KR20220008601A - Electronic device for obtaining biological signal and method thereof - Google Patents

Abstract

The present invention provides an electronic device capable of performing filtering which can reduce signal distortion while removing noise with dominant performance if the frequency bands of a biosignal and noise are overlapped and a noise generation section is short. The electronic device comprises: at least one sensor; a memory storing information on the length of an initial window and information on a threshold value; and a processor operatively connected to the at least one sensor and the memory. The processor can be configured to use the at least one sensor to obtain a biosignal, sequentially perform fitting based on the length of the initial window for a plurality of times of the biosignal, obtain a first difference value of the biosignal and a fitting result for the plurality of times, perform fitting based on the length of a first window again after adjusting the initial window to the first window if the first difference value is higher than or equal to the threshold value, determine a trend estimation value if the first difference value is lower than the threshold value, obtain a baseline signal based on the trend estimation values for the plurality of times, and obtain a filtered signal resulting from removing the baseline signal from the biosignal. Various other embodiments identified through the specification are possible.

Description

Electronic device for acquiring biosignals and method for operating the same

Various embodiments of the present invention relate to an electronic device for acquiring a biosignal and a method for operating the same.

In general, biological signals refer to information that can be generated and measured in the human body, and includes electroencephalogram (EEG), electroretinogram (ERG), RESP), electrocardiogram (ECG), electrocardiogram (EGG), photoplethysmogram (PPG), electromyogram (EMG), electroneurogram (ENG), or galvanic skin response (GSR) may include

Due to the popularization of the wearable electronic device and the miniaturization of sensors for measuring the biosignal, a biosignal may be obtained using the wearable electronic device.

Unlike conventional medical measurement systems that acquire biosignals in a state where the measurement environment is very well controlled, wearable electronic devices acquire biosignals in everyday environments. may include noise caused by the movement of

Various filtering techniques may be applied in order to obtain a significant bio-signal obtained by removing noise from a user's bio-signal obtained using the wearable electronic device. For example, in order to remove noise from the biosignal, the electronic device may include a bandpass filter for extracting only a signal of a desired frequency band. For example, the band pass filter may include a high pass filter (HPF) for removing baseline drift and/or a low pass filter (LPF) for removing noise in a high frequency band.

When the biosignal is passed through the bandpass filter, noise generated due to factors such as movement of a subject may appear in the filtered signal as a transient response. Noise causing a transient response included in the biosignal may be referred to as transient response noise.

Since the biosignal has a low frequency band, it is possible to filter the biosignal using the HPF having a low cutoff frequency. Since the low cutoff frequency HPF has a long filter response, it may be vulnerable to transient response noise such as step noise or impulse noise.

The LPF can be used to filter the overall ramp-up pattern while ignoring fast-changing signals. Since the baseline change has a very low frequency range, it may be possible to restore the baseline signal even with a simple filtering method in a stable measurement environment. However, when the frequency range of the baseline overlaps with the noise, it may be difficult to restore the signal. For example, noise having a wide frequency range (eg, step noise) contains high frequency components, so the ability of the LPF to reject it may be poor. In addition, since significant biosignals have regular and small shapes that exist within a trend, it may be difficult to obtain meaningful biosignals if a rapidly changing signal such as LPF filtering is ignored. Therefore, according to the filtering technique, the trend estimation capability and the signal restoration capability may have a trade-off relationship.

According to various embodiments of the present disclosure, the electronic device may use a filtering method based on a trend estimation algorithm.

An electronic device according to an embodiment disclosed in this document includes: a memory in which at least one sensor, information on a length of an initial window, and information on a threshold value are stored; and a processor operatively coupled to the at least one sensor and the memory. The processor obtains a biosignal using the at least one sensor, and sequentially performs fitting based on the length of the initial window for a plurality of time points of the biosignal, and for each of the plurality of time points A first difference value between a fitting result and the biosignal is obtained. If the first difference value is equal to or greater than the threshold value, the initial window is adjusted to the first window, and then fitting is performed again based on the length of the first window. and if the first difference value is less than the threshold value, a trend estimation value is determined, a baseline signal is obtained based on the trend estimation values for the plurality of time points, and the baseline signal is obtained from the biosignal. It may be set to obtain the removed filtered signal, and the length of the first window may be smaller than the length of the initial window.

According to the embodiments disclosed in this document, when the frequency band of the noise and the biosignal overlap and the noise generation period is short, the electronic device performs filtering capable of reducing signal distortion while removing noise with superior performance. can be done

In addition, various effects directly or indirectly identified through this document may be provided.

1 is a block diagram of an electronic device in a network environment, according to various embodiments of the present disclosure;
2 is a block diagram illustrating a configuration of an electronic device according to an exemplary embodiment.
3 schematically illustrates a filtering operation of an electronic device, according to an embodiment.
4 is a comparison of a filtering method according to an exemplary embodiment and a filtering method according to a comparative example.
5 is a diagram illustrating fitting on a biosignal in an electronic device according to an exemplary embodiment.
6 is a diagram illustrating a method of fitting a variable window method of an electronic device according to an exemplary embodiment.
7 is a flowchart illustrating a filtering operation of an electronic device, according to an embodiment.
8 is a flowchart illustrating a sequential fitting method for a plurality of time points of a biosignal of an electronic device, according to an exemplary embodiment.
9 illustrates a comparison of a signal obtained through a filtering method according to an exemplary embodiment with a signal obtained through a filtering method according to a comparative example.
10 is a diagram illustrating a result value obtained by performing filtering according to an exemplary embodiment.
In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.

1 is a block diagram of an electronic device 101 in a network environment 100 according to various embodiments. Referring to FIG. 1 , in a network environment 100 , an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or a second network 199 . It may communicate with the electronic device 104 or the server 108 through (eg, a long-distance wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120 , a memory 130 , an input module 150 , a sound output module 155 , a display module 160 , an audio module 170 , and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or an antenna module 197 may be included. In some embodiments, at least one of these components (eg, the connection terminal 178 ) may be omitted or one or more other components may be added to the electronic device 101 . In some embodiments, some of these components (eg, sensor module 176 , camera module 180 , or antenna module 197 ) are integrated into one component (eg, display module 160 ). can be

The processor 120, for example, executes software (eg, a program 140) to execute at least one other component (eg, a hardware or software component) of the electronic device 101 connected to the processor 120 . It can control and perform various data processing or operations. According to one embodiment, as at least part of data processing or operation, the processor 120 converts commands or data received from other components (eg, the sensor module 176 or the communication module 190 ) to the volatile memory 132 . may be stored in the volatile memory 132 , and may process commands or data stored in the volatile memory 132 , and store the result data in the non-volatile memory 134 . According to an embodiment, the processor 120 is the main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit) a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, when the electronic device 101 includes the main processor 121 and the sub-processor 123 , the sub-processor 123 may use less power than the main processor 121 or may be set to be specialized for a specified function. can The auxiliary processor 123 may be implemented separately from or as a part of the main processor 121 .

The auxiliary processor 123 is, for example, on behalf of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, executing an application). ), together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to an embodiment, the co-processor 123 (eg, an image signal processor or a communication processor) may be implemented as part of another functionally related component (eg, the camera module 180 or the communication module 190). have. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. Artificial intelligence models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself on which artificial intelligence is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example not limited The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the above example. The artificial intelligence model may include, in addition to, or alternatively, a software structure in addition to the hardware structure.

The memory 130 may store various data used by at least one component of the electronic device 101 (eg, the processor 120 or the sensor module 176 ). The data may include, for example, input data or output data for software (eg, the program 140 ) and instructions related thereto. The memory 130 may include a volatile memory 132 or a non-volatile memory 134 .

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 .

The input module 150 may receive a command or data to be used in a component (eg, the processor 120 ) of the electronic device 101 from the outside (eg, a user) of the electronic device 101 . The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output a sound signal to the outside of the electronic device 101 . The sound output module 155 may 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 may be used to receive an incoming call. According to one embodiment, the receiver may be implemented separately from or as part of the speaker.

The display module 160 may visually provide information to the outside (eg, a user) of the electronic device 101 . The display module 160 may include, for example, a control circuit for controlling a display, a hologram device, or a projector and a corresponding device. According to an embodiment, the display module 160 may include a touch sensor configured to sense a touch or a pressure sensor configured to measure the intensity of a force generated by the touch.

The audio module 170 may convert a sound into an electric signal or, conversely, convert an electric signal into a sound. According to an embodiment, the audio module 170 acquires a sound through the input module 150 , or an external electronic device (eg, a sound output module 155 ) connected directly or wirelessly with the electronic device 101 . A sound may be output through the electronic device 102 (eg, a speaker or headphones).

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, user state), and generates an electrical signal or data value corresponding to the sensed state. can do. According to an embodiment, the sensor module 176 may 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, It may include a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface 177 may support one or more designated protocols that may be used by the electronic device 101 to directly or wirelessly connect with an external electronic device (eg, the electronic device 102 ). According to an 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.

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

The haptic module 179 may convert an electrical signal into a mechanical stimulus (eg, vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to an embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

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

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). It can support establishment and communication performance through the established communication channel. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, 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 (eg, : It may include a LAN (local area network) communication module, or a power line communication module). A corresponding communication module among these communication modules is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (eg, a telecommunication network such as a LAN or a WAN). These various types of communication modules may be integrated into one component (eg, a single chip) or may be implemented as a plurality of components (eg, multiple chips) separate from each other. The wireless communication module 192 uses the subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199 . The electronic device 101 may be identified or authenticated.

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

The antenna module 197 may transmit or receive a signal or power to the outside (eg, an external electronic device). According to an embodiment, the antenna module 197 may include an antenna including a conductor formed on a substrate (eg, a PCB) or a radiator formed of a conductive pattern. According to an embodiment, the antenna module 197 may include a plurality of antennas (eg, 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 is connected from the plurality of antennas by, for example, the communication module 190 . can be selected. 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. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) other than the radiator may be additionally formed as a part of the antenna module 197 .

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module comprises a printed circuit board, an RFIC disposed on or adjacent to a first side (eg, bottom side) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, an array antenna) disposed on or adjacent to a second side (eg, top or side) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and a signal ( eg commands or data) can be exchanged with each other.

According to an embodiment, the command or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or a part of operations executed in the electronic device 101 may be executed in one or more external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 is to perform a function or service automatically or in response to a request from a user or other device, the electronic device 101 may perform the function or service itself instead of executing the function or service itself. Alternatively or additionally, one or more external electronic devices may be requested to perform at least a part of the function or the service. One or more external electronic devices that have received the request may execute at least a part of the requested function or service, or an additional function or service related to the request, and transmit a result of the execution to the electronic device 101 . The electronic device 101 may process the result as it is or additionally and provide it as at least a part of a response to the request. For this, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to an embodiment, the external electronic device 104 or the server 108 may be included in the second network 199 . The electronic device 101 may be applied to an intelligent service (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The electronic device according to various embodiments disclosed in this document may have various types of devices. The electronic device may include, for example, a portable communication device (eg, a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance device. The electronic device according to the embodiment of the present document is not limited to the above-described devices.

The various embodiments of this document and terms used therein are not intended to limit the technical features described in this document to specific embodiments, but it should be understood to include various modifications, equivalents, or substitutions of the embodiments. In connection with the description of the drawings, like reference numerals may be used for similar or related components. The singular form of the noun corresponding to the item may include one or more of the item, unless the relevant context clearly dictates otherwise. As used herein, "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 "A , B, or C" each may include any one of the items listed together in the corresponding one of the phrases, or all possible combinations thereof. Terms such as "first", "second", or "first" or "second" may be used simply to distinguish the element from other elements in question, and may refer to elements in other aspects (e.g., importance or order) is not limited. It is said that one (eg, first) component is "coupled" or "connected" to another (eg, second) component, with or without the terms "functionally" or "communicatively". When referenced, it means that one component can be connected to the other component directly (eg by wire), wirelessly, or through a third component.

The term “module” used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as, for example, logic, logic block, component, or circuit. can be used as A module may be an integrally formed part or a minimum unit or a part of the part that performs one or more functions. For example, according to an embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).

According to various embodiments of the present document, one or more instructions stored in a storage medium (eg, internal memory 136 or external memory 138) readable by a machine (eg, electronic device 101) may be implemented as software (eg, the program 140) including For example, a processor (eg, processor 120 ) of a device (eg, electronic device 101 ) may call at least one command among one or more commands stored from a storage medium and execute it. This makes it possible for the device to be operated to perform at least one function according to the called at least one command. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' only means that the storage medium is a tangible device and does not include a signal (eg, electromagnetic wave), and this term is used in cases where data is semi-permanently stored in the storage medium and It does not distinguish between temporary storage cases.

According to one embodiment, the method according to various embodiments disclosed in this document may be provided as included in a computer program product. Computer program products may be traded between sellers and buyers as commodities. The computer program product is distributed in the form of a machine-readable storage medium (eg compact disc read only memory (CD-ROM)), or via an application store (eg Play Store™) or on two user devices ( It can be distributed (eg downloaded or uploaded) directly between smartphones (eg: smartphones) and online. In the case of online distribution, at least a part of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium such as a memory of a server of a manufacturer, a server of an application store, or a relay server.

According to various embodiments, each component (eg, module or program) of the above-described components may include a singular or a plurality of entities, and some of the plurality of entities may be separately disposed in other components. have. According to various embodiments, one or more components or operations among the above-described corresponding components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (eg, a module or a program) may be integrated into one component. In this case, the integrated component may perform one or more functions of each component 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, operations performed by a module, program, or other component are executed sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations are executed in a different order, or omitted. or one or more other operations may be added.

2 is a block diagram illustrating a configuration of an electronic device according to an exemplary embodiment.

According to an embodiment, the electronic device 200 (eg, the electronic device 101 of FIG. 1 ) may include a processor 210 , a memory 220 , and/or at least one sensor 230 . The block diagram of FIG. 2 is exemplary, and the electronic device 200 may further include a configuration not shown in FIG. 2 . For example, the electronic device 200 may further include a display (eg, the display module 160 of FIG. 1 ). The electronic device 200 may output, for example, a result value of filtering the biosignal through a display.

According to an embodiment, the processor 210 (eg, the processor 120 of FIG. 1 ) executes, for example, software (eg, the program 140 of FIG. 1 ) to the electronic device connected to the processor 210 ( 200) may control at least one other component (eg, a hardware or software component), and may perform various data processing or operations. According to one embodiment, as at least part of data processing or computation, the processor 210 stores commands or data received from other components (eg, the at least one sensor 230 ) into a volatile memory (eg, the volatile memory of FIG. 1 ). It may be stored in the memory 132 ), process commands or data stored in the volatile memory 132 , and store the result data in a non-volatile memory (eg, the non-volatile memory 134 of FIG. 1 ).

According to an embodiment, the memory 220 (eg, the memory 130 of FIG. 1 ) is stored in at least one component (eg, the processor 210 or the at least one sensor 230 ) of the electronic device 200 . It can store various data used by The data may include, for example, input data or output data for software (eg, the program 140 of FIG. 1 ) and commands related thereto. According to an embodiment, the memory 220 may include a volatile memory (eg, the volatile memory 132 of FIG. 1 ) or a non-volatile memory (eg, the non-volatile memory 134 of FIG. 1 ). According to an embodiment, data for performing a filtering operation may be stored in the memory 220 . For example, the processor 210 may store parameter values (eg, a window length or a threshold value) for performing a filtering operation in the memory 220 .

At least one sensor 230 (eg, the sensor module 176 of FIG. 1 ) detects an external environmental state (eg, a user state) of the electronic device 200 , and an electrical signal or data corresponding to the sensed state value can be created. According to an embodiment, the at least one sensor 230 (eg, an electrocardiogram (ECG) sensor) may include, for example, a biosensor.

According to an embodiment, the processor 210 may acquire a biosignal using at least one sensor 230 . For example, the biosignal may be an electroencephalogram (EEG), an electroretinogram (ERG), an electrooculography (EOG), a respiratory (RESP), an electrocardiogram (ECG), or an electroretinogram (EGG). ), a pulse wave (photoplethysmogram, PPG), an electromyogram (EMG), an electroneurogram (ENG), and a galvanic skin response (GSR) related signal.

According to an embodiment, the processor 210 may perform a filtering operation on the biosignal based on data stored in the memory 220 . According to an embodiment, the processor 210 may obtain a signal from which the DC component of the biosignal is removed by performing a filtering operation.

3 schematically illustrates a filtering operation of an electronic device, according to an embodiment.

According to an embodiment, the processor (eg, the processor 210 of FIG. 2 ) may acquire the biosignal 300 using at least one sensor (eg, the at least one sensor 230 of FIG. 2 ). . The biosignal 300 may include a significant biosignal 342 and a transient response noise 344 . According to an embodiment, a significant biosignal 342 in the biosignal 300 may be understood as an alternating current (AC) component of the biosignal 300 . For example, the significant bio-signal 342 may be understood as a signal that changes regularly and small. Signal components other than the significant biosignal 342 may be understood as direct current (DC) components of the biosignal. The transient responsive noise 344 may include step noise or impulse noise of a rapidly changing low frequency band. When the signal including the transient responsive noise 344 passes through the band pass filter, the filtered signal may be distorted due to the transient response caused by the transient responsive noise 344 .

According to an embodiment, the processor 210 may obtain a baseline signal 350 by estimating a trend from the biosignal 300 ( 310 ). The processor 210 may determine a trend estimation value based on the fitting results for a plurality of time points of the biosignal 300 , and obtain the baseline signal 350 based on the trend estimation value. The baseline signal 350 may be understood as a DC component of the biosignal 300 . The processor 210 may estimate the trend of the biosignal 300 by performing the fitting according to FIGS. 5 to 6 . The processor 210 may obtain a baseline signal 350 by estimating a trend. According to an embodiment, when the processor 210 obtains the baseline signal 350 from the biosignal 300 , it may be understood as extracting a DC component of the biosignal 300 . As the processor 210 more precisely estimates the trend, the processor 210 may acquire the baseline signal 350 more similar to the DC component of the biosignal 300 . According to various embodiments of the present disclosure, fitting may be understood to more precisely estimate a trend.

According to an embodiment, the processor 210 may remove the baseline signal 350 from the biosignal 300 ( 320 ). The processor 210 may obtain the filtered signal 360 by removing the baseline signal 350 from the biosignal 300 ( 330 ). The processor 210 may remove at least a portion of the DC component included in the biosignal 300 by removing the baseline signal 350 from the biosignal 300 . For example, the processor 210 may remove the transient response noise 344 from the biosignal 300 . Accordingly, the filtered signal 360 may include a significant biosignal 342 that is not distorted by the transient response. The significant biosignal 342 may be used as data for analyzing the user's current state.

4 is a comparison of a filtering method according to an exemplary embodiment and a filtering method according to a comparative example. A block diagram 400a illustrates a filter 410 and a signal obtained through the filter 410 according to an embodiment. The block diagram 400b shows the filter 420 and the signal obtained through the filter 420 according to the comparative example.

Referring to the block diagram 400a, according to an embodiment, a processor (eg, the processor 210 of FIG. 2 ) uses a filter 410 according to an embodiment to generate a biosignal (eg, 300 of FIG. 3 ). A filtered signal 360 may be obtained from The filter 410 according to an embodiment may be understood as performing, for example, filtering according to one of the filtering methods according to various embodiments disclosed in this document.

The filter 410 according to an exemplary embodiment may remove a DC component including a transient response noise from the biosignal 300 . According to an embodiment, the filter 410 may be implemented in hardware and/or software. For example, the filter 410 may include a hardware filter for an analog digital converter (ADC). For example, the filter 410 may further include a software algorithm. Accordingly, the processor 210 uses the filter 410 to generate a filtered signal (eg, 360 in FIG. 3 ) in which a significant biosignal (eg, the significant biosignal 342 of FIG. 3 ) is not distorted from the biosignal 300 . ) can be obtained.

Referring to the block diagram 400b , according to an embodiment, the processor 210 may obtain a filtered signal 425 from the biosignal 300 using the filter 420 according to the comparative example. The filter 420 according to the comparative example may be understood as, for example, a frequency band pass filter. When the significant biosignal 342 passes through the filter 420 according to the comparative example, it may be distorted by transient response noise. For example, when the biosignal 300 includes step noise or impulse noise, the transient response 430 may be included in the filtered signal 425 due to the transient response noise. In a section in which the transient response appears, the significant biosignal 342 may be distorted by the transient response.

According to an embodiment, the processor 210 may use a fitting method for removing transient response noise by estimating a trend with high performance according to FIGS. 5 to 6 .

5 is a diagram illustrating fitting on a biosignal in an electronic device according to an exemplary embodiment. In FIG. 5 , the horizontal axis may be referred to as a time axis in a time domain. In FIG. 5 , a vertical axis may be referred to as a magnitude axis of a biosignal (voltage, current).

According to an embodiment, the processor (eg, the processor 210 of FIG. 2 ) may acquire the biosignal 500 using at least one sensor (eg, the at least one sensor 230 of FIG. 2 ). . For example, the biosignal 500 may be understood as a signal including transient response noise.

The processor 210 may obtain a filtered signal (eg, the filtered signal 360 of FIG. 3 ) by filtering the biosignal 500 . The filtered signal according to an embodiment may be understood as a signal having no transient response or appearing for a very short period. Accordingly, in the filtered signal according to an exemplary embodiment, distortion of a significant biosignal (eg, the significant biosignal 342 of FIG. 3 ) due to transient responsive noise may be reduced.

According to an embodiment, the processor 210 may determine a reference time based on data stored in a memory (eg, the memory 220 of FIG. 2 ). The reference time may vary according to length information of the initial window stored in the memory 220 . For example, the reference view may be set to 'length of the initial window/2+1'.

According to an embodiment, the interval between the reference viewpoints may be set to be constant or variously. For example, the interval between the reference time points may be variously designated based on the variation width of the baseline. When the frequency of change of the baseline is small, the processor 210 may set the interval between the reference points to be long. In this case, the processor 210 may perform filtering with a small operation and a high speed. Conversely, when the frequency of change of the baseline is high, the processor 210 may set the interval between the reference points to be short. In this case, the processor 210 may perform filtering at a high operation and low speed. The reference viewpoint may be changed to a viewpoint moved in the positive direction of the time axis by a specified time interval from the existing reference viewpoint as filtering proceeds. For example, the processor 210 may determine the reference viewpoint as the first viewpoint 510 . The processor 210 may change the reference time point to the second time point 512 after obtaining the trend estimation value for the reference time point. A description of the trend estimate will be provided below. As another example, the processor 210 may obtain a trend estimation value for the second time point (eg, the second time point 512 ) and then change the reference time point to the third time point 514 .

According to an embodiment, the processor 210 may determine the reference viewpoint as the first viewpoint 510 . The processor 210 may extract raw data corresponding to the length of the window 520 based on the reference viewpoint. The raw data may be referred to as at least a part of the biosignal 500 . Information about the reference time point may be stored in a memory (eg, the memory 220 of FIG. 2 ).

According to an embodiment, information on the length of the initial window may be stored in the memory 220 of the electronic device (eg, the electronic device 200 of FIG. 2 ). According to an embodiment, the initial window length may be set to a minimum value at which distortion of a biosignal does not appear in a stable measurement environment, and may be set differently according to a sampling rate.

According to an embodiment, the processor 210 may perform fitting on raw data corresponding to the length of the window 520 . The length of the window 520 may be understood as the length of the initial window, but the processor 210 may adjust the length of the window 520 until a specified condition is satisfied according to the fitting result. For example, the designated condition may be understood as a case in which a difference value between the biosignal 500 and the fitting result is less than a threshold value. For example, when the difference value is less than a threshold value due to transient responsive noise, the processor 210 may decrease the length of the window 520 .

According to an embodiment, the processor 210 may obtain a trend estimation value by performing fitting until a specified condition is satisfied. For example, the trend estimate may be determined as the median 530 of the fitting result.

According to an embodiment, the processor 210 may change the reference time point to the second time point 512 after obtaining the trend estimation value. The processor 210 may perform fitting by extracting raw data corresponding to the length of the window 522 with respect to the reference viewpoint. According to an embodiment, the length of the window 522 may be understood as the length of the initial window. According to another embodiment, in order to increase the operation speed, the processor 210 may set the length of the window 522 based on the changed length of the window used at the first reference time 510 . For example, the length of the window 522 may be referred to as a higher value among peripheral values based on the length of the window 520 adjusted with respect to the first reference viewpoint 510 . A detailed description of the window length reduction and fitting method may be referred to by reference to FIG. 6 . According to an embodiment, the processor 210 may adjust the length of the window 522 until a specified condition is satisfied to obtain the trend estimation value. The processor 210 may perform fitting to obtain a trend estimation value. The processor 210 may obtain a median value 532 of the fitting result as a trend estimation value.

According to an embodiment, the processor 210 may change the reference time point to the third time point 514 after obtaining the trend estimation value. The processor 210 may perform fitting by extracting raw data corresponding to the length of the window 524 with respect to the reference viewpoint. The processor 210 may adjust the length of the window 524 until a specified condition is satisfied to obtain a trend estimation value. The processor 210 may perform fitting to obtain a trend estimation value. The processor 210 may obtain a median value 534 of the fitting result as a trend estimation value.

According to an embodiment, the processor 210 may acquire a baseline signal 540 based on trend estimation values for each reference time point. The processor 210 may obtain a filtered signal (eg, the filtered signal 360 of FIG. 3 ) by removing the baseline signal 540 from the biosignal 500 .

6 is a diagram illustrating a method of fitting a variable window method of an electronic device according to an exemplary embodiment. Reference numeral 600a denotes a fitting based on the length of the first window 630 , reference numeral 600b based on the length of the second window 632 , and reference numeral 600c based on the length of the third window 634 . ) can be done. According to an embodiment, the processor (eg, the processor 210 of FIG. 2 ) uses at least one sensor (eg, the at least one sensor 230 of FIG. 2 ) to the biosignal 605 (eg, FIG. 5 ) of 500) can be obtained. In FIG. 6 , a horizontal axis may be referred to as a time axis in a time domain.

In 600a, the processor 210 may extract the first raw data 620 corresponding to the length of the first window 630 with respect to a reference viewpoint (eg, the first viewpoint 510 of FIG. 5 ). The processor 210 may perform fitting on the first raw data 620 . For example, the fitting may include a linear curve fitting method. The processor 210 may perform fitting to obtain a first fitting result 640 . According to an embodiment, the processor 210 may obtain a residual value by calculating a difference between the first fitting result 640 and the first raw data 620 . For example, reference number 660 may be referred to as an example of the residual value. The processor 210 may obtain a first difference value by calculating a root mean square (RMS) value for the residual values.

According to an embodiment, the processor 210 may compare the first difference value with the threshold value. For example, the threshold value may be referred to as a value obtained by adding a margin to the RMS value of the biosignal 500 . According to an embodiment, when the first difference value is less than the threshold value, it may be understood that the similarity between the first fitting result 640 and the first raw data 620 is high. In this case, the processor 210 may determine the median value 650 of the first fitting result 640 as the trend estimation value. After obtaining the trend estimation value, the processor 210 may change the reference time point to a second time point (eg, 512 in FIG. 5 ).

When the first difference value is equal to or greater than the threshold value, it may be understood that the similarity between the first fitting result 640 and the first raw data 620 is low. For example, in reference numeral 600a, the first raw data 620 may include transient response noise having high power (power). Accordingly, the DC component of the first raw data 620 includes a section that changes rapidly due to transient responsive noise, and the first fitting result 640 may not sufficiently reflect the DC component of the first raw data 620 . can According to an embodiment, when the first difference value is less than the threshold value, it may be referred to as a case in which the fitting result sufficiently reflects the DC component of the first raw data 620 . According to an embodiment, the processor 210 decreases the length of the first window 630 in order to re-estimate the trend of the biosignal 605 and based on the length of the second window 632 a reference time point (eg: Fitting for the first time point 610 ) may be performed again.

Referring to reference numeral 600b, the processor 210 may extract second raw data 622 corresponding to the length of the second window 632 with respect to the reference viewpoint 610 . The length of the second window 632 may be shorter than the length of the first window 630 . For example, the length of the second window 632 may be 1/2 of the length of the first window 630 . The reduction by 1/2 is an example, and the length of the second window 632 may be 1/n (n is a constant) of the length of the first window 630 . The processor 210 may perform fitting on the second raw data 622 . The processor 210 may perform fitting to obtain a second fitting result 642 . According to an embodiment, the processor 210 may obtain a residual value by calculating a difference between the second fitting result 642 and the second raw data 622 . For example, reference number 662 may be referred to as an example of the residual value. The processor 210 may obtain a second difference value by calculating an RMS value for the residual values. According to an embodiment, the processor 210 may compare the second difference value with the threshold value. According to an embodiment, at least a portion of the transient response noise may be included in the second window 632 . Accordingly, the DC component of the second raw data 622 may include a section that changes rapidly due to transient responsive noise, and the second fitting result 642 may not sufficiently reflect the DC component of the second raw data 622 . have. According to an embodiment, a case in which the second difference value is less than a threshold value may be referred to as a case in which the fitting result sufficiently reflects the DC component of the second raw data 622 . According to an embodiment, the processor 210 decreases the length of the second window 632 in order to estimate the trend of the biosignal 605 , and based on the length of the third window 634 , a reference time point (eg, the second Fitting for one time point 610) may be performed again.

At reference numeral 600c, the processor 210 may extract the third raw data 624 corresponding to the length of the third window 634 with respect to the reference viewpoint 610 . The length of the third window 634 may be shorter than the length of the second window 632 . The processor 210 may perform fitting on the third raw data 624 . The processor 210 may perform fitting to obtain a third fitting result 644 . For example, reference number 664 may be referred to as an example of the residual value. The processor 210 may obtain a residual value by calculating a difference between the third fitting result 644 and the third raw data 624 . The processor 210 may obtain a third difference value by calculating an RMS value for the residual values.

According to an embodiment, the processor 210 may compare the third difference value and the threshold value. Referring to reference numeral 600c, the third raw data 624 may include little or no transient response noise. For example, reference numeral 600c may have a third difference value less than a threshold value. The processor 210 may determine the median 654 of the third fitting result 644 as the trend estimation value.

According to an embodiment, the fitting of FIG. 6 may be repeatedly performed with respect to other reference points after the processor 210 obtains a trend estimation value for the reference time point 610 . The processor 210 may acquire a baseline signal (eg, the baseline signal 540 of FIG. 5 ) based on trend estimation values acquired at each reference point.

According to an embodiment, the processor 210 may decrease the length of the window (eg, the first window 630 ) based on the characteristics of the user. For example, the transient response noise may be generated based on the user's surrounding environment (eg, temperature, humidity) and/or the user's body characteristics (sweat, skin). The processor 210 may monitor the occurrence frequency of the transient noise according to the user. The processor 210 may reduce the window length in the case of a user with a small transient response than in the case of a user with a large transient response. By optimizing the degree of reduction in the length of the window to the user, the processor 210 may reduce current consumption due to repeated filtering. According to another embodiment, the processor 210 may reduce the length of the window (eg, the first window 630 ) to a specified length in the case of a user having a high frequency of occurrence of transient noise. The specified length may be determined based on a past fitting result stored in a memory (eg, the memory 220 of FIG. 2 ).

According to an embodiment, unlike in FIG. 6 , the processor 210 may increase the length of the window under a specified condition to reduce power consumption. For example, if there is no significant change in the DC component of the bio-signal (eg, the bio-signal 500 of FIG. 5 ) due to small fluctuations in the trend estimation value, the processor 210 determines that the bio-signal 500 is stable and determines the window can increase the length of In this case, the baseline signal cannot accurately reflect the DC component of the biosignal 500 compared to the case in which the window length is short, but power consumption due to filtering may be reduced. For example, the processor 210 may set the length of the window to increase when a predetermined time elapses. For another example, when determining that the biosignal 500 is a stable signal, the processor 210 may increase the length of the window.

7 is a flowchart illustrating a filtering operation of an electronic device, according to an embodiment.

Referring to operation 700 , the processor (eg, the processor 210 of FIG. 2 ) uses at least one sensor (eg, the at least one sensor 230 of FIG. 2 ) to generate a biosignal (eg, the biosignal of FIG. 3 ). (300)) can be obtained. The raw data of the biosignal may include noise (eg, the transient response noise 344 of FIG. 3 ) due to the movement of the subject. The processor 210 may perform a filtering operation for removing noise in operations 710 to 740 . For example, the subject may be a user who uses an electronic device (eg, the electronic device 200 of FIG. 2 ).

In operation 710 , the processor 210 determines the length of the initial window with respect to a plurality of viewpoints (eg, the first reference viewpoint 510 , the second reference viewpoint 512 , or the third reference viewpoint 514 of FIG. 5 ). Fitting can be performed sequentially. For example, information on the length of the initial window may be stored in the memory of the electronic device 200 (eg, the memory 220 of FIG. 2 ).

In operation 720, the processor 210 may obtain a difference value between the fitting result and the biosignal for each of a plurality of time points of the biosignal (eg, the biosignal 500 of FIG. 5 ). For example, the processor 210 may obtain a residual value by calculating a difference between the fitting result and the biosignal, and may obtain a first difference value by calculating an RMS value for the residual values.

In operation 730, the processor 210 may compare the first difference value with a threshold value.

When the first difference value is equal to or greater than the threshold value (operation 730 - YES), the processor 210 may adjust the initial window to the first window in operation 740 and then perform fitting based on the first window again. The first window may be shorter than the initial window. In FIG. 7 , the processor 210 returns to operation 720, obtains a difference value between the fitting result and the biosignal based on the length of the first window, compares it with a threshold value, and adjusts the window length if the difference value is greater than or equal to the threshold value The action can be repeated.

If the first difference value is less than the threshold value (730-NO), the processor 210 may determine a trend estimation value in operation 750 . For example, the processor 210 may determine a median value of a fitting result for each of a plurality of time points as a trend estimation value.

In operation 760 , the processor 210 may determine a baseline signal based on trend estimates for a plurality of time points. According to an embodiment, the baseline signal may be obtained by connecting trend estimation values. The baseline signal may be understood as an extracted signal corresponding to the DC component of the biosignal.

In operation 770 , the processor 210 may obtain a filtered signal by removing the baseline signal from the biosignal. The filtered signal may be understood as a signal in which the DC component of the biosignal is removed and the AC component is preserved. In the operation of removing the baseline signal from the biosignal, the transient response noise may also be removed. Accordingly, a significant biosignal (eg, 342 of FIG. 3 ) of the filtered signal may not be distorted by the transient response or a distortion section due to the transient response may appear short. According to an embodiment, the processor 210 may pass the filtered signal through a low pass filter (LPF) in order to remove high-frequency noise in a frequency band that does not overlap with a significant biosignal.

8 is a flowchart illustrating a sequential fitting method for a plurality of time points of a biosignal of an electronic device, according to an exemplary embodiment.

Referring to operation 800 , the processor (eg, the processor 210 of FIG. 2 ) receives at least a portion of the biosignal (eg, the first raw data 620 of FIG. 6 ) corresponding to the length of the window with respect to the reference viewpoint. can be extracted. For example, information on the length of the initial window may be stored in a memory (eg, the memory 220 of FIG. 2 ) of the electronic device (eg, the electronic device 200 of FIG. 2 ).

In operation 810 , the processor 210 may perform fitting on at least a portion of the extracted biosignal. For example, the fitting may be performed using a linear curve fitting method. According to an embodiment, the processor 210 may obtain a fitting result for at least a part of the biosignal through fitting.

In operation 820 , the processor 210 may obtain a difference value between the fitting result and at least a portion of the biosignal. According to an embodiment, the processor 210 may obtain a residual value by calculating a difference between the fitting result and at least a portion of the biosignal. The processor 210 may obtain a difference value by calculating an RMS value for the residual values.

In operation 830 , the processor 210 may determine whether the difference value is equal to or greater than a threshold value. Information on the threshold value may be stored in the memory 220 . For example, the threshold value may be determined based on a biosignal of a subject obtained in a stable measurement environment. The threshold value may be determined differently depending on the subject. As another example, the length of the window may be determined as a value in which distortion of the biosignal does not appear due to transient response noise in a stable measurement environment. When the difference value is less than the threshold value, the processor 210 may proceed to operation 840 ( 830 -NO). If the difference value is equal to or greater than the threshold value, the processor 210 may proceed to operation 850 ( 830 - YES).

In operation 850, the processor 210 may determine whether the length of the window is the minimum length. For example, in operation 860, although the length of the window is repeatedly reduced to reach the minimum length, a difference between the fitting result and at least a portion of the biosignal may still be greater than or equal to a threshold value. In this case, the processor 210 can no longer reduce the length of the window. For example, information about the minimum length of the window may be stored in the memory 220 . In this case, since the length of the window cannot be further reduced, the processor 210 may proceed to operation 840 to obtain a trend estimation value (850 -YES). If it is possible to further reduce the length of the window, operation 860 may proceed (850-NO).

In operation 860 , the processor 210 may decrease the length of the window. For example, the length of the reduced window may be 1/2 of the length of the existing window. As another example, the length of the reduced window may be 1/n (n is a constant) of the length of the existing window. Information on the constant n may be stored in the memory 220 . In an embodiment, the processor 210 may adjust the size of the constant n to reduce the amount of computation. For example, when the magnitude of the transient response noise is large, the processor 210 may reduce the length of the window to 1/4 of the length of the existing window. A case in which the magnitude of the transient response noise is above a certain level may be understood as a case in which a reduction in the amount of computation is required.

According to an embodiment, the processor 210 may decrease the length of the window and return to operation 800 to perform operations 800 to 830 again based on the reduced window length.

In operation 840, the processor 210 may determine a trend estimate. For example, the processor 210 may determine a median value of the fitting result as a trend estimation value.

In operation 870 , the processor 210 may change the reference time point to a time point moved by a specified time interval from the existing reference time point after determining the trend estimate value. The processor 210 may perform the fitting of FIG. 8 with respect to the changed reference time point.

According to an embodiment, the processor 210 may obtain trend estimation values by sequentially performing the operation of FIG. 8 for each of a plurality of time points of the biosignal, and then acquire a baseline signal based on the trend estimation values. have.

According to an embodiment, the processor 210 may acquire information on which section of the biosignal in which the transient response noise is generated based on the fitting result. For example, the processor 210 may obtain the timing when the length of the window is reduced as information about the generation period of the transient response noise. For example, the processor 210 may obtain information related to the magnitude of the transient response noise in association with the degree to which the length of the window is reduced.

9 illustrates a comparison of a signal obtained through a filtering method according to an exemplary embodiment with a signal obtained through a filtering method according to a comparative example. 900a illustrates a biosignal obtained by a processor (eg, the processor 210 of FIG. 2 ) using at least one sensor (eg, the at least one sensor 230 of FIG. 2 ). For example, the at least one sensor 230 may include an electrocardiogram (ECG) sensor. 900b illustrates a signal obtained by the processor 210 passing the biosignal through the frequency band pass filter. 900c illustrates a filtered signal (eg, the filtered signal 360 of FIG. 3 ) obtained by passing the biosignal through the filter according to FIGS. 7 to 8 by the processor 210 . In FIG. 9 , the horizontal axis may be referred to as a time axis in a time domain.

Referring to 900a, the biosignal obtained by the processor 210 may include transient response noise. The plurality of time points 910 indicate a time point at which the transient response noise appears.

Referring to 900b, when the transient response noise of the plurality of time points 910 of 900a passes through the frequency band pass filter, the transient response 920 may be caused. Biosignals near the plurality of viewpoints 910 may be distorted by the transient response 920 .

Referring to 900c, when the transient response noise of the plurality of viewpoints 910 of 900a passes through the filters according to FIGS. 7 to 8 according to various embodiments of the present disclosure, the transient response 930 may appear. In 900c, the transient response 930 may appear in a very short period compared to the transient response 920 in 900b, or may not appear at all. Accordingly, the biosignals near the plurality of viewpoints 910 may reduce signal quality degradation due to the transient response 930 .

According to an embodiment, it may be difficult to estimate the original signal because the transient response signal shown in 900b has a large waveform and a large time interval. However, since the transient response signal shown in 900c is an impulse signal, it is possible to easily identify or filter the transient response signal. In addition, since the transient response signal of 900c appears in a short period, it may not cause a break in the overall bio-signal. Accordingly, the processor 210 may obtain a higher quality filtered signal from the biosignal by using the filter according to FIGS. 7 to 8 .

According to an embodiment, the filtering according to FIGS. 7 to 8 may be performed based on a specified event. For example, the specified event may be referred to as a case in which the user manipulates a physical button of the electronic device 200 . As another example, the designated event may be referred to as a case in which the processor 210 detects a user's movement. The processor 210 may detect the user's movement using, for example, an acceleration sensor and/or a gyro sensor. Transient responsive noise can be caused by a specified event. The processor 210 may detect that a specified event has occurred and perform filtering based thereon.

10 is a diagram illustrating a result value obtained by performing filtering according to an exemplary embodiment. The description of the result value of FIG. 10 may be referred to by the description of the filtering method of FIGS. 7 to 8 . 1000a is a fitting according to an embodiment with the biosignal 1010 obtained by the processor (eg, the processor 210 of FIG. 2 ) using at least one sensor (eg, the at least one sensor 230 of FIG. 2 ) A baseline signal 1015 obtained using the method is also shown. 1000b illustrates a filtered signal (eg, the filtered signal 360 of FIG. 3 ) obtained by removing the baseline signal from the biosignal 1010 . 1000c shows a window length when the processor 210 obtains a trend estimation value for each of a plurality of time points. 1000d shows the final difference values obtained for each of the plurality of time points. In FIG. 10 , the horizontal axis represents sampling of the time axis in the time domain. The sample rate in FIG. 10 may be referred to as 250HZ.

The biosignal 1010 acquired by the processor 210 in 1000a may include transient response noise. The processor 210 may obtain the baseline signal 1015 by performing fitting according to FIGS. 7 to 8 . The baseline signal 1015 may be understood as an extracted DC component of the biosignal 1010 .

In 1000b, the processor 210 may obtain a filtered signal by removing the baseline signal 1015 from the biosignal 1010 . The filtered signal may contain a transient response for a very short time or no transient response at all. In the filtered signal, a filter response period due to transient noise is minimized, and there is only data loss for a very short time period, so that degradation of biosignal quality can be reduced. According to an embodiment, the filter response may appear long in a filter having a low cut-off frequency. The biosignal 1010 may be generally referred to as a signal of a low frequency band. Despite these characteristics of the biosignal 1010 , the processor 210 may obtain a filtered signal with a minimized filter response by using the filters of FIGS. 7 to 8 .

The window length of 1000c may be correlated with the difference value of 1000d. For example, when the difference value of 1000d is large (eg, when the difference value is 0.4 or more), it may be understood that the similarity between the fitting value and the raw data is low. When the similarity is low, the processor 210 may reduce the length of the window to perform fitting to the reference view again. Accordingly, the window length at one time point of 1000c corresponding to the time point at which the difference value is high at 1000d may appear short. Conversely, when the difference value is low (eg, the difference value is less than 0.4), it may be understood that the similarity between the fitted value and the raw data is high. When the similarity is high, since the processor 210 determines the trend estimation value, the window length at one point in time 1000c corresponding to the point in time at which the difference value is low in 1000d may appear long.

According to an embodiment, the electronic device (eg, the electronic device 200 of FIG. 2 ) includes at least one sensor (eg, the at least one sensor 230 of FIG. 2 ) and information on the length of an initial window and a memory (eg, the memory 220 of FIG. 2 ) storing information about the threshold, and a processor (eg, the processor of FIG. 2 ) operatively connected to the at least one sensor 230 and the memory 220 . 210)) may be included. The processor 210 obtains a bio-signal (eg, the bio-signal 300 of FIG. 3 ) using the at least one sensor 230 , and the initial Fitting based on the length of the window is sequentially performed, and fitting results (eg, the first fitting result 640 , the second fitting result 642 , and the third fitting result 644 of FIG. 6 ) are performed for each of the plurality of time points. )) and a first difference value (eg, the first difference value, the second difference value, and the third difference value of FIG. 6 ) of the biosignal 300 , and if the first difference value is equal to or greater than the threshold value, , after adjusting the initial window to the first window, fitting based on the length of the first window is performed again, and if the first difference value is less than the threshold value, a trend estimation value (eg, the trend estimation value of FIG. 6 ) ), obtains a baseline signal (eg, 350 in FIG. 3 ) based on the trend estimation values for the plurality of time points, and removes the baseline signal 350 from the biosignal 300 . It may be configured to acquire a signal (eg, the filtered signal 360 of FIG. 3 ). A length of the first window may be smaller than a length of the initial window.

According to an embodiment, the processor 210 obtains a second difference value between the fitting result and the biosignal performed based on the length of the first window, and when the second difference value is equal to or greater than the threshold value, After adjusting the first window to the second window, fitting based on the length of the second window is performed again, and if the second difference value is less than the threshold value, it may be further configured to determine a trend estimation value. . A length of the second window may be smaller than a length of the first window.

According to an embodiment, the processor 210 is configured to include at least a part of the biosignal 300 (eg, the first raw data of FIG. 6 ) corresponding to the length of the initial window or the adjusted window with respect to the plurality of viewpoints. 620 , the first raw data 622 , and the first raw data 624 may be further configured to be extracted. For example, the fitting may be fitting to at least a part of the biosignal. For example, The fitting may be a linear curve fitting.

According to an embodiment, the processor 210 may be further configured to determine the trend estimation value when the second difference value is equal to or greater than the threshold value and the length of the first window is the minimum length.

According to an embodiment, the electronic device 200 may further include a low pass filter (LPF), and the processor 210 may be further configured to pass the filtered signal through the LPF.

According to an embodiment, the biosignal 300 includes an electroencephalogram (EEG), an electroretinogram (ERG), an electrooculography (EOG), a respiratory (RESP), an electrocardiogram (ECG), It may be a signal related to at least one of an electromyogram (EGG), a pulse wave (photoplethysmogram, PPG), an electromyogram (EMG), an electroneurogram (ENG), and a galvanic skin response (GSR).

Claims (20)

In an electronic device,
at least one sensor;
a memory in which information on the length of an initial window and information on a threshold value are stored; and
a processor operatively coupled to the at least one sensor and the memory;
The processor is
Obtaining a biosignal using the at least one sensor,
sequentially performing fitting based on the length of the initial window with respect to a plurality of time points of the biosignal;
For each of the plurality of time points:
obtaining a first difference value between the fitting result and the biosignal;
If the first difference value is equal to or greater than the threshold value, after adjusting the initial window to the first window, fitting based on the length of the first window is performed again;
if the first difference value is less than the threshold value, determine a trend estimation value;
obtaining a baseline signal based on the trend estimation values for the plurality of time points;
set to obtain a filtered signal in which the baseline signal is removed from the biosignal,
The length of the first window is less than the length of the initial window,
electronic device.
According to claim 1,
The processor is
obtaining a second difference value between a fitting result performed based on the length of the first window and the biosignal;
If the second difference value is equal to or greater than the threshold value, after adjusting the first window to be a second window, fitting based on the length of the second window is performed again;
if the second difference value is less than the threshold value, further configured to determine a trend estimation value;
The length of the second window is less than the length of the first window,
electronic device.
3. The method of claim 2,
The processor is
further configured to extract at least a portion of the biosignal corresponding to the length of the initial window or the adjusted window based on the plurality of viewpoints,
electronic device.
4. The method of claim 3,
wherein the fitting is a fitting for at least a portion of the biosignal;
electronic device.
3. The method of claim 2,
The fitting is a linear curve fitting,
electronic device.
3. The method of claim 2,
The processor is
further configured to determine a trend estimation value when the second difference value is greater than or equal to the threshold value and the length of the first window is the minimum length;
electronic device.
3. The method of claim 2,
The electronic device further includes a low pass filter (LPF),
The processor is
further configured to pass the filtered signal to the LPF,
electronic device.
According to claim 1,
The biosignal is an electroencephalogram (EEG), an electroretinogram (ERG), a safety degree (electrooculography, EOG), a respiratory (RESP), an electrocardiogram (ECG), an electrocardiogram (EGG), a pulse wave. A signal related to at least one of (photoplethysmogram, PPG), electromyogram (EMG), electroneurogram (ENG), and galvanic skin response (GSR),
electronic device.
A method of operating an electronic device, comprising:
acquiring a biosignal using at least one sensor;
sequentially performing fitting based on the length of an initial window with respect to a plurality of time points of the biosignal;
For each of the plurality of time points:
obtaining a first difference value between the fitting result and the biosignal;
If the first difference value is equal to or greater than the threshold value, after adjusting the initial window to the first window, fitting based on the length of the first window is performed again;
determining a trend estimation value when the first difference value is less than the threshold value;
obtaining a baseline signal based on the trend estimation values for the plurality of time points; and
and obtaining a filtered signal obtained by removing the baseline signal from the biosignal.
The length of the first window is less than the length of the initial window,
Way.
10. The method of claim 9,
obtaining a second difference value between a fitting result performed based on the length of the first window and the biosignal;
if the second difference value is equal to or greater than the threshold value, adjusting the first window to a second window and then performing fitting again based on a length of the second window; and
If the second difference value is less than the threshold value, determining a trend estimation value; further comprising,
The length of the second window is less than the length of the first window,
Way.
11. The method of claim 10,
extracting at least a portion of the biosignal corresponding to the length of the initial window or the adjusted window based on the plurality of viewpoints; further comprising
Way.
12. The method of claim 11,
wherein the fitting is a fitting for at least a portion of the biosignal;
Way.
11. The method of claim 10,
The fitting is a linear curve fitting,
Way.
11. The method of claim 10,
determining a trend estimation value when the second difference value is equal to or greater than the threshold value and the length of the first window is the minimum length;
Way.
11. The method of claim 10,
The operation of passing the filtered signal through the low pass filter (LPF); further comprising,
Way.
A wearable electronic device comprising:
at least one sensor;
a memory in which information on the length of an initial window and information on a threshold value are stored; and
a processor operatively coupled to the at least one sensor and the memory;
The processor is
Detecting the wear of the user using the at least one sensor,
acquiring the user's bio-signal using the at least one sensor,
When the occurrence of a designated event based on the user's movement or manipulation is detected using the at least one sensor, the biosignal is set to obtain a filtered signal in which noise generated due to the designated event is removed,
wearable electronics.
17. The method of claim 16,
The processor is
sequentially performing fitting based on the length of the initial window with respect to a plurality of time points of the biosignal;
For each of the plurality of time points:
obtaining a first difference value between the fitting result and the biosignal;
If the first difference value is equal to or greater than the threshold value, after adjusting the initial window to the first window, fitting based on the length of the first window is performed again;
if the first difference value is less than the threshold value, determine a trend estimation value;
obtaining a baseline signal based on the trend estimation values for the plurality of time points;
set to obtain the filtered signal in which the baseline signal is removed from the biosignal,
The length of the first window is less than the length of the initial window,
wearable electronics.
18. The method of claim 17,
The processor is
obtaining a second difference value between a fitting result performed based on the length of the first window and the biosignal;
If the second difference value is equal to or greater than the threshold value, after adjusting the first window to be a second window, fitting based on the length of the second window is performed again;
if the second difference value is less than the threshold value, further configured to determine a trend estimation value;
The length of the second window is less than the length of the first window,
wearable electronics.
18. The method of claim 17,
The processor is
further configured to extract at least a portion of the biosignal corresponding to the length of the initial window or the adjusted window based on the plurality of viewpoints,
wearable electronics.
18. The method of claim 17,
The fitting is a linear curve fitting,
wearable electronics.

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