220) may be configured to include at least one storage medium among flash memory, a hard disk, a multimedia card micro type memory (e.g., a secure digital (SD) or extreme digital (XD) memory), RAM, and ROM.

[0083] According to one embodiment, the sensor circuit (230) (e.g., the sensor module (176) of FIG. 1) may include various sensors for detecting sleep-related information. For example, the sensor circuit (230) may include an acceleration sensor for detecting sensing information related to PLMS. The sensor circuit (230) may include at least one sensor for detecting the user's biometric information (e.g., a photoplethysmography (PPG) sensor and / or an electrocardiogram (ECG) sensor), at least one sensor for detecting the user's situation (e.g., an acceleration sensor, a proximity sensor, a gyro sensor, and / or a body temperature sensor) (231), and at least one sensor for detecting the user's external environment (e.g., a temperature sensor, a humidity sensor, an illuminance sensor, a camera sensor, a gas sensor, and / or a fine dust sensor). In addition, the sensor circuit (230) may include various sensors for detecting sleep-related information.

[0084] According to one embodiment, the communication circuit (240) (e.g., the communication module (190) of FIG. 1) can communicate with an external electronic device (e.g., the electronic device (101) of FIG. 1, the server (108) of FIG. 1, or an electronic device of another user). For example, the communication circuit (240) can transmit information on the result of confirming whether a PLMS disease is present to the external electronic device. The communication circuit (240) can receive and / or transmit sleep-related information (e.g., movement information, biometric information, and / or sensing information acquired in relation to PLMS) from and / or to the external electronic device. According to one embodiment, the communication circuit (240) can include a cellular module, a wireless-fidelity (Wi-Fi) module, a Bluetooth module, or a near field communication (NFC) module.

[0085] According to one embodiment, the display (250) (e.g., the display module (160) of FIG. 1) may display an execution screen of an application related to the user's sleep. The display (250) may, under the control of the processor (210), display information on the results of confirming whether the user has PLMS disease. The display (250) may, under the control of the processor (210), display history information including previously acquired PLMS-related information and / or previously acquired result information. The display (250) may, under the control of the processor (210), display guidance information related to PLMS disease.

[0086] According to one embodiment, the display (250) may be implemented in the form of a touch screen. When the display (250) is implemented in the form of a touch screen together with an input module, it may display various pieces of information generated according to a user's touch operation. According to one embodiment, the display (250) may be configured with at least one of a liquid crystal display (LCD), a thin film transistor LCD (TFT-LCD), an organic light emitting diode (OLED), a light emitting diode (LED), an active matrix organic LED (AMOLED), a flexible display, and a 3-dimensional display. In addition, some of these displays may be configured as transparent or light-transmitting so that the outside can be seen through them. This may be configured in the form of a transparent display including a TOLED (transparent OLED). According to another embodiment, in addition to the display (250), it may further include another display module (e.g., an extended display or a flexible display) mounted thereon.

[0087] According to one embodiment, the electrode circuit (260) may include at least one electrode that comes into contact with the user's human body. For example, the electrode circuit (260) may obtain sleep-related biometric information, such as electrocardiogram (ECG) information and bioelectrical impedance analysis (BIA) information, from the user's human body using the electrodes. According to one embodiment, the electronic device (201) may further include an audio circuit (e.g., an audio module (170) of FIG. 1) or a vibration circuit (e.g., a haptic module (179) of FIG. 1). The audio circuit may output sound and may be configured to include, for example, at least one of an audio codec, a microphone (MIC), a receiver, an earphone output (EAR_L), or a speaker. The audio circuit may output result information of confirming whether the user has PLMS disease and / or guidance information related to PLMS disease obtained based on the result information as an audio signal. For example, the vibration circuit can output, as vibration, result information for determining whether the user has PLMS disease and / or guidance information related to PLMS disease obtained based on the result information.

[0088] As such, in one embodiment, the main components of the electronic device have been described through the electronic device (201) of FIG. 2. However, in various embodiments, not all of the components illustrated through FIG. 2 are essential components, and the electronic device (201) may be implemented with more components than the illustrated components, or with fewer components. In addition, the positions of the main components of the electronic device (201) described above through FIG. 2 may be changed according to various embodiments.

[0089] Referring to FIGS. 1 and 2, an electronic device according to an embodiment (e.g., electronic device (101) of FIG. 1 and / or electronic device (201) of FIGS. 2, 3A, and 3B) may implement a sleep-related software module (e.g., program (140) of FIG. 1) for identifying PLMS disease. A memory of the electronic device (e.g., memory (130) of FIG. 1 and / or memory (220) of FIG. 2) may store commands (e.g., instructions) to implement the software module. At least one processor (e.g., processor (120) of FIG. 1 and / or processor (210) of FIG. 2) can execute instructions stored in a memory to implement a software module and control hardware associated with the function of the software module (e.g., sensor module (176) of FIG. 1 and / or sensor circuit (230) of FIG. 2, communication module (190) of FIG. 1 and / or communication circuit (240) of FIG. 2, display module (160) of FIG. 1 and / or display (250) of FIG. 2).

[0090] A software module of an electronic device according to an embodiment may be configured to include a kernel (or HAL), a framework (e.g., middleware (144) of FIG. 1), and an application (e.g., application (146) of FIG. 1). At least a portion of the software module may be preloaded on the electronic device (101) or downloadable from a server (e.g., server (108)).

[0091] According to one embodiment, the kernel may include, but is not limited to, a system resource manager or device driver, and may further include other modules. The system resource manager may perform control, allocation, or retrieval of system resources. The device driver may include, for example, a display driver, a camera driver, a Bluetooth driver, a shared memory driver, a USB driver, a keypad driver, a WIFI driver, an audio driver, or an inter-process communication (IPC) driver.

[0092] According to one embodiment, the framework may provide functions commonly required by applications or provide various functions to applications through an application programming interface (API) (not shown) so that the applications can efficiently utilize limited system resources within the electronic device. The framework may include modules that form a combination of various functions of the components described above in FIGS. 1 and 2. The framework may provide specialized modules for each type of operating system to provide differentiated functions. The framework may dynamically delete some existing components or add new components.

[0093] According to one embodiment, the application may be configured to include an application (e.g., a module, a manager, or a program) related to sleep. The application may include an application received from an external electronic device (e.g., a server (108) or an electronic device (102, 104)). According to one embodiment, the application may include a preloaded application or a third-party application downloadable from a server. The components and names of the components of the software module according to the illustrated embodiment may vary depending on the type of operating system. According to one embodiment, at least a portion of the software module may be implemented as software, firmware, hardware, or a combination of at least two or more thereof. At least a portion of the software module may be implemented (e.g., executed) by, for example, a processor (e.g., an AP). At least a portion of the software module may include, for example, a module, a program, a routine, a set of instructions, or a process for performing at least one function.

[0094] As such, in one embodiment, the main components of the electronic device have been described through the electronic device (101) of FIGS. 1 and 2. However, in various embodiments, not all of the components illustrated through FIGS. 1 and 2 are essential components, and the electronic device (101) may be implemented with more components than the illustrated components, or with fewer components. In addition, the positions of the main components of the electronic device (101) described above through FIGS. 1 and 2 may be changed according to various embodiments.

[0095] According to one embodiment, an electronic device (e.g., electronic device (101) of FIG. 1, electronic device (201) of FIG. 2, FIG. 3A, and FIG. 3B) may include a sensor circuit including an acceleration sensor, a memory (e.g., memory (130) of FIG. 1, memory (220) of FIG. 2) for storing instructions, and at least one processor (e.g., processor (120) of FIG. 1, processor (210) of FIG. 2).

[0096] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to acquire first peak signals having a user movement component based on sensing information acquired by the acceleration sensor related to periodic limb movement of sleep (PLMS) while the user is sleeping.

[0097] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to obtain second peak signals based on first peak signals that satisfy a specified PLMS condition among the first peak signals.

[0098] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to perform interpolation between second peak signals acquired at designated intervals during the total sleep time to acquire third peak signals.

[0099] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to determine whether a PLMS disease is present based on third peak signals identified in designated intervals remaining from among the third peak signals acquired during the total sleep time, excluding a designated interval corresponding to a REM sleep stage.

[0100] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to identify a false detection signal associated with apnea or hypopnea for each designated section during the total sleep time, and, based on a number of occurrences of the false detection signal exceeding a designated number, remove second peak signals identified corresponding to the false detection signal in the section in which the false detection signal occurred.

[0101] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to: determine the number of the third peak signals identified in the remaining designated intervals; obtain a periodic limb movement index (PLMI) by dividing the determined number by the total sleep time; and determine whether the patient has the PLMS disease and the PLMS stage based on the PLMI.

[0102] According to one embodiment, the sensing information may include first raw data (ACC_raw_x) of a first axis (e.g., x-axis), second raw data (ACC_raw_y) of a second axis (e.g., y-axis), and third raw data (ACC_raw_z) of a third axis (e.g., z-axis).

[0103] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to acquire first peak signals at first time intervals in the designated section based on identifying a user motion component in at least one of the first raw data, the second raw data, or the third raw data.

[0104] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to obtain corrected first peak signals by correcting the interval to a value of the first peak signal based on the interval between the first peak signals being identified as being less than a specified time.

[0105] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to add a virtual peak signal within the interval based on the interval between the acquired second peak signals being greater than a specified interval.

[0106] According to one embodiment, when the instructions are executed by the at least one processor, the electronic device can select first peak signals that satisfy a condition that a signal generation time is less than or equal to a first specified range time among the first peak signals in the specified section, select first peak signals that satisfy a condition that an interval of the first peak signals selected in the specified section is less than or equal to a second specified range time, and acquire the selected first peak signals that are repeated continuously a specified number of times or more in the specified section as the second peak signals having a value of 1.

[0107] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to retain second peak signals among the acquired second peak signals, the intervals of the second peak signals being within a third designated range of time (12 seconds to 90 seconds), and to remove one of the second peak signals acquired before the interval and / or the second peak signals acquired after the interval, based on the interval being greater than the third designated range of time (12 seconds to 90 seconds).

[0108] According to one embodiment, the electronic device may further include a communication circuit (e.g., a communication module (190) of FIG. 1, a communication circuit (240) of FIG. 2).

[0109] According to one embodiment, the instructions, when executed by the at least one processor, may be configured to cause the electronic device to obtain result information for confirming whether the PLMS disease is present, control the display to display the result information, and control the communication circuit to transmit the result information to an external electronic device.

[0110] Figure 8 is a diagram illustrating an example of an operating method in an electronic device according to one embodiment. In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0111] Referring to FIG. 8, in operation 801, an electronic device according to an embodiment (e.g., the electronic device (101) of FIG. 1 and the electronic device (201) of FIGS. 2, 3A, and 3B) may obtain sensing information (ACC_raw) related to periodic limb movement disorder (PLMS) detected by an acceleration sensor included in a sensor circuit (e.g., the sensor module (176) of FIG. 1 and / or the sensor circuit (230) of FIG. 2) while the user is sleeping. According to an embodiment, the electronic device may receive the sensing information detected by the acceleration sensor of an external electronic device worn by the user during sleep through a communication circuit (e.g., the communication module (190) of FIG. 1 and / or the communication circuit (240) of FIG. 2).

[0112] In operation 803, an electronic device according to one embodiment may acquire first peak signals (ACC_or or ACC merge) having a user motion component in a designated section based on the acquired sensing information. According to one embodiment, the electronic device may perform an operation of acquiring the first peak signals in a designated section (e.g., 10 minutes) and repeatedly perform the operation in all sections while the user is sleeping.

[0113] In operation 805, the electronic device according to one embodiment may acquire (e.g., set) second peak signals (ACC_plms) in a designated section based on first peak signals identified (e.g., selected) in response to a designated condition of PLMS among the acquired first peak signals. According to one embodiment, the electronic device may perform an operation of acquiring second peak signals based on the first peak signals (or corrected first peak signals) acquired in a designated section (e.g., 10 minutes), and may repeatedly perform this operation in all sections while the user is sleeping.

[0114] In operation 807, an electronic device according to one embodiment may obtain third peak signals by performing interpolation between second peak signals. The electronic device may obtain the third peak signals at designated intervals (e.g., 10 minutes) during the total sleep time.

[0115] In operation 809, the electronic device according to one embodiment may determine whether the patient has PLMS disease (or PLMS stage) based on third peak signals (e.g., final peak signal (ACC_plms_REM)) included in the remaining sections excluding the section corresponding to the REM sleep stage among all designated sections within the total sleep time. According to one embodiment, the electronic device may perform an operation of determining whether the patient has PLMS disease (or PLMS stage) after the end of sleep.

[0116] According to the operating method of FIG. 8 described above, an electronic device according to one embodiment can obtain result information for confirming whether a user has PLMS disease, display the obtained result information on a display, and / or transmit the obtained result information to an external electronic device via a communication circuit. The electronic device can more accurately determine whether a user has PLMS disease by using the result information obtained over a certain period of time (e.g., 3 days, 1 week, or 1 month). For example, if the user is confirmed to have the second PLMS stage or higher continuously or a specified number of times over a certain period of time, the electronic device can confirm that the user has PLMS disease and provide guidance information related to PLMS disease and / or guidance information related to treatment.

[0117] According to one embodiment, an electronic device may be a wearable device that can execute a sleep-related application for PLMS and be worn on a user's body, but is not limited thereto. The electronic device may be a user device (e.g., a mobile phone) that can execute a sleep-related application for PLMS and communicate with a wearable device worn by a user. The user device may receive sensing information detected by an acceleration sensor from a wearable device worn by a user during sleep, and perform operations 803 to 807 using the received sensing information to determine whether a PLMS disease is present.

[0118] Figure 9 is a diagram illustrating an example of an operating method in an electronic device according to one embodiment. In the following embodiments, the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0119] Referring to FIG. 9, an electronic device according to an embodiment (e.g., the electronic device (101) of FIG. 1 and the electronic device (201) of FIGS. 2, 3A, and 3B) may obtain sensing information (ACC_raw) related to periodic limb movement disorder (PLMS) detected by an acceleration sensor included in a sensor circuit (e.g., the sensor module (176) of FIG. 1 and / or the sensor circuit (230) of FIG. 2) while the user is sleeping, as in operation 801 of FIG. 8. According to an embodiment, the electronic device may obtain the sensing information from an acceleration sensor of an external electronic device (e.g., a wearable electronic device) worn by the user. Sensing information (e.g., sensing information (401) of FIG. 4) may include first raw data (ACC_raw_x) of a first axis (e.g., x-axis), second raw data (ACC_raw_y) of a second axis (e.g., y-axis), and third raw data (ACC_raw_z) of a third axis (e.g., z-axis), as illustrated in FIG. 4b.

[0120] In operation 901, according to one embodiment, the electronic device can classify the sensing information (ACC_raw) into first raw data (ACC_raw_x) of a first axis (e.g., x-axis), second raw data (ACC_raw_y) of a second axis (e.g., y-axis), and third raw data (ACC_raw_z) of a third axis (e.g., z-axis). According to one embodiment, the electronic device obtains filtered raw data (ACC_filt_x, ACC_filt_y, ACC_filt_z) of each axis through a band pass filter (e.g., a band pass filter having a cutoff frequency of 0.5 to 5 Hz) (501) of raw data (ACC_raw_x, ACC_raw_y, ACC_raw_z) of each axis, and obtains a root mean square value (ACC_rms value) for the obtained filtered raw data of each axis (e.g., calculated by shifting filtered raw data of 1 second in length by 0.04 seconds). According to one embodiment, the electronic device may calculate (e.g., obtain) a variance for the root mean square values ​​(ACC_rms_x, ACC_rms_y, and ACC_rms_z) of each axis acquired during a first period of time (e.g., 10 seconds) and determine whether the calculated variance is greater than a variance threshold value (variance_threshold). If the calculated variance is greater than the variance threshold value, the electronic device may maintain a previously set resting level value, and if the calculated variance is less than or equal to the variance threshold value, the electronic device may set the resting level value to an average of data detected during the first period of time (e.g., 10 seconds). For example, the processor (210) may repeatedly perform the setting of the resting level value by shifting the time axis by 1 second.The electronic device can set a threshold (e.g., a resting level value plus a threshold margin) for a predefined condition (e.g., a first condition) for identifying a motion component using a resting level. Here, the predefined condition for the PLMS can include a first condition in which data (e.g., root mean square values ​​of each axis (ACC_rms_x, ACC_rms_y, and ACC_rms_z)) detected for a first time period (e.g., 10 seconds) are greater than a threshold value (e.g., a precalculated value of 0.0145) plus a threshold margin, a second condition in which the sleep stage is not wake, and a third condition in which the sleep state is normal (e.g., not sleep apnea or sleep hypopnea). According to one embodiment, the electronic device can check whether the raw data of each axis (e.g., the root mean square values ​​of each axis (ACC_rms_x, ACC_rms_y, and ACC_rms_z)) satisfies a predefined condition for PLMS, and if the raw data does not satisfy the predefined condition, it can be determined that there is no motion component (acc_move = 0), and if the raw data satisfies the predefined condition, it can be determined that the raw data has a motion component (acc_move = 1).

[0121] In operation 903, the electronic device may check whether a user motion component exists in the raw data of at least one axis among the raw data of each axis classified from the sensed information detected by shifting by, for example, 1 second every first time (e.g., 10 seconds) in a specified section (e.g., 10 minutes). In operation 903, the electronic device according to an embodiment may check whether a user motion component exists in the raw data of at least one axis among the raw data of each axis using an OR gate. As a result of the check, if a motion component exists in at least one of the raw data of each axis, the electronic device may perform operation 905. As a result of the check, if a motion component does not exist in at least one of the raw data of each axis (e.g., if there is no motion component in the raw data of all of the axes), the electronic device may perform operation 911 without generating the first peak signals (ACC_or) having the user motion component.

[0122] In operation 905 (operation 903 - example), the electronic device may obtain (e.g., generate or set) first peak signals (ACC_or) having a user motion component based on the user motion component being identified in the raw data of at least one axis among the raw data of each axis.

[0123] In operation 907, the electronic device can check the interval between the first peak signals (ACC_or) acquired during a specified period and correct the first peak signals. If there is an interval shorter than the specified interval (e.g., 1.5 seconds) among the intervals between the first peak signals confirmed during the specified period, the electronic device can acquire corrected first peak signals (acc_merge) by merging the first peak signals between the short intervals into one signal (e.g., correcting the short interval to 1).

[0124] In operation 909, the electronic device may identify (e.g., confirm, detect, or select) first peak signals satisfying a specified condition of PLMS among the first peak signals, and acquire (e.g., set, designate, or generate) second peak signals (ACC_plms) in a specified section based on the identified first peak signals. According to one embodiment, the electronic device may maintain (e.g., maintain a value of 1) at least one first peak signal satisfying a condition that a signal generation time (e.g., time length (x)) is within a first specified time range (0.5 seconds to 10 seconds) (e.g., longer than 0.5 seconds and shorter than 10 seconds) among the corrected first peak signals (acc_merge), and remove (e.g., set a value of 0) at least one first peak signal that does not satisfy the condition that the signal generation time (e.g., time length (x)) is within a first specified time range (0.5 seconds to 10 seconds). The electronic device can select first peak signals that satisfy the condition that the interval of the first peak signals (ACC_cut) maintained in a specified section is less than or equal to a second specified range time (5 seconds to 90 seconds). The processor (210) can acquire the selected first peak signals that are repeated continuously a specified number of times (e.g., 4 times) or more in the specified section as second peak signals (ACC_plms) having a value of 1. The processor (210) can remove (e.g., set to a value of 0) at least one remaining selected first peak signal that is not repeated continuously a specified number of times (e.g., 4 times) or more in the specified section.

[0125] The above-described operations 901 to 909 are operations that are repeatedly performed every first time (e.g., 10 seconds) during a first period (e.g., approximately 10 minutes), and the first peak signals (ACC_or) and second peak signals (ACC_plms) having a user movement component may be signals acquired during the first period (e.g., approximately 10 minutes).

[0126] In Action 911 (Action 903 - No), the electronic device can determine whether sleep has ended. If the determination is negative, the electronic device can perform Action 901 again. If sleep has ended, the electronic device can perform Action 913.

[0127] In operation 913 (operation 911 - example), the electronic device can identify signals related to apneas and / or hypopneas as false detection signals based on information related to sleep, and can obtain (e.g., count) the number of occurrences of false detection signals. The electronic device can identify false detection signals in each designated section of the total sleep time, and remove the identified false detection signals from the corresponding section. The electronic device can identify that the second peak signals acquired in the corresponding section correspond to false detection signals based on the occurrence of false detection signals exceeding a designated number of times (e.g., a count value of 5), and can remove the second peak signals corresponding to the false detection signals (e.g., set the value ACC_plms_RRLM=0). The signals related to apneas and / or hypopneas can occur when a respiratory event occurs in which blood oxygen saturation decreases to a certain level (e.g., 3% or more), and can be counted, for example, when it lasts for 10 seconds. In the operating method of FIG. 9, the above operation 913 is described as being performed after the end of sleep, but is not limited thereto, and the above operation 913 may be performed at designated intervals while the user is sleeping before the end of sleep operation (operation 911).

[0128] In operation 915, the electronic device may perform interpolation between second peak signals for all intervals in units of a specified interval in the total sleep time. According to one embodiment, the electronic device may remove false detection signals from the specified intervals, check the intervals between the remaining second peak signals, and, based on some of the identified intervals being greater than the specified interval (1.5 times the median value), add virtual peak signals to the some of the intervals to perform interpolation between the second peak signals. The electronic device may check the number of intervals (N_interp=round(plms_inverval[i] / median(plms_interval))+1)) that are confirmed to be greater than the specified interval (1.5 times the median value), and generate virtual peak signals (e.g., virtual PLMS data) as many as the confirmed number (N_interp). The processor (210) may maintain the intervals of the second peak signals that are confirmed to be less than the specified interval (1.5 times the median value). According to one embodiment, the electronic device can determine whether the interval (e.g., time interval) between second peak signals in all intervals including second peak signals during the total sleep time is outside a third designated time range (12 seconds to 90 seconds) which is a PLMS interval condition. If the interval between second peak signals is less than or equal to the third designated time range (12 seconds to 90 seconds), the processor (210) can maintain the second peak signals, and based on the interval between second peak signals being greater than the third designated time range (12 seconds to 90 seconds), can remove one of the second peak signals existing before and after the interval.

[0129] In operation 917, the electronic device may obtain (e.g., set, specify, or generate) final third peak signals for determining whether a PLMS disease is present based on the interpolated second peak signals.

[0130] In operation 919, the electronic device can identify a section corresponding to the REM sleep stage among all sections during the total sleep time, and determine whether the patient has PLMS disease based on the third peak signals (e.g., final peak signals (PLMS signals)) included in the remaining sections excluding the identified section. According to one embodiment, the electronic device can determine whether the patient has PLMS disease using a measure of severity (e.g., a periodic limb movement index (PLMI) defined as the average number of limb movements per hour of sleep). The electronic device can determine the number of third peak signals (e.g., a count value) acquired during the total sleep time, calculate the PLMI by dividing the identified number by the total sleep time, and determine whether the patient has PLMS disease or a PLMS stage based on the calculated PLMI. For example, if the calculated PLMI is less than a designated first PLMI index (e.g., 5), the electronic device can determine that the patient has normal PLMS stage 1. If the calculated PLMI is less than a designated second PLMI index (e.g., 5 to 25), the electronic device can determine that the patient has mild PLMS disease as a second PLMS stage. If the calculated PLMI is less than a designated third PLMI index (e.g., 25 to 50), the electronic device can determine that the patient has moderate PLMS disease as a third PLMS stage. If the calculated PLMI is less than a designated fourth PLMI index, the electronic device can determine that the patient has moderate PLMS disease as a third PLMS stage. If the index is above 50 (e.g., 50), it can be confirmed as severe PLMS disease, which is stage 4 PLMS.

[0131] In operation 921, according to one embodiment, the electronic device may obtain result information for determining whether a patient has PLMS disease and control the display (250) to display the result information. The electronic device may control the communication circuit (240) to transmit the result information to an external electronic device. Thereafter, the electronic device may terminate the operation.

[0132] In one embodiment, the electronic device can more accurately determine the presence of PLMS using the results obtained over a certain period of time (e.g., three days, one week, or one month). For example, if the user is confirmed to have a PLMS level 2 or higher continuously or a specified number of times over a certain period of time, the electronic device can confirm that the user has PLMS and provide (e.g., display, output, or transmit) guidance information related to PLMS and / or guidance information related to treatment.

[0133] According to one embodiment, an operating method in an electronic device (e.g., electronic device (101) of FIG. 1 and electronic device (201) of FIGS. 2, 3A and 3B) may include an operation of acquiring sensing information related to periodic limb movement of sleep (PLMS) by an acceleration sensor included in a sensor circuit of the electronic device (e.g., sensor module (176) of FIG. 1, sensor circuit (230) of FIG. 2) while a user is sleeping.

[0134] According to one embodiment, the method may include an operation of obtaining first peak signals having a user motion component based on the sensing information.

[0135] According to one embodiment, the method may include an operation of obtaining second peak signals based on first peak signals that satisfy a specified PLMS condition among the first peak signals.

[0136] According to one embodiment, the method may include an operation of obtaining third peak signals by performing interpolation between second peak signals obtained at specified intervals during the total sleep time.

[0137] According to one embodiment, the method may include an operation of determining whether a PLMS disease is present based on third peak signals identified in designated sections remaining from among the third peak signals acquired during the total sleep time, excluding a designated section corresponding to the REM sleep stage.

[0138] According to one embodiment, the method may further include an operation of identifying a false detection signal related to apnea or hypopnea for each designated section during the total sleep time, and an operation of removing second peak signals identified in response to the false detection signal in the section in which the false detection signal is generated based on the number of occurrences of the false detection signal exceeding the designated number.

[0139] According to one embodiment, the sensing information may include first raw data (ACC_raw_x) of a first axis (e.g., x-axis), second raw data (ACC_raw_y) of a second axis (e.g., y-axis), and third raw data (ACC_raw_z) of a third axis (e.g., z-axis).

[0140] According to one embodiment, the method may include an operation of acquiring first peak signals at a first time interval in the designated section based on identifying a user movement component in at least one of the first raw data, the second raw data, or the third raw data.

[0141] According to one embodiment, the method may further include an operation of obtaining corrected first peak signals by correcting the interval to a value of the first peak signal based on the interval between the first peak signals being identified as being less than a specified time.

[0142] According to one embodiment, the method may further include an operation of adding a virtual peak signal within the interval based on the interval between the acquired second peak signals being greater than a specified interval.

[0143] According to one embodiment, the operation of obtaining the second peak signals may include an operation of selecting first peak signals that satisfy a condition that a signal generation time is less than or equal to a first specified range time among the first peak signals in the specified section, an operation of selecting first peak signals that satisfy a condition that an interval of the first peak signals selected in the specified section is less than or equal to a second specified range time, and an operation of obtaining the selected first peak signals that are repeated continuously a specified number of times or more in the specified section as the second peak signals having a value of 1.

[0144] According to one embodiment, the operation of obtaining third peak signals by performing interpolation between second peak signals may include the operation of maintaining second peak signals among the obtained second peak signals whose intervals between the second peak signals are within a third designated range of time (12 seconds to 90 seconds), and the operation of removing one of the second peak signals obtained before the interval and / or the second peak signals obtained after the interval based on the interval being greater than the third designated range of time (12 seconds to 90 seconds).

[0145] According to one embodiment, the method may further include an operation of obtaining result information for confirming whether the PLMS disease is present and an operation of displaying the result information on the display.

[0146] According to one embodiment, the method may further include transmitting the result information to an external electronic device via a communication circuit of the electronic device.

[0147] According to one embodiment, a non-transitory storage medium storing a program may include executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform an operation of acquiring sensing information related to periodic limb movement of sleep (PLMS) by an acceleration sensor while a user sleeps, an operation of acquiring first peak signals having a user movement component based on the sensing information, an operation of acquiring second peak signals based on first peak signals satisfying a specified PLMS condition among the first peak signals, an operation of performing interpolation between second peak signals acquired at each specified section during a total sleep time to acquire third peak signals, and an operation of determining whether a PLMS disease exists based on third peak signals identified in specified sections remaining except for a specified section corresponding to a REM sleep stage among the third peak signals acquired during a total sleep time.

[0148] According to one embodiment of the present disclosure, by using sensing information detected by an acceleration sensor, peak signals having a motion component and satisfying PLMS conditions are acquired and corrected, and PLMS can be measured easily at home using an electronic device (e.g., a wearable device) through signal processing appropriate for PLMS, thereby enabling early detection of PLMS disease, as PLMS, which could be measured using an EMG sensor in a hospital, can be measured. In addition, various effects that can be directly or indirectly identified through this document can be provided. The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by those skilled in the art to which the present disclosure pertains from the description below.

[0149] The embodiments disclosed in this document are presented for the purpose of explaining and understanding the disclosed technical content, and do not limit the scope of the technology described in this document. Therefore, the scope of this document should be interpreted to include all modifications or various other embodiments based on the technical concepts of this document.

[0150] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0151] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0152] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0153] Various embodiments of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g. electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0154] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as a computer program product. The computer program product may be traded between sellers and buyers as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or may be provided through an application store (e.g., Play Store). TM ) or directly between two user devices (e.g., smart phones), online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0155] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In the electronic device (101, 201), A sensor circuit (176, 230) including an acceleration sensor; Memory (130, 220) for storing instructions; and Contains at least one processor (120, 210), The above instructions, when executed by the at least one processor, cause the electronic device to: Based on sensing information obtained by the acceleration sensor related to periodic limb movement of sleep (PLMS) while the user is sleeping, first peak signals having a user movement component are obtained, Obtain second peak signals based on first peak signals that satisfy a specified PLMS condition among the first peak signals, Obtain third peak signals by performing interpolation between second peak signals acquired at specified intervals during the total sleep time, An electronic device configured to determine whether a PLMS disease is present based on third peak signals identified in designated sections excluding a designated section corresponding to the REM sleep stage among the third peak signals acquired during the total sleep time.

2. In the first paragraph, the instructions, when executed by the at least one processor, cause the electronic device to: An electronic device configured to identify a false detection signal related to apnea or hypopnea for each specified section during the total sleep time, and to remove second peak signals identified in response to the false detection signal in the section in which the false detection signal occurred based on the number of occurrences of the false detection signal exceeding the specified number of times.

3. In the first or second paragraph, the instructions, when executed by the at least one processor, cause the electronic device to: Check the number of third peak signals identified in the remaining designated sections above, The periodic limb movement index (PLMI) is obtained by dividing the above-mentioned confirmed number by the total sleep time, It is configured to check whether the PLMS disease and the PLMS stage are present based on the PLMI. An electronic device, wherein the sensing information includes first raw data (ACC_raw_x) of a first axis, second raw data (ACC_raw_y) of a second axis, and third raw data (ACC_raw_z) of a third axis.

4. In any one of paragraphs 1 to 3, The above instructions, when executed by the at least one processor, cause the electronic device to: An electronic device configured to acquire first peak signals at a first time interval in the designated section based on identifying a user movement component in at least one of the first raw data, the second raw data, or the third raw data.

5. In any one of paragraphs 1 to 4, the instructions, when executed by the at least one processor, cause the electronic device to: Based on the interval between the first peak signals being identified as being less than a specified time, the interval is configured to be corrected to the value of the first peak signal to obtain corrected first peak signals. An electronic device configured to add a virtual peak signal within the interval based on the interval between the acquired second peak signals being greater than a specified interval.

6. In any one of paragraphs 1 to 5, the instructions, when executed by the at least one processor, cause the electronic device to: In the above-mentioned specified section, first peak signals are selected from among the first peak signals that satisfy the condition that the signal generation time is less than or equal to the first specified range time, Selecting first peak signals that satisfy the condition that the interval of the first peak signals selected in the above-mentioned specified section is less than or equal to the second specified range time, An electronic device that acquires the selected first peak signals that are repeated continuously a specified number of times or more in the above-mentioned specified section as the second peak signals having a value of 1.

7. In any one of paragraphs 1 to 6, the instructions, when executed by the at least one processor, cause the electronic device to: Among the second peak signals obtained above, the second peak signals are maintained within a third specified range of time intervals, An electronic device configured to remove one of a second peak signal acquired before and / or a second peak signal acquired after the interval, based on the interval being greater than a third specified range time.

8. In any one of paragraphs 1 to 9, display (160, 250); and It further includes a communication circuit (190, 240), The above instructions, when executed by the at least one processor, cause the electronic device to: Obtain information on whether the above PLMS disease is confirmed, Control the display to display the above result information, An electronic device configured to control the communication circuit to transmit the result information to an external electronic device.

9. In the method of operation in an electronic device (101, 201), An operation of acquiring sensing information related to periodic limb movement of sleep (PLMS) by an acceleration sensor included in a sensor circuit (176, 230) of the electronic device while the user is sleeping; An operation of acquiring first peak signals having a user movement component based on the above sensing information; An operation of obtaining second peak signals based on first peak signals that satisfy a specified PLMS condition among the first peak signals; An operation of obtaining third peak signals by performing interpolation between second peak signals obtained at specified intervals during the total sleep time; and A method comprising an operation of determining whether a PLMS disease is present based on third peak signals identified in designated sections excluding a designated section corresponding to the REM sleep stage among the third peak signals acquired during the total sleep time.

10. In the 9th paragraph, the method, An operation of checking for false detection signals related to apnea or hypopnea at each specified interval during the above total sleep time; and Further comprising an operation of removing second peak signals identified in response to the false detection signal in a section in which the false detection signal is generated, based on the occurrence number of the false detection signal exceeding a specified number of times. A method wherein the sensing information includes first raw data (ACC_raw_x) of a first axis, second raw data (ACC_raw_y) of a second axis, and third raw data (ACC_raw_z) of a third axis.

11. In the 9th or 10th paragraph, the operation of acquiring the first peak signals comprises: A method comprising: acquiring first peak signals at a first time interval in the designated section based on identifying a user movement component in at least one of the first raw data, the second raw data, or the third raw data.

12. In any one of the 9th to 11th clauses, the method, An operation of obtaining corrected first peak signals by correcting the interval to the value of the first peak signal based on the interval between the first peak signals being identified as being less than a specified time; and A method further comprising an operation of adding a virtual peak signal within the interval based on the interval between the acquired second peak signals being greater than a specified interval.

13. In any one of the 9th to 12th clauses, the operation of obtaining the second peak signals comprises: An operation of selecting first peak signals that satisfy a condition that the signal generation time is less than or equal to a first specified range time among the first peak signals in the above-mentioned specified section; An operation of selecting first peak signals that satisfy a condition that the interval of the first peak signals selected in the above-mentioned specified section is less than or equal to a second specified range time; and A method comprising an operation of acquiring the selected first peak signals that are repeated continuously a specified number of times or more in the specified section as the second peak signals having a value of 1.

14. In any one of the 9th to 13th clauses, the operation of obtaining third peak signals by performing interpolation between the second peak signals comprises: An operation of maintaining second peak signals among the acquired second peak signals within a third specified range of time intervals; and An operation of removing one of a second peak signal acquired before and / or a second peak signal acquired after the interval, based on the interval being greater than a third specified range time, The above method, An action to obtain information as a result of confirming whether the above PLMS disease is present; An operation of displaying the above result information on the display (160, 250) of the electronic device; and A method further comprising an operation of transmitting the result information to an external electronic device through a communication circuit (190, 240) of the electronic device.

15. In a non-transitory storage medium storing a program, the program, when executed by at least one processor (120, 210) of the electronic device, the electronic device (101, 201), An action of acquiring sensing information related to periodic limb movement of sleep (PLMS) by the acceleration sensor while the user is sleeping; An operation of acquiring first peak signals having a user movement component based on the above sensing information; An operation of obtaining second peak signals based on first peak signals that satisfy a specified PLMS condition among the first peak signals; An operation of obtaining third peak signals by performing interpolation between second peak signals obtained at specified intervals during the total sleep time; and A non-transitory storage medium comprising executable commands for executing an operation of determining whether a patient has PLMS disease based on third peak signals identified in designated sections excluding a designated section corresponding to a REM sleep stage among the third peak signals acquired during the total sleep time.

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