Apparatus and method for processing biological signal

Abstract

The present disclosure provides an apparatus and method of processing a biological signal. A biological signal processing method includes: receiving a biological signal from a sensor; performing a first signal processing based on variable frequency complex demodulation (VFCDM) on the biological signal; performing, one or more times, a VFCDM-based second signal processing on a periodic signal having an instantaneous frequency as a center frequency, the instantaneous frequency being calculated on the basis of the first signal processing; and estimating biological information from at least one processed signal calculated based on the second signal processing performed one or more times.

Description

Technical Field

[0001] The present disclosure relates to an apparatus and method for processing biological signals, and more particularly, to an apparatus and method for processing biological signals through ultra-high resolution time-varying frequency analysis. Background Art

[0002] Biosignals such as electrocardiogram (ECG) signals, photoplethysmogram (PPG) signals, and electromyogram (EMG) signals have time characteristics and periodic characteristics, and therefore, time and frequency analysis is required in order to extract the characteristics of the biosignals. Similarly, because the noise that penetrates into the biosignals has time characteristics and / or periodic characteristics, time and frequency analysis is required in order to remove the noise. In the case of separately analyzing the time characteristics and the periodic characteristics, unstable information may be extracted, and therefore, it is necessary to simultaneously analyze the time characteristics and the frequency characteristics. In addition, because the biosignals have nonlinear time-frequency characteristics, a signal processing method suitable for nonlinear time-frequency characteristic analysis is required. Summary of the invention

[0003] The present disclosure provides a biosignal processing method and apparatus for ultra-high resolution time-varying frequency analysis of biosignals, and further provides a biosignal processing method and apparatus that can improve the accuracy of bioinformation estimation through ultra-high resolution time-varying frequency analysis.

[0004] The present disclosure is not limited to the foregoing, and other objects not described herein will be clearly understood by those skilled in the art through the following description.

[0005] According to one aspect of the present disclosure, a biological signal processing method may include: receiving a biological signal from a sensor; performing a first signal processing based on variable frequency complex demodulation (VFCDM) on the biological signal; performing a second signal processing based on the VFCDM on a periodic signal with an instantaneous frequency as the center frequency at least once, wherein the instantaneous frequency is obtained based on the first signal processing; estimating biological information based on at least one processed signal obtained based on the second signal processing; and displaying the biological information.

[0006] The second signal processing may be performed N times, where N is an integer of at least 2. The (n+1)th second signal processing may be performed on the periodic signal having a time-varying frequency as the center frequency, the time-varying frequency being obtained based on the second signal processing performed the nth time, where n is an integer of at least 1 and less than N.

[0007] The first signal processing may include: performing complex demodulation on the biological signal to generate a first signal; removing signal components greater than or equal to the center frequency from the first signal to generate a second signal; reconstructing the biological signal based on the amplitude and phase obtained from each of the second signals; and calculating the instantaneous frequency from the reconstructed biological signal.

[0008] The second signal processing may include: performing complex demodulation on the periodic signal to generate a third signal; removing signal components greater than or equal to the center frequency from the third signal to generate a fourth signal; reconstructing the periodic signal based on the amplitude and phase obtained from each of the fourth signals; and obtaining a time-varying frequency from the reconstructed periodic signal.

[0009] Obtaining the time-varying frequency may include: performing Hilbert transform on the reconstructed periodic signal to obtain a time-varying amplitude and a time-varying phase of the reconstructed periodic signal; and calculating the time-varying frequency based on the time-varying phase.

[0010] The second signal processing may further include normalizing the amplitude obtained from the fourth signal to obtain a weight, and the weight may be applied to the time-varying amplitude of the reconstructed periodic signal.

[0011] Estimating the bio-information may include: obtaining the reconstructed periodic signal as a denoised bio-signal; and estimating a bio-parameter based on the denoised bio-signal.

[0012] Estimating the bio-information may include: comparing the reconstructed periodic signal with the periodic signal; and determining a quality of the bio-signal based on a comparison result.

[0013] The bio-signal may include at least one of an electrocardiogram (ECG) signal and a photoplethysmogram (PPG) signal, and estimating the bio-information may include estimating a heart rate based on a time-varying frequency obtained as a result of the second signal processing.

[0014] The bio-signal may have a time-based period.

[0015] According to one aspect of the present disclosure, a biological signal processing method may include: performing a first signal processing based on variable frequency complex demodulation (VFCDM) on a first periodic signal obtained from a biological signal; performing a second signal processing based on the VFCDM on a second periodic signal with an instantaneous frequency as a second center frequency, the instantaneous frequency being obtained based on the first signal processing; performing a third signal processing based on the VFCDM on a third periodic signal with a time-varying frequency as a third center frequency, the time-varying frequency being obtained based on the second signal processing; estimating biological information based on at least one processed signal obtained based on the first signal processing, the second signal processing and the third signal processing; and displaying the biological information.

[0016] Each of the second signal processing and the third signal processing may include: performing complex demodulation on an input periodic signal; performing low-pass filtering on the complex demodulated signal by using a low-pass filter having a cutoff frequency lower than a center frequency; reconstructing the periodic signal based on the amplitude and phase obtained from each of the filtered signals; and obtaining the time-varying frequency from the reconstructed periodic signal.

[0017] Obtaining the time-varying frequency may include: performing a Hilbert transform on the reconstructed periodic signal; and calculating a time-varying amplitude, a time-varying phase, and the time-varying frequency based on an analytical signal generated by the Hilbert transform.

[0018] Each of the second signal processing and the third signal processing may include normalizing the amplitude obtained from the filtered signal to obtain a weight, and the weight may be applied to the time-varying amplitude of the reconstructed periodic signal.

[0019] Estimating the bio-information may include estimating the bio-information based on the reconstructed periodic signal.

[0020] The bio-signal may include at least one of an electrocardiogram (ECG) signal and a photoplethysmogram (PPG) signal, and estimating the bio-information may include estimating a heart rate based on the time-varying frequency obtained as a result of the third signal processing.

[0021] A biological signal processing device for processing a biological signal may include: a display; a memory storing instructions; and at least one processor, the at least one processor being operably connected to the display and the memory, the at least one processor being configured to run the instructions to: perform a first signal processing based on variable frequency complex demodulation (VFCDM) on a first periodic signal obtained from the biological signal; perform a second signal processing based on the VFCDM at least once on a second periodic signal with an instantaneous frequency as a center frequency, the instantaneous frequency being obtained based on the first signal processing; estimate biological information based on at least one processed signal obtained based on the second signal processing; and instruct the display to display the biological information.

[0022] The at least one processor may also be configured to execute the instructions to: perform complex demodulation on the second periodic signal; perform low-pass filtering on the complex demodulated signal based on a cutoff frequency lower than the center frequency; reconstruct the second periodic signal based on the amplitude and phase obtained from each of the filtered signals; and obtain a time-varying frequency from the reconstructed second periodic signal.

[0023] The at least one processor may be further configured to execute the instructions to: normalize the amplitude obtained from the filtered signal to obtain a weight; and apply the weight to the amplitude obtained from the reconstructed second periodic signal through Hilbert transform.

[0024] The at least one processor may be further configured to execute the instructions to estimate the biological information based on the reconstructed second periodic signal.

[0025] The at least one processor may be further configured to execute the instructions to: compare the reconstructed second periodic signal with the second periodic signal; and determine a quality of the biological signal based on a comparison result.

[0026] The at least one processor may be further configured to execute the instructions to estimate a heart rate based on a result of the second signal processing.

[0027] The at least one processor may be further configured to execute the instructions to estimate a heart rate based on the time-varying spectrum obtained as a result of the second signal processing.

[0028] The biosignal processing apparatus may further include at least one sensor configured to measure the biosignal.

[0029] The at least one sensor may be further configured to provide at least one of an electrocardiogram (ECG) signal, a photoplethysmogram (PPG) signal, and an electromyogram (EMG) signal to the at least one processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Embodiments will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0031] Figure 1 is a block diagram illustrating an electronic device according to one or more embodiments;

[0032] Figure 2A An example in which an electronic device according to one or more embodiments is implemented as a wearable device is illustrated;

[0033] Figure 2B An example in which an electronic device according to one or more embodiments is implemented as a wearable device is illustrated;

[0034] Figure 3A illustrates a biosignal according to one or more embodiments;

[0035] Figure 3B illustrates a biosignal to which noise is added according to one or more embodiments;

[0036] Figure 4A illustrates time domain parameters of a biosignal according to one or more embodiments;

[0037] Figure 4B illustrates frequency domain parameters of a biosignal according to one or more embodiments;

[0038] Figure 5 is a flowchart illustrating a signal processing method according to one or more embodiments;

[0039] Figure 6 The invention illustrates in detail signal processing based on iterative time-varying frequency complex demodulation (iTFCDM) according to one or more embodiments;

[0040] Figure 7 illustrates iTFCDM based signal processing according to one or more embodiments;

[0041] Fig. 8A illustrates a time-frequency spectrum of a time series signal according to one or more embodiments;

[0042] Figure 8B illustrates a time-frequency spectrum of a time series signal according to one or more embodiments;

[0043] Figure 8C illustrates a time-frequency spectrum of a time series signal according to one or more embodiments;

[0044] Fig.8D illustrates a time-frequency spectrum of a time series signal according to one or more embodiments;

[0045] Fig. 9 is a flowchart illustrating a signal processing method according to one or more embodiments;

[0046] Fig. 10A illustrates a time-frequency spectrum of a photoplethysmogram (PPG) signal according to one or more embodiments;

[0047] Fig. 10B illustrates a time-frequency spectrum of a photoplethysmogram (PPG) signal according to one or more embodiments;

[0048] Fig.11 is a flowchart illustrating a signal processing method according to one or more embodiments;

[0049] Fig. 12A illustrates an electrocardiogram (ECG) signal according to one or more embodiments;

[0050] Fig. 12B illustrates a reconstructed ECG signal generated based on signal processing according to one or more embodiments;

[0051] Fig.13 is a flowchart illustrating a signal processing method according to one or more embodiments;

[0052] Fig.14A illustrates a reconstructed ECG signal generated based on signal processing according to one or more embodiments;

[0053] Fig. 14B illustrates a reconstructed ECG signal generated based on signal processing according to one or more embodiments;

[0054] Fig.15A illustrates a biosignal monitoring system according to one or more embodiments; and

[0055] Fig. 15B A biosignal monitoring system according to one or more embodiments is illustrated. DETAILED DESCRIPTION

[0056] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

[0057] Elements described as “module” or “part” or “unit” or “device” may be physically implemented by analog circuits and / or digital circuits including one or more of logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, etc.

[0058] Figure 1 is a block diagram illustrating an electronic device 100 according to one or more embodiments.

[0059] refer to Figure 1 , the electronic device 100 may include a processor 110, a sensor module 120, an input / output (I / O) device 130, a communication module 140, a memory 150, a storage device 160, and a power module 170. The electronic device 100 is not limited thereto and may further include various elements.

[0060] The electronic device 100 may be a user wearable device for monitoring a user's biological signal. The user may wear the electronic device 100 on a body part such as an arm, a leg, or a neck, and the electronic device 100 may sense the user's biological signal by using sensors (e.g., first to third sensors 121 to 123) included in the sensor module 120, and may monitor the user's health status.

[0061] The sensor module 120 may include a plurality of sensors (eg, a first sensor 121, a second sensor 122, and a third sensor 123). Figure 1 In the example, three sensors are illustrated, but the present disclosure is not limited thereto, and the sensor module 120 may include two sensors or four or more sensors.

[0062] The plurality of sensors may include a biosignal measurement sensor that measures a biosignal of the user. The plurality of sensors may sense different biosignals. In one or more embodiments, at least one of the plurality of sensors may be a motion sensor that senses the motion of the user. In one or more embodiments, at least one of the plurality of sensors may be a voice sensor (e.g., a microphone) that senses the voice of the user.

[0063] For example, the first sensor 121 may be an electrocardiogram (ECG) sensor. The ECG sensor may measure the user's ECG to generate an ECG signal. The ECG sensor may include a plurality of electrodes (or a plurality of sheets) (e.g., Figure 2A 35a and Figure 2B 35b) and analog front end (AFE).

[0064] An electrical signal may be applied to the user's skin through at least one electrode among the plurality of electrodes, and based on the electrical signal, an ECG signal representing electrical activity occurring in the heart may be output through at least another electrode among the plurality of electrodes.

[0065] In one or more embodiments, Figure 2A As shown, at least one of the plurality of electrodes may be arranged to contact the user's skin, and at least another of the plurality of electrodes may contact the user's skin when a body part (e.g., a finger) of the user intentionally contacts the corresponding electrode. Thus, the first sensor 121 may measure the ECG in response to a user's request (e.g., contact), or may measure the ECG in response to a request of the processor 110. The AFE may amplify and analog-to-digital convert the measured electrical signal to generate an ECG signal.

[0066] The first sensor 121 can transmit an ECG signal to the processor 110. For example, the ECG signal can be transmitted according to an interface based on a serial peripheral interface (SPI). However, the present disclosure is not limited thereto, and one or more interface protocols such as the following high-speed serial interface (HSSI) protocols can be applied between the first sensor 121 and the processor 110: inter-integrated circuit (I2C), improved inter-integrated circuit (I3C), mobile industrial processor interface (MIPI), universal asynchronous receiver / transmitter (UART), embedded display port (eDP), low voltage differential signaling (LVDS), universal serial interface (USI), ultra-path interface (UPI) and enhanced step-down differential signal transmission (eRVDS). However, the present disclosure is not limited thereto, and one of the above-mentioned various HSSI protocols can be applied to the first sensor 121 and the processor 110.

[0067] For example, the second sensor 122 may be a photoplethysmogram (PPG) sensor. The PPG sensor may measure the user's pulse wave to generate a PPG signal. The PPG sensor may measure the user's pulse wave based on the principle that when light is irradiated onto the user's skin, the amount of light absorbed changes due to the heartbeat.

[0068] The second sensor 122 may include a light emitting module and a light receiving module, the light emitting module may include one or more light emitting devices (e.g., a light emitting diode (LED)), and the light receiving module may include one or more light receiving devices (e.g., a photoelectric sensor including a photodetector). In one or more embodiments, the second sensor 122 may also include an AFE, and the AFE may drive the light emitting module in response to a measurement request from the processor 110, and may convert the signal from the light receiving module into analog-to-digital for transmission to the processor 110.

[0069] The second sensor 122 can measure the user's pulse wave to generate a PPG signal. The light emitting module and the light receiving module of the second sensor 122 can contact the user's skin, and therefore, the user's pulse wave can be measured all the time. For example, the second sensor 122 can continuously measure the pulse wave without the user's perception.

[0070] The second sensor 122 may transmit a PPG signal to the processor 110. For example, the PPG signal may be transmitted according to an SPI-based interface. However, the present disclosure is not limited thereto, and one of the above-mentioned various HSSI protocols may be applicable to the second sensor 122 and the processor 110.

[0071] The third sensor 123 may be a motion sensor. For example, the third sensor 123 may be implemented as an inertial measurement unit (IMU). The IMU may include an acceleration sensor and a gyroscope. The IMU may also include a geomagnetic sensor. The third sensor 123 may measure the user's motion (hereinafter referred to as motion) (e.g., posture changes, change speed of position movement, and displacement) to generate an IMU signal (e.g., a three-axis accelerometer signal). The IMU signal may be referred to as a motion sensing signal. The third sensor 123 may continuously measure the user's motion to generate an IMU signal.

[0072] The third sensor 123 may transmit an IMU signal to the processor 110. For example, the IMU signal may be transmitted according to an I2C-based interface. However, the present disclosure is not limited thereto, and one of the above-mentioned various HSSI protocols may be applicable to the third sensor 123 and the processor 110.

[0073] In one or more embodiments, the sensor module 120 may also include additional biosensors. For example, the sensor module 120 may also include a sensor for measuring the bioimpedance of the user, or a sensor for sensing changes or states of sweat, blood, urine, or iris. For example, the sensor module 120 may also include a respiratory (RSP, respiratory) sensor, a galvanic skin response (GSR, galvanic skin response) sensor, an electrical skin activity (EDA) sensor, an electromyography (EMG, Electromyography) sensor, a ballistocardiogram (BCG, ballistocardiogram) sensor, a sweat sensor for sensing hydration or dehydration, an iris sensor, an electroretinogram (ERG, electroretinogram) sensor, an electrooculogram (EOG, electrooculography) sensor, a body temperature sensor, or an electroencephalogram (EEG, electroencephalogram) sensor. In one or more embodiments, the sensor module 120 may include sensors such as voice sensors in addition to biosensors.

[0074] The processor 110 may control the overall operation of the electronic device 100, and may control each element (e.g., the sensor module 120, the I / O module 130, the communication circuit 140, the memory 150, the storage device 160, and the power module 170). In one or more embodiments, the processor 110 may include a micro control unit (MCU). However, the present disclosure is not limited thereto, and the processor 110 may include a processor such as a central processing unit (CPU) or a micro processing unit (MPU).

[0075] The processor 110 may perform signal processing on biological signals (eg, a PPG signal, an ECG signal, and an IMU signal) received from the sensor module 120 , and may monitor a user's health status based on the signal processing result.

[0076] The processor 110 can perform ultra-high resolution time-varying frequency analysis on the biosignal by performing signal processing on the biosignal based on the following iterative time-varying frequency complex demodulation (iTFCDM) scheme (algorithm). Here, iTFCDM can be a method of iteratively performing signal processing based on variable frequency complex demodulation (VFCDM) to amplify the signal and reduce noise based on an increase in the number of iterations.

[0077] VFCDM may be a nonlinear time-frequency characteristic analysis method based on Hilbert transform. The processor 110 may iteratively perform signal processing based on VFCDM to continuously update the reconstructed biological signal generated based on VFCDM.

[0078] In one or more embodiments, the processor 110 may normalize the amplitude of the reconstructed biosignal and may apply the normalized value to the instantaneous amplitude, which is time- and frequency-dependent and calculated based on the analytic signal extracted by the Hilbert transform. Thus, the signal of the center frequency may be enhanced.

[0079] Biosignals may have time characteristics and periodic characteristics. Noise may also have time characteristics and / or periodic characteristics. Therefore, time and frequency analysis may be required to extract the characteristics of the biosignal. Here, in the case of separately analyzing the time characteristics and the periodic characteristics (i.e., frequency characteristics), the information may not be analyzed normally due to instability. Therefore, both time analysis and frequency analysis may be required.

[0080] In the electronic device 100 according to one or more embodiments, the processor 110 can perform ultra-high resolution time-varying frequency analysis on a biosignal (e.g., a periodic signal obtained from the biosignal) by signal processing based on iTFCDM, and can estimate bioinformation based on the analysis result. Therefore, the electronic device 100 can provide accurate bioinformation to the user. The processor 110 can estimate bioinformation based on at least one processed signal calculated by signal processing based on iTFCDM.

[0081] In one or more embodiments, the biosignal may include an ECG signal (or a PPG signal), and the processor 110 may estimate the time-varying frequency generated by signal processing of the ECG signal based on iTFCDM as the heart rate. Alternatively, the processor 110 may estimate the heart rate based on the time-frequency spectrum generated by signal processing based on iTFCDM.

[0082] In one or more embodiments, the processor 110 can obtain a reconstructed biosignal (e.g., a reconstructed periodic signal) generated during the iTFCDM-based signal processing as a denoised biosignal, and can estimate biometric parameters (e.g., heart rate, respiratory rate, oxygen saturation, blood pressure, heart rate variability, stress, and sleep apnea) based on the denoised biosignal.

[0083] In one or more embodiments, the processor 110 may compare the biosignal (raw biosignal) with the reconstructed biosignal generated in the iTFCDM-based signal processing process, and may determine the quality of the biosignal based on the comparison result.

[0084] In one or more embodiments, the processor 110 can enhance the biosignal by iTFCDM-based signal processing. For example, the processor 110 can improve the signal-to-noise ratio (SNR) of the biosignal. The electronic device 100 can output the enhanced biosignal. Similarly, the processor 110 can perform iTFCDM-based signal processing during the signal extraction process to improve the SNR of a sensing signal such as a voice signal other than the biosignal.

[0085] The processor 110 may output the estimated biometric information through the display 131 and / or the audio module 132. Likewise, the processor 110 may transmit the estimated biometric information to an external device (e.g., a medical server) through the communication module 140. The processor 110 may output an enhanced voice signal through the audio module 132, or may transmit the enhanced voice signal to an external device through the communication module 140.

[0086] The display 131 may display various information based on the control performed by the processor 110. For example, the display 131 may display biological information about the user, such as heart rate and oxygen saturation. The display 131 may display atrial fibrillation detection information (whether atrial fibrillation occurs), arrhythmia information (the presence of arrhythmia and / or the type of arrhythmia), or suspected disease information. The display 131 may display information requesting the user's movement, and, for example, may display information requesting that a finger touch an ECG measurement electrode for ECG measurement and recommendation information for visiting a hospital.

[0087] The display 131 may be configured as at least one of various displays such as 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 micro LED, a flexible display, and a three-dimensional (3D) display. In one or more embodiments, the display 131 may be implemented as a touch screen type. In one or more embodiments, the display 131 may be implemented as an extended display or a flexible display.

[0088] The audio module 132 can output sound, and for example, the audio module 132 may include at least one of an audio codec, a microphone (MIC), a receiver, an earphone output, and a speaker. The audio module 132 may output information associated with the user's physical state, information associated with the symptoms of the user's health state, or additional information as an audio signal. For example, when atrial fibrillation is detected, the audio module 132 may output an alarm indicating that atrial fibrillation has occurred. In one or more embodiments, the audio module 132 may receive a user's voice signal or an external voice signal through a microphone, and may output a voice signal enhanced based on iTFCDM through a speaker.

[0089] The communication module 140 can communicate with an external device. In one or more embodiments, the communication module 140 may include a Bluetooth module. However, the present disclosure is not limited thereto, and for example, the communication module 140 may include a communication interface that may be accessed to a wireless local area network (WLAN) such as wireless fidelity (Wi-Fi), a wireless personal area network (WPAN), a wireless universal serial bus (USB), Zigbee, a near field communication (NFC), or a radio frequency identification (RFID), or a mobile cellular network such as a third generation (3G), a fourth generation (4G) or a long term evolution (LTE). In one or more embodiments, the communication module 140 may also include a communication interface that may be accessed to a wired local area network.

[0090] The communication module 140 can transmit information associated with the user's physical state, information associated with the symptoms of the user's health state, or additional information to an external electronic device (e.g., a user's smart phone). The communication module 140 can transmit the measured biosignals (e.g., ECG data) to the external electronic device. The external electronic device can transmit the biosignals to a medical server. In one or more embodiments, the communication module 140 can access a network (e.g., an access point or a mobile network) to transmit the ECG data directly to the medical server.

[0091] The memory 150 may be implemented as a volatile memory such as a dynamic random access memory (DRAM) or a static random access memory (SRAM), or a nonvolatile resistive memory such as a phase change random access memory (PRAM) or a resistive random access memory (ReRAM).

[0092] An operating program or application program running in the processor 110 may be loaded into the memory 150 and run by the processor 110. A program including instructions (or codes) for implementing the functions of the at least one processor 110 described above (e.g., a function of estimating biological information based on a signal processing result and / or based on signal processing of iTFCDM or at least one processed signal generated during signal processing) may be loaded into the memory 150 and run by (at least one) processor 110.

[0093] Likewise, the memory 150 may store data to be processed in the processor 110 or data generated by the processor 110. For example, the memory 150 may temporarily store biosignal measurement records (eg, measurement times, measurement times, etc.), estimated bio-information, and suspected disease information.

[0094] exist Figure 1 , the memory 150 is illustrated as a separate element provided independently from the processor 110, but is not limited thereto, and in one or more embodiments, the memory 150 may be integrated into the processor 110. The memory 150 may be implemented as a storage area in the processor 110.

[0095] The storage device 160 may be implemented as a non-volatile memory device such as a NAND flash memory or a resistive memory, and, for example, the storage device 160 may be provided as a memory card (e.g., a multimedia card (MMC), an embedded multimedia card (eMMC), a secure digital (SD) card, or a micro SD). The memory 160 may store data generated by the processor 110. The memory 160 may store bio-signal measurement records (e.g., number of measurements, measurement time, etc.), bio-information about the user, and suspected disease information. Similarly, the memory 160 may store measured bio-signals.

[0096] The power module 170 may include a battery, a charging circuit, and a power management unit (PMU). In one or more embodiments, the PMU may be integrated into the processor 110. The power module 170 may generate and provide a voltage used in the electronic device 100 based on power supplied from a battery or an external power source. The power module 170 may also charge the battery based on external power. The PMU may manage the power of the components of the battery of each component. For example, the PMU may supply power to each component, and may adjust the operating frequency or level (e.g., voltage level) of the power supplied to each component based on the operating state of the electronic device 100 or the operating state of each component. Similarly, the PMU may cut off the power.

[0097] Figure 2A and Figure 2B An example in which the electronic device 100 according to one or more embodiments is implemented as a wearable device is illustrated. Figure 2A A first surface (eg, a front surface) of the electronic device 100 may be illustrated, and Figure 2B A second surface (eg, backside surface) of the electronic device 100 may be illustrated.

[0098] refer to Figure 2A and Figure 2B The electronic device 100 may be, for example, a watch-type wearable device that can be worn on a user's wrist or a wearable device that can be worn on other parts of the human body (e.g., the head, forearm, thigh, or other parts of the human body capable of performing ECG measurements).

[0099] A display 33, an input button 34, a plurality of electrodes (e.g., a first electrode 35a and a second electrode 35b), at least one light emitting device 36, and at least one light receiving device 37 may be provided in a housing 31 configuring the external appearance of the electronic device 100. An auxiliary band 32 that enables the electronic device 100 to be worn on the user's body may be connected to the housing 31. Figure 1 Some of the described elements (eg, the processor 110 , the communication module 140 , at least one sensor (eg, the third sensor 123 ), the audio module 132 , the memory 150 , the storage device 160 , and the power module 170 ) may be disposed in the housing 31 .

[0100] The first electrode 35a and the second electrode 35b may be electrodes for detecting the same biosignal (e.g., ECG signal). The first electrode 35a may be disposed on the first surface of the electronic device 100, and the second electrode 35b may be disposed on the second surface of the electronic device 100. At least one light emitting device 36 and at least one light receiving device 37 may be disposed on the second surface of the electronic device 100. At least one light emitting device 36 and at least one light receiving device 37 may be devices for detecting the user's PPG and may be included in the second sensor ( Figure 1 122).

[0101] In one or more embodiments, an element for detecting another biosignal (eg, respiration, EDA, EMG, or EOG) may also be provided in the first surface or the second surface of the electronic device 100 .

[0102] Figure 3A The biosignals are shown graphically, and Figure 3B The figure shows a biological signal with noise added. Figure 3A and Figure 3B In the curve graph, the horizontal axis represents time, and the vertical axis represents the amplitude of the biological signal.

[0103] refer to Figure 3A, the biological signal may include signals such as an ECG signal, a PPG signal, an RSP signal, an EDA signal, and an EMG signal.

[0104] The ECG signal may be a biological signal representing an EDA signal occurring during a heartbeat. The ECG signal may include five waveforms P, Q, R, S, and T with different heights and intervals, and the P, Q, R, S, and T waves may be generated iteratively. The QRS complex wave may be an important feature of the ECG signal and may represent a heartbeat state.

[0105] The PPG signal may be a biological signal representing changes in blood flow rate with respect to the diastole and systole of the heart. The PPG signal may be generated by measuring an electrical signal obtained by light reflected by changes in blood flow in the skin. The PPG signal may be used to represent the period of a signal waveform and may include quasi-periodic pulses with peaks and valleys, thereby enabling measurement of heart rate.

[0106] The RSP signal may be a biosignal for estimating a user's breathing rate. The RSP signal may include a Sin or Cos waveform.

[0107] The EDA signal may represent the conductivity of the user's skin and may be measured based on the amount of current flowing through a plurality of electrodes attached to the user's skin. The EDA signal may include a skin conductance level (SCL) as a low-frequency component and a skin conductance response (SCR) as a high-frequency component, and the sympathetic skin response may be estimated based on changes in the EDA signal.

[0108] The EMG signal may be an electrical signal generated when a muscle contracts and relaxes, and may represent frequency characteristics of a wide band (eg, about 10 Hz to about 3 KHz).

[0109] As described above, each biosignal may be a periodic signal in which a pattern of a specific signal is periodically iterated. In addition, the period of the biosignal may change over time, and therefore, the biosignal may have both time characteristics and periodic characteristics. Therefore, time and frequency analysis may be required in order to extract the characteristics of the biosignal.

[0110] Figure 3B The figure illustrates the noise types of ECG signals. The ECG signal may be a minute biological signal with the maximum amplitude of the waveform measured in millivolts (mV). When measuring ECG, there may be various kinds of noise in the ECG signal, and these noises may have unique characteristics respectively.

[0111] Case 1 may represent a baseline drift of an ECG signal. Baseline drift may be a basis for measuring characteristic points of an ECG signal. Low-frequency artifacts due to breathing, charged electrodes, or user's movement may cause a baseline drift of an ECG signal, and in an ECG signal with a baseline drift, an accurate diagnosis may not be possible when the artifact factor is not removed.

[0112] Case 2 may represent noise caused by power line interference. The measurement environment of the ECG signal may be mainly exposed to power line interference of about 50 Hz to about 60 Hz. In ECG signal analysis, power line interference may cause distortion of a certain cycle. Cycles that are important factors in arrhythmia diagnosis like P waves and R waves may have small amplitudes and intervals similar to components of power line interference, and therefore, when the ECG signal is distorted, errors in diagnostic results may occur due to inaccurate detection.

[0113] Case 3 may represent noise caused by an EMG signal. The EMG noise may have a frequency characteristic of a wide band, and the impulse noise may have a characteristic that a needle-shaped impulse noise appears in an extremely short interval of several ms (milliseconds).

[0114] As described above, noise may also have a time characteristic and / or a period characteristic, and the time characteristic and / or the period characteristic may be different based on the type of noise. Therefore, time and frequency analysis may be required in order to remove noise from a biosignal.

[0115] Figure 4A and Figure 4B The time domain parameters and frequency domain parameters of the ECG signal (ECG voltage) are respectively illustrated. The time domain parameters can reflect the time characteristics, and the frequency domain parameters can reflect the frequency characteristics.

[0116] refer to Figure 4A ,The parameters in the time domain may include signal duration, signal period, signal frequency, signal maximum value, signal minimum value and amplitude.

[0117] The signal duration may be a value obtained by dividing the number of sampling times by the sampling frequency. The ECG signal may include iteratively generated P, Q, R, S, and T waves. The interval between an R peak and an adjacent R peak may be the period of the signal, and the reciprocal value of the period of the signal may be the frequency of the signal. The R peak (peak of the R wave) may be the maximum value of the signal, and the S peak (peak of the S wave) may be the minimum value of the signal. The voltage difference between the R peak and the S peak within one cycle may be the amplitude of the signal.

[0118] refer to Figure 4B, the parameters of the frequency domain may include fundamental frequency, minimum frequency, maximum frequency, harmonics, and bandwidth. The fundamental frequency may be the lowest frequency of a periodic waveform, and the harmonics may be multiples of the fundamental frequency. The bandwidth may be the difference between the maximum frequency and the minimum frequency.

[0119] Figure 5 is a flowchart illustrating a signal processing method according to one or more embodiments. Figure 5 The iTFCDM-based signal processing is illustrated and may be performed by at least one processor (eg, Figure 1 Therefore, the above reference Figure 1 The description of the signal processing performed by the processor 110 described above may be applicable to the present embodiment.

[0120] refer to Figure 5 In operation S110, the processor may receive a biological signal from a sensor. Figure 3A As described, the biological signal may be a periodic signal.

[0121] In operation S120, the processor may perform iTFCDM-based signal processing on the biosignal (eg, the first periodic signal). The iTFCDM-based signal processing may include first signal processing S121 and second signal processing S122 based on VFCDM.

[0122] In operation S121, the processor may perform a first signal processing based on VFCDM on the biosignal. A periodic signal may be represented by frequency, amplitude, and phase. The biosignal may include a center frequency, an instantaneous amplitude, an instantaneous phase, and a direct current (DC) power. In the first signal processing operation, a reconstructed biosignal from which noise has been removed may be calculated by complex demodulation and low-pass filtering, and the instantaneous frequency of the reconstructed biosignal may be calculated by Hilbert transform of the reconstructed biosignal.

[0123] In operation S122 , the processor may perform second signal processing based on VFCDM on a second periodic signal having the instantaneous frequency as a center frequency.

[0124] The second periodic signal may include the instantaneous frequency, instantaneous amplitude, instantaneous phase and DC power respectively calculated in the first signal processing operation S121. In the second signal processing operation on the second periodic signal, the reconstructed second periodic signal from which noise has been removed may be calculated by complex demodulation and low-pass filtering, and the time-varying frequency of the reconstructed second periodic signal may be calculated by Hilbert transform of the reconstructed second periodic signal. Moreover, the time-varying amplitude and time-varying phase of the reconstructed second periodic signal may be calculated.

[0125] Here, the second signal processing can be performed once or more times. The processor can perform the second signal processing once or iteratively. As the second signal processing is iteratively performed, the reconstructed second periodic signal can be updated. The signal component of the reconstructed second periodic signal can be amplified, and the noise can be reduced.

[0126] The processor may estimate the biological information based on at least one processed signal calculated by the second signal processing performed one or more times. For example, the processor may estimate the biological information based on a reconstructed signal (e.g., a reconstructed biological signal or a reconstructed second periodic signal) calculated by the second signal processing performed one or more times, or may estimate the biological information based on a time-varying frequency, a time-varying amplitude, and / or a time-varying phase each output as a result of the second signal processing performed one or more times.

[0127] Figure 6 iTFCDM based signal processing according to one or more embodiments is illustrated in more detail.

[0128] The biosignal can be represented by the first periodic signal S(t) of the following Formula 1.

[0129] [Formula 1]

[0130]

[0131] Here, C(t) can refer to DC power, A(t) can refer to amplitude, and f c can refer to the center frequency, and Can refer to the phase. Center frequency f c Here, when it is assumed that the center frequency of the biosignal is 0, the processor may shift the center frequency upward by 0.5 units to fs / 2 to detect the center frequency f c Here, fs may refer to the sampling rate of the biological signal.

[0132] In operation S121, the processor may perform a first signal processing based on VFCDM on the first periodic signal S(t). The first signal processing S121 may include complex demodulation S11, low pass filtering S12, signal reconstruction S13, Hilbert transform S14, and instantaneous frequency calculation S15.

[0133] In operation S11, the processor may perform complex demodulation on the first periodic signal S(t). The first periodic signal S(t) may be multiplied by And the result thereof may be expressed as the following Equation 2. The center frequency of the first period signal S(t) may be shifted to 0. In other words, the center frequency of the first period signal S(t) may be modulated.

[0134] [Formula 2]

[0135]

[0136] In operation S12, the processor may perform complex demodulation on the signal S c (t) performs low-pass filtering. The processor can process the complex demodulated signal S c (t) The application cut-off frequency is less than the center frequency f c Therefore, the second complex exponential term can be removed in Equation 2 and the third exponent And the filtered signal S of the following formula 3 can be obtained lp (t). Therefore, the center frequency f c or higher signal components can be removed.

[0137] [Formula 3]

[0138]

[0139] Amplitude A(t) and Phase They can be expressed as the following Formula 4 and Formula 5 respectively.

[0140] [Formula 4]

[0141] A(t)=2|S lp (t)|

[0142] [Formula 5]

[0143]

[0144] Here, real(·) and imag(·) may refer to the real part and imaginary part of a complex signal, respectively.

[0145] In operation S14, the processor can use the amplitude A(t) and the phase To reconstruct the first periodic signal (ie, biological signal), and the reconstructed first periodic signal s r (t) can be expressed as the following Formula 6.

[0146] [Formula 6]

[0147]

[0148] Compared with the first periodic signal S(t), the reconstructed first periodic signal s r In (t), it can be seen that the signal (e.g., the biological signal whose characteristics are to be extracted) is amplified and the noise is removed.

[0149] In operation S14, the processor may process the reconstructed first period signal s r (t) Perform Hilbert transform to detect instantaneous frequency and phase. Hilbert transform can only shift the phase of the real signal by ±90 degrees so that the amplitude and phase can be easily analyzed. The analytical signal of the following formula 7 can be calculated by Hilbert transform.

[0150] [Formula 7]

[0151] Y(t)=s r (t)+jH(s r (t))

[0152] By analyzing the signal Y(t), we can get the first periodic signal s r (t), and can generate the imaginary variable H(s r (t)). Here, H(·) can refer to the Hilbert transform operator. H(·) can assign a phase change of ±90 degrees (π / 2 radians) to all frequency components, and can change the sign of the change based on the sign of the frequency. The real part of the analytical signal can represent the original signal, and the imaginary part can represent the instantaneous phase of the signal.

[0153] The processor can calculate the instantaneous amplitude A(t)′ and the instantaneous phase A(t)′ as expressed in the following equations 8 and 9 based on the analytical signal Y(t):

[0154] [Formula 8]

[0155]

[0156] [Formula 9]

[0157]

[0158] The instantaneous frequency f(t)′ may refer to a time derivative of a phase (time rate), and may be calculated based on Equation 10 below.

[0159] [Formula 10]

[0160]

[0161] When the center frequency is assumed to have a value that varies over time rather than being a fixed frequency f c When the biological signal is represented by a periodic signal (e.g., a second periodic signal) having an instantaneous frequency f(t)′, the first periodic signal S(t) of Formula 1 can be expressed again as a second periodic signal S based on the instantaneous frequency f(t)′. n (t). Here, n may refer to the number of times the second signal processing is performed.v (t) can be the instantaneous amplitude A(t)′ of Equation 8, and It can be the instantaneous phase of Equation 9

[0162] [Formula 11]

[0163]

[0164] In operation S122, the processor may process the second period signal S n (t) Performing the second signal processing based on VFCDM. The second signal processing S122 may include complex demodulation S21, low-pass filtering S22, signal reconstruction S23, Hilbert transform S24 and time-varying frequency calculation S25.

[0165] In operation S21, the processor may process the second period signal S n (t) Perform complex demodulation. The second periodic signal S n (t) can be multiplied by And the result can be expressed as the following formula 12. The second periodic signal S n (t) can be shifted And therefore, its center frequency can be 0. In other words, the second periodic signal S n The center frequency of (t) can be modulated.

[0166] [Formula 12]

[0167]

[0168] In operation S22, the processor may process the complex demodulated signal S v.n (t) performs low-pass filtering. The processor can process the complex demodulated signal S v.n (t) The application cut-off frequency is less than the center frequency Therefore, the filtered signal S of the following formula 13 can be obtained: lp.n (t).

[0169] [Formula 13]

[0170]

[0171] AmplitudeA v (t) and phase They can be expressed as the following Equations 14 and 15 respectively.

[0172] [Formula 14]

[0173] A v (t) = 2|S lp.n (t)|

[0174] [Formula 15]

[0175]

[0176] In operation S24, the processor can use the amplitude A v (t) and phase To reconstruct the second periodic signal (ie, biological signal), and the reconstructed second periodic signal s r.n (t) can be expressed as the following formula 16.

[0177] [Formula 16]

[0178]

[0179] In operation S24, the processor may process the reconstructed second periodic signal s r.n (t) Applying Hilbert transform. In operation S25, the processor may calculate the time-varying frequency based on the analytical signal calculated by the Hilbert transform. The processor may calculate the time-varying amplitude A based on the following equations 17 and 18: n (t)′ and time-varying phase

[0180] [Formula 17]

[0181]

[0182] [Formula 18]

[0183]

[0184] The processor can be based on Calculate the time-varying frequency f expressed in the following equation 19: n (t)′.

[0185] [Formula 19]

[0186]

[0187] It can be based on the time-varying amplitude A n (t)′, time-varying phase and the time-varying frequency f n (t)′ generates a time-frequency spectrum.

[0188] In one or more embodiments, the processor may iteratively perform the second signal processing S122. The processor may perform the second signal processing S122 on the signal with the time-varying frequency f n (t)′ is a periodic signal with a center frequency, and the second signal processing S122 is performed for the second time. For example, the time-varying frequency f n (t)′ is applied to the second periodic signal S based on Eq. 11 n(t).

[0189] As described above, the processor may perform the second signal processing S122 N times (where N may be an integer of 2 or more), and the time-varying frequency f calculated by the second signal processing performed the nth time may be n (t)′ may be applied as the center frequency of the periodic signal on which the (n+1)th second signal processing is performed. Similarly, the time-varying amplitude A calculated by the second signal processing performed the nth time n (t)′ may be applied as the amplitude A of the periodic signal on which the (n+1)th second signal processing is performed. v (t), and the time-varying phase calculated by the second signal processing performed for the nth time The phase of the periodic signal that can be applied to perform the (n+1)th second signal processing

[0190] Figure 7 Illustrated is iTFCDM based signal processing according to one or more embodiments. Figure 7 yes Figure 6 Therefore, repeated description is omitted.

[0191] refer to Figure 7 The second signal processing S122a may include complex demodulation S21, low-pass filtering S22, signal reconstruction S23, Hilbert transform S24, normalization S26 and time-varying frequency calculation S25. Figure 6 Compared with the second signal processing S122, Figure 7 The second signal processing S122a may further include normalization S26. The processor may normalize the amplitude by using the maximum value, minimum value, intermediate value, and standard normal distribution value of the amplitude with respect to the time axis. For example, the processor may normalize the amplitude A calculated based on Formula 14 v (t) divided by the maximum value A v,max (t) to normalize. Here, the maximum value A v,max (t) may refer to a maximum value within a full frequency range (eg, a frequency range from a minimum frequency to a maximum frequency).

[0192] In the time-varying frequency calculation operation S25, the normalized result value (eg, the normalized value) may be applied as the instantaneous amplitude A n The weight W of (t)′ n The instantaneous amplitude A can be calculated based on the following formula 20: n (t)′.

[0193] [Formula 20]

[0194]

[0195] A weight may be applied to the instantaneous amplitudes about all center frequencies.

[0196] As described above, in the iTFCDM-based signal processing, a method in which weights are assigned to amplitudes may be referred to as weighted iTFCDM (wiTFCDM). Therefore, a signal of a center frequency may be enhanced.

[0197] FIG. 8A to FIG. 8D The time-frequency spectrum of a time series signal is shown in the figure.

[0198] Fig. 8A shows the spectrum obtained based on short time Fourier transform (STFT), Figure 8B shows the spectrum obtained based on VFCDM, Figure 8C shows a spectrum obtained based on iTFCDM according to one or more embodiments, and Fig.8D Spectra obtained based on wiTFCDM are shown. Each spectrum may represent a time-varying analysis of a cross-chirped signal including approximately 10 dB of Gaussian white noise.

[0199] As you can see, Fig. 8A The resolution of the spectrum obtained based on STFT is relatively low. STFT may have a trade-off relationship between time resolution and frequency resolution. When time resolution is improved, frequency resolution may be reduced, but when frequency resolution is improved, time resolution may be reduced. Therefore, high-resolution time-frequency analysis may be difficult.

[0200] Figure 8B In the figure, high-resolution time-frequency spectrum can be obtained based on VFCDM. It can be seen that the cross-chirp signal is very clear.

[0201] As you can see, Figure 8C The spectrum and Fig.8D The spectrum ratio obtained based on wiTFCDM Figure 8B The spectrum of shows the cross-chirp signal more clearly. Similarly, it can be seen that Fig.8D The noise component is clearly removed from the spectrum.

[0202] Fig. 9 is a flowchart illustrating a signal processing method according to one or more embodiments. Fig. 9 The signal processing method may be performed by at least one processor (e.g., Figure 1 110) for execution.

[0203] refer to Fig. 9In operation S210, the processor may receive a biosignal from a sensor. The biosignal may be an ECG signal and / or a PPG signal.

[0204] In operation S220, the processor may perform signal processing based on iTFCDM on the biosignal. A time-varying frequency including the signal processing may be calculated. The processor may perform signal processing based on wiTFCDM. Hereinafter, in the present disclosure, iTFCDM may have a meaning including wiTFCDM.

[0205] The processor may estimate the heart rate based on the time-varying frequency. For example, the processor may perform iTFCDM-based signal processing on the ECG signal or the PPG signal, and may estimate the time-varying frequency calculated as a result of the signal processing as the heart rate.

[0206] In one or more embodiments, the processor may obtain a time-frequency spectrum based on iTFCDM. When iTFCDM-based signal processing is performed on an ECG signal or a PPG signal, the signal may be amplified and noise may be reduced.

[0207] Fig. 10A and Fig. 10B The time-frequency spectrum of the PPG signal is shown.

[0208] Fig. 10A shows the spectrum obtained based on STFT, and Fig. 10B The spectrum obtained based on VFCDM is shown.

[0209] exist Fig. 10A In , the signal can appear strongly at various frequencies. Therefore, it may not be easy to extract the center frequency in order to estimate the heart rate. However, in Fig. 10B In the iTFCDM according to one or more embodiments, the center frequency can be shown more clearly as the VFCDM is iteratively performed. The extracted center frequency can be estimated as the heart rate.

[0210] Fig.11 is a flowchart illustrating a signal processing method according to one or more embodiments. Fig.11 The signal processing method may be performed by at least one processor (e.g., Figure 1 110) for execution.

[0211] refer to Fig.11 In operation S310, the processor may receive a biosignal from a sensor. The biosignal may be an ECG signal and / or a PPG signal.

[0212] In operation S320, the processor may perform signal processing based on iTFCDM on the biological signal. In the process of performing the first signal processing and the one or more second signal processing, the instantaneous frequency, the time-varying frequency, the time-varying amplitude, the time-varying phase, the instantaneously reconstructed first periodic signal and the one or more reconstructed second periodic signals may be calculated as the processing signal.

[0213] In operation S330, the processor may obtain a reconstructed signal (eg, a reconstructed first periodic signal or a reconstructed second periodic signal) calculated by at least one of the first signal processing and the one or more second signal processing as a denoised signal. Figure 6 As described, in the reconstructed signal, the signal component can be amplified and the noise can be reduced.

[0214] In operation S340, the processor may estimate biological information based on the denoised biological signal. For example, the processor may estimate biological parameters such as heart rate, respiratory rate, oxygen saturation, blood pressure, heart rate variability, stress, or sleep apnea based on the denoised biological signal.

[0215] Fig. 12A The ECG signal is shown, and Fig. 12B A reconstructed ECG signal generated in a signal processing operation according to one or more embodiments is illustrated.

[0216] refer to Fig. 12A , because the noise component NC is added to the ECG signal, the ECG signal may be distorted. In addition to the peak caused by the heartbeat, the peak caused by the noise component NC may also be added to the ECG signal. Figure 4A As described, the heart rate can be estimated based on the RR intervals. However, an inaccurate heart rate may be estimated due to peaks caused by the noise component NC.

[0217] Fig. 12B The reconstructed ECG signal generated in the iTFCDM-based signal processing operation according to one or more embodiments is illustrated. The noise component can be removed in the reconstructed ECG signal, and therefore, the peak caused by the noise component can be removed. Therefore, an accurate heart rate can be estimated based on the reconstructed ECG signal.

[0218] Fig.13 is a flowchart illustrating a signal processing method according to one or more embodiments. Fig.13 The signal processing method may be performed by at least one processor (e.g., Figure 1 110) for execution.

[0219] refer to Fig.13, the processor may receive a biosignal from a sensor in operation S410, and may perform iTFCDM-based signal processing on the biosignal in operation S420.

[0220] In operation S430, the processor may compare the biosignal with a reconstructed signal calculated by at least one of the first signal processing and / or one or more second signal processing. In operation S440, the processor may determine the quality of the biosignal based on the comparison result. For example, the processor may calculate the similarity between the biosignal and the reconstructed signal, and determine a quality indicator based on the similarity. The quality indicator may represent the quality of the original biosignal. As the similarity between the original biosignal and the reconstructed signal increases, the quality of the biosignal may be determined to be high.

[0221] Fig.14A The situation that the similarity between ECG signal and the reconstructed ECG signal is high is illustrated.As shown in the figure, the characteristic point (for example, PQRST wave) in the original ECG signal can have a normal form.The reconstructed ECG signal can be a signal in which noise is removed and signal components are amplified in the original ECG signal.Therefore, as the quality of the original ECG signal improves, the similarity between the ECG signal and the reconstructed ECG signal can be high.

[0222] Fig. 14B The diagram shows a situation where the similarity between the ECG signal and the reconstructed ECG signal is low. The feature points in the original ECG signal may have an abnormal form. The T wave may be very high, and the S wave may be very deep. Such abnormal feature points may be removed as noise in the reconstructed ECG signal. For example, the T wave and S wave in the reconstructed ECG signal may be different from the T wave and S wave in the original ECG signal. Therefore, the similarity between the reconstructed ECG signal and the original ECG signal may be low.

[0223] Reference again Fig.13 , the processor may determine the quality or quality indicator based on the reference value. For example, when the similarity is less than a first reference value, the processor may determine that the quality of the original biological signal is low, and when the similarity is greater than or equal to the first reference value, the processor may determine that the quality of the original biological signal is high. However, the present disclosure is not limited thereto, and the processor may determine quality indicators having various levels (or ranges) based on the similarity.

[0224] In one or more embodiments, the processor may estimate the bio-information based on the quality indicator. For example, when the quality indicator of the bio-signal is low (e.g., the quality indicator may be less than a threshold), the processor may exclude the use of the bio-signal in the bio-information estimation operation. As another example, the processor may apply a weight to the bio-signal in the bio-information estimation operation based on the quality indicator of each bio-signal.

[0225] In one or more embodiments, the processor may determine the reliability of the estimated biometric information based on the quality indicator. For example, when the quality indicator of the biometric signal used in the estimation operation of the estimated biometric information is low, the processor may determine that the reliability of the estimated biometric information is low. As another example, the processor may use the quality indicator as a weight when determining the reliability of the estimated biometric information.

[0226] As referenced above Figures 9 to 14B As described, bio-information may be estimated from a bio-signal based on the iTFCDM-based high-resolution time-frequency analysis according to one or more embodiments, and the accuracy of the bio-information may be enhanced.

[0227] Fig.15A A biosignal monitoring system 1000a according to one or more embodiments is illustrated, and Fig. 15B A biosignal monitoring system 1000b according to one or more embodiments is illustrated.

[0228] refer to Fig.15A and Fig. 15B , each of the biosignal monitoring system 1000a and the biosignal monitoring system 1000b may include a biosignal monitoring device 1100 and a data receiving device 1200. The biosignal monitoring system 1000b may further include a server 1300.

[0229] Reference above Figure 1 The electronic device 100 (e.g., a wearable device) described may be applicable to the biosignal monitoring device 1100. The above description of the electronic device 100 and its operation may be applicable to the present embodiment. For example, the data receiving device 1200 may be an electronic device including a mobile communication interface, such as a smart phone, a tablet personal computer (PC), or a mobile communication device. The server 1300 may be, for example, a medical server or a cloud server.

[0230] The biosignal monitoring device 1100 can measure a biosignal such as a PPG signal, an ECG signal, or an EMG signal, and can perform ultra-high resolution time-frequency analysis on the biosignal through the above-mentioned iTFCDM-based signal processing. Moreover, the level of the biosignal can be enhanced in the signal processing operation, and a reconstructed biosignal with reduced noise can be generated. The biosignal monitoring device 1100 can estimate bioinformation based on at least one processed signal or signal processing operation generated as a result of the signal processing. Therefore, the accuracy of the bioinformation can be improved.

[0231] The biosignal monitoring device 1100 may output estimated bio-information (e.g., heart rate, respiratory rate, oxygen saturation, blood pressure, heart rate variability, stress, or sleep apnea) through a display or a speaker. In one or more embodiments, the bio-signal monitoring device 1100 may diagnose a disease based on the estimated bio-information. For example, the bio-signal monitoring device 1100 may determine atrial fibrillation based on the heart rate, and may provide event information indicating atrial fibrillation detection to the user.

[0232] The data receiving device 1200 may transmit the biosignal, estimated bioinformation, or diagnosed disease to the server 1300. In one or more embodiments, the biosignal monitoring device 1100 may include a mobile communication interface, and may directly transmit the biosignal, estimated bioinformation, or diagnosed disease to the server 1300. The medical team may detect (diagnose) atrial fibrillation based on the ECG signal transmitted to the server 1300.

[0233] In one or more embodiments, the data receiving device 1200 or the server 1300 may perform ultra-high resolution time-frequency analysis on the biosignal received from the biosignal monitoring device 1100 through the above-described iTFCDM-based signal processing.

[0234] In the biosignal monitoring system 1000a and the biosignal monitoring system 1000b according to one or more embodiments, the biosignal monitoring device 1100, the data receiving device 1200, or the server 1300 may perform iTFCDM-based signal processing on the biosignal measured by the biosignal monitoring device 1100 to analyze the ultra-high resolution time-frequency. Also, the bioinformation may be estimated based on the analysis result, and thus, the accuracy of the bioinformation may be improved.

[0235] In the above, exemplary embodiments have been described in the drawings and the specification. The embodiments have been described by using the terms described herein, but this is only used to describe the present disclosure and is not used to limit the meaning or scope of the present disclosure defined in the attached claims. Therefore, it can be understood by those of ordinary skill in the art that various modifications and other equivalent embodiments can be implemented according to the present disclosure. Therefore, the subject matter and scope of the present disclosure can be limited based on the subject matter and scope of the attached claims.

[0236] While the present disclosure has been particularly shown and described with reference to embodiments thereof, it will be understood that various changes in form and details may be made therein without departing from the spirit and scope of the appended claims.

Claims

1. A biological signal processing method, the biological signal processing method comprising: receiving a biological signal from a sensor; Performing a first signal processing based on frequency conversion complex demodulation (VFCDM) on the biological signal; Performing a second signal processing based on the VFCDM at least once on a periodic signal having an instantaneous frequency as a center frequency, wherein the instantaneous frequency is obtained based on the first signal processing; estimating biological information based on at least one processed signal obtained based on the second signal processing; and The biological information is displayed.

2. The biological signal processing method according to claim 1, in, The second signal processing is performed N times, where N is an integer of at least 2, and The second signal processing is performed for the n+1th time on the periodic signal with the time-varying frequency as the center frequency, and the time-varying frequency is obtained based on the second signal processing performed for the nth time, wherein n is an integer that is at least 1 and less than N.

3. The biological signal processing method according to claim 1, wherein: The first signal processing comprises: Performing complex demodulation on the biological signal to generate a first signal; removing a signal component greater than or equal to the center frequency from the first signal to generate a second signal; reconstructing the biosignal based on the amplitude and phase obtained from each of the second signals; and The instantaneous frequency is calculated from the reconstructed biosignal.

4. The biological signal processing method according to claim 1, wherein: The second signal processing comprises: Performing complex demodulation on the periodic signal to generate a third signal; removing a signal component greater than or equal to the center frequency from the third signal to generate a fourth signal; reconstructing the periodic signal based on the amplitude and phase obtained from each of the fourth signals; and The time-varying frequency is obtained from the reconstructed periodic signal.

5. The biological signal processing method according to claim 4, wherein: Obtaining the time-varying frequency includes: performing a Hilbert transform on the reconstructed periodic signal to obtain a time-varying amplitude and a time-varying phase of the reconstructed periodic signal; and The time-varying frequency is calculated based on the time-varying phase.

6. The biological signal processing method according to claim 5, in, The second signal processing further includes normalizing the amplitude obtained from the fourth signal to obtain a weight, and Wherein, the weight is applied to the time-varying amplitude of the reconstructed periodic signal.

7. The biological signal processing method according to claim 4, wherein: It is estimated that the biological information includes: Obtaining the reconstructed periodic signal as a denoised biological signal; and A biological parameter is estimated based on the de-noised biological signal.

8. The biological signal processing method according to claim 4, wherein: It is estimated that the biological information includes: comparing the reconstructed periodic signal with the biosignal; and The quality of the biosignal is determined based on the comparison result.

9. The biological signal processing method according to claim 1, in, The biosignal includes at least one of an electrocardiogram signal and a photoplethysmogram signal, and wherein estimating the biological information includes estimating a heart rate based on a time-varying frequency obtained as a result of the second signal processing.

10. The biological signal processing method according to claim 1, wherein: The biosignal has a time-based period.

11. A biological signal processing method, the biological signal processing method comprising: Performing first signal processing based on frequency conversion complex demodulation (VFCDM) on a first periodic signal obtained from a biological signal; performing a second signal processing based on the VFCDM on a second periodic signal having an instantaneous frequency as a second center frequency, wherein the instantaneous frequency is obtained based on the first signal processing; performing a third signal processing based on the VFCDM on a third periodic signal having a time-varying frequency as a third center frequency, wherein the time-varying frequency is obtained based on the second signal processing; estimating biological information based on at least one processed signal obtained based on the first signal processing, the second signal processing, and the third signal processing; and The biological information is displayed.

12. The biological signal processing method according to claim 11, wherein: Each of the second signal processing and the third signal processing comprises: Perform complex demodulation on the input periodic signal; low pass filtering the complex demodulated signal by using a low pass filter having a cut-off frequency lower than the center frequency; reconstructing the periodic signals based on amplitudes and phases obtained from the filtered signals respectively; and The time-varying frequency is obtained from the reconstructed periodic signal.

13. The biological signal processing method according to claim 12, wherein: Obtaining the time-varying frequency includes: performing a Hilbert transform on the reconstructed periodic signal; and The time-varying amplitude, the time-varying phase, and the time-varying frequency are calculated based on the analytical signal generated by the Hilbert transform.

14. The biological signal processing method according to claim 13, in, Each of the second signal processing and the third signal processing includes normalizing the amplitude obtained from the filtered signal to obtain a weight, and Wherein, the weight is applied to the time-varying amplitude of the reconstructed periodic signal.

15. A biological signal processing device for processing a biological signal, the biological signal processing device comprising: monitor; A memory storing instructions; as well as at least one processor operably connected to the display and the memory and configured to execute the instructions to: Performing first signal processing based on frequency conversion complex demodulation (VFCDM) on a first periodic signal obtained from the biological signal; Performing a second signal processing based on the VFCDM at least once on a second periodic signal having an instantaneous frequency as a center frequency, wherein the instantaneous frequency is obtained based on the first signal processing; estimating biological information based on at least one processed signal obtained based on the second signal processing; and The display is instructed to display the biological information.

16. The biological signal processing device according to claim 15, wherein: The at least one processor is further configured to execute the instructions to: Performing complex demodulation on the second periodic signal; Low-pass filtering the complex demodulated signal based on a cutoff frequency lower than the center frequency; reconstructing the second periodic signal based on the amplitude and phase respectively obtained from the filtered signal; and The time-varying frequency is obtained from the reconstructed second periodic signal.

17. The biological signal processing device according to claim 16, wherein: The at least one processor is further configured to execute the instructions to: normalizing the amplitude obtained from the filtered signal to obtain a weight; and The weight is applied to the amplitude obtained from the reconstructed second periodic signal through Hilbert transform.

18. The biological signal processing device according to claim 16, wherein: The at least one processor is further configured to execute the instructions to estimate the biological information based on the reconstructed second periodic signal.

19. The biological signal processing device according to claim 16, wherein: The at least one processor is further configured to execute the instructions to: comparing the reconstructed second periodic signal with the biological signal; and The quality of the biosignal is determined based on the comparison result.

20. The biological signal processing device according to claim 15, wherein: The at least one processor is further configured to execute the instructions to estimate a heart rate based on a result of the second signal processing.

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