Patent Publication Number: US-2023157554-A1

Title: Apparatus and method for detecting bio-signal feature

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application is a divisional application of U.S. application Ser. No. 16/044,020, filed Jul. 24, 2018, which claims priority from Korean Patent Application No. 10-2017-0094310, filed on Jul. 25, 2017 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     1. Field 
     Apparatuses and methods consistent with exemplary relate to detecting a bio-signal feature. 
     2. Description of Related Art 
     Healthcare technology has attracted much attention due to the rapid entry into an aging society and relevant social problems such as increase in medical expenses. Accordingly, medical devices that can be utilized by hospitals and inspection agencies as well as small-sized medical devices that can be carried by individuals such as wearable devices are being developed. In addition, such a small-sized medical device is worn by a user in the form of a wearable device capable of directly measuring cardiovascular health status such as blood pressure or the like, so that the user can directly measure and manage cardiovascular health status. 
     Therefore, recently, studies on a method of estimating a blood pressure by analyzing a bio-signal for minimization of a device, particularly, a method of stably detecting features of a bio-signal, which are used in estimating the blood pressure, with a small amount of computation, have been conducted. 
     SUMMARY 
     One or more exemplary embodiments provide a bio-signal feature detection apparatus and method capable of detecting a bio-signal feature based on the bio-signal and an envelope signal. 
     According to an aspect of an exemplary embodiment, there is provided an apparatus for detecting a bio-signal feature, the apparatus including: a bio-signal acquirer configured to acquire a bio-signal; and a processor configured to generate an envelope signal of the bio-signal, and detect at least one feature of the bio-signal based on a difference between the envelope signal and the bio-signal. 
     The bio-signal may be a pulse wave signal, a first-order differential signal of the pulse wave signal, or a second-order differential signal of the pulse wave signal. 
     The bio-signal acquirer may include at least one of a photoplethysmogram (PPG) sensor that detects a PPG signal or a pressure pulse wave signal that corresponds to the bio-signal, and a communication interface configured to receive the bio-signal from an external device. 
     The candidate features may include a reflection wave component constituting the bio-signal. 
     The processor may detect at least one peak point or at least one valley point in one period of the bio-signal, and may generate the envelope signal by linearly connecting a start point, the at least one peak point or valley point, and an end point of the bio-signal in the period. 
     The processor may determine an effective range of the bio-signal by setting a minimum point in the period of the bio-signal as the start point and setting a last zero crossing point or a last valley point in the period of the bio-signal as the end point. 
     The processor may calculate a plurality of separate areas between a first graph representing the envelope signal and a second graph representing the bio-signal, and may detect, as the at least one feature, a peak point or a valley point from a largest area of the plurality of separate areas between the first graph and the second graph. 
     The processor may divide an effective range of the bio-signal into a plurality of sections based on a peak point or a valley point within the effective range, and may calculate the plurality of separate areas by summing differences between the envelope signal and the bio-signal in each of the plurality of sections. 
     The processor may correct the calculated plurality of separate areas using a scaling function. 
     The scaling function may be generated based on probability that a feature exists in the bio-signal. 
     The processor may perform signal smoothing on the bio-signal. 
     The processor may divide an effective range of the bio-signal into a plurality of sections, may calculate an area of each of the plurality of sections by summing absolute values of differences between the envelope signal and the bio-signal in each of the plurality of sections, may detect at least one of peak points and valley points of largest N sections as candidate features, among the plurality of sections, and may detect the at least one feature from the candidate features, based on priori information, wherein N is a natural number. 
     The priori information may include information about a position at which the at least one feature is detected. 
     The processor may detect one of the candidate features that is closest to the priori information as the at least one feature. 
     The processor may update the priori information based on the detected at least one feature. 
     According to an aspect of another exemplary embodiment, there is provided a method of detecting a bio-signal feature, including: acquiring a bio-signal; generating an envelope signal of the bio-signal; and detecting at least one feature of the bio-signal based a difference between the envelope signal and the bio-signal. 
     The bio-signal may be a pulse wave signal, a first-order differential signal of the pulse wave signal, or a second-order differential signal of the pulse wave signal. 
     The pulse wave signal may include a photoplethysmogram (PPG) signal and a pressure pulse wave signal. 
     The at least one feature may represent a reflection wave component constituting the bio-signal. 
     The generating the envelope signal may include: detecting at least one peak point or at least one valley point in one period of the bio-signal; and generating the envelope signal by linearly connecting a start point, the at least one peak point or valley point, and an end point of the bio-signal in the period. 
     The generating the envelope signal may further include determining an effective range of the bio-signal by setting a minimum point in the period of the bio-signal as the start point and setting a last zero crossing point or a last valley point in the period of the bio-signal as the end point. 
     The detecting the at least one feature may include: calculating a plurality of separate areas between a first graph representing the envelop signal and a second graph representing the bio-signal; and detecting, as the at least one feature, a peak point or a valley point from a largest area of the plurality of separate areas between the first graph and the second graph. 
     The detecting the at least one feature may further include: dividing an effective range of the bio-signal into a plurality of sections based on a peak point or a valley point within the effective range. 
     The calculating the plurality of separate areas may include correcting the calculated plurality of separate areas using a scaling function. 
     The scaling function is generated based on probability that a feature exists in the bio-signal. 
     The method may further include performing signal smoothing on the bio-signal, so that the envelope signal is generated based on the bio-signal, on which the signal smoothing is performed. 
     The detecting the at least one feature may include: dividing an effective range of the bio-signal into a plurality of sections; calculating an area of each of the plurality of sections by summing absolute values of differences between the envelope signal and the bio-signal in each of the plurality of sections; detecting at least one of peak points and valley points of largest N sections, among the plurality of sections, as the candidate features; and detecting the at least one feature from the candidate features based on priori information, wherein N is a natural number. 
     The priori information may include information about a position at which the at least one feature is detected. 
     The detecting the at least one critical feature may include detecting one of the candidate features that is closest to the priori information as the at least one feature. 
     The method may further include updating the priori information based on the detected at least one feature. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and/or other aspects will be more apparent by describing certain exemplary embodiments, with reference to the accompanying drawings, in which: 
         FIG.  1    is a diagram illustrating a bio-signal according to one exemplary embodiment; 
         FIG.  2    is a block diagram illustrating an apparatus for detecting a bio-signal feature according to one exemplary embodiment; 
         FIG.  3    is a block diagram illustrating a processor according to one exemplary embodiment; 
         FIG.  4    is a diagram for describing an example of detecting a feature using an upper envelope signal; 
         FIG.  5    is a diagram for describing an example of detecting a feature using a lower envelope signal; 
         FIGS.  6 A and  6 B  are diagrams for describing an example of detecting a feature using both an upper envelope signal and a lower envelope signal; 
         FIG.  7    is a diagram for describing a method of generating a scaling function; 
         FIG.  8    is a block diagram illustrating a processor according to another exemplary embodiment; 
         FIG.  9    is a block diagram illustrating a processor according to still another exemplary embodiment; 
         FIG.  10    is a block diagram illustrating an apparatus for detecting a bio-signal feature according to another exemplary embodiment; 
         FIG.  11    is a flowchart illustrating a method of detecting a bio-signal feature according to one exemplary embodiment; 
         FIG.  12    is a flowchart illustrating an operation of generating an envelope signal of a bio-signal according to one exemplary embodiment; 
         FIG.  13    is a flowchart illustrating an operation of detecting a feature according to one exemplary embodiment; and 
         FIG.  14    is a flowchart illustrating the operation of detecting a feature according to another exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary embodiments are described in greater detail below with reference to the accompanying drawings. 
     Throughout the drawings and the detailed description, unless otherwise described, the same drawing reference numerals will be understood to refer to the same elements, features, and structures. The relative size and depiction of these elements may be exaggerated for clarity, illustration, and convenience. The matters defined in the description, such as detailed construction and elements, are provided to assist in a comprehensive understanding of the exemplary embodiments. However, it is apparent that the exemplary embodiments can be practiced without those specifically defined matters. Also, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail. 
     It should be noted that in some alternative implementations, the functions/acts noted in the blocks may occur out of the order noted in the flowcharts. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     Terms described in below are selected by considering functions in the embodiment and meanings may vary depending on, for example, a user or operator&#39;s intentions or customs. Therefore, in the following embodiments, when terms are specifically defined, the meanings of terms should be interpreted based on definitions, and otherwise, should be interpreted based on general meanings recognized by those skilled in the art. 
     As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” or “includes” and/or “including” when used in this description, specify the presence of stated features, numbers, steps, operations, elements, components or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components or combinations thereof. 
     Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. For example, the expression, “at least one of a, b, and c,” should be understood as including only a, only b, only c, both a and b, both a and c, both b and c, or all of a, b, and c. 
     It will also be understood that the elements or components in the following description are discriminated in accordance with their respective main functions. In other words, two or more elements may be made into one element or one element may be divided into two or more elements in accordance with a subdivided function. Additionally, each of the elements in the following description may perform a part or whole of the function of another element as well as its main function, and some of the main functions of each of the elements may be performed exclusively by other elements. Each element may be realized in the form of a hardware component, a software component, and/or a combination thereof. 
     Meanwhile, an apparatus for detecting a bio-signal feature described herein may be implemented as a software module or in the form of a hardware chip and be mounted in an electronic device. In this case, the electronic device may include a mobile phone, a smart phone, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, an MP3 player, a digital camera, a wearable device, etc., and the wearable device may include various types of wearable devices, such as a wristwatch type, a wristband type, a ring type, a belt-type, a necklace type, an ankle band type, a thigh band type, a forearm band type, and the like. However, the electronic device is not limited to the above mentioned examples, and the wearable device is also not limited to the above-described examples. 
       FIG.  1    is a diagram illustrating a bio-signal according to one exemplary embodiment. Specifically,  FIG.  1    illustrates one embodiment of a photoplethysmogram (PPG) signal. 
     Referring to  FIG.  1   , a waveform of a PPG signal  100  may be a summation of a propagation wave  110  propagating from the heart to peripheral parts of a body and reflection waves  120  and  130  returning from the peripheral parts of the body. In the illustrated example, the PPG signal  100  is a summation of the propagation wave  110  and the reflection waves  120  and  130 . 
       FIG.  2    is a block diagram illustrating an apparatus for detecting a bio-signal feature according to one exemplary embodiment. 
     Referring to  FIG.  2   , the apparatus  200  for detecting a feature of a bio-signal includes a bio-signal acquirer  210  and a processor  220 . 
     The bio-signal acquirer  210  may acquire a bio-signal of one period. In this case, the bio-signal may be a pulse wave signal (e.g., a PPG signal or a pressure pulse wave signal), a first-order differential signal of a pulse wave signal, or a second-order differential signal of a pulse wave signal. 
     According to one exemplary embodiment, the bio-signal acquirer  210  may acquire a bio-signal from an external device which senses and/or stores the bio-signal. In this case, the bio-signal acquirer  210  may correspond to a communication interface which uses various communication technologies, such as Bluetooth, Bluetooth low energy (BLE), near field communication (NFC), wireless local area network (WLAN), ZigBee, infrared data association (IrDA), Wi-Fi direct, ultra-wideband, Ant+, Wi-Fi, radio frequency identification (RFID), 3G communication, 4G communication, and 5G communication. 
     According to another exemplary embodiment, the bio-signal acquirer  210  may include various sensors, such as an electrocardiography (ECG) sensor that measures an electrical activity of the heart by using electrodes placed on the skin of a subject, or a PPG sensor that optically senses the rate of blood flow as controlled by the heart&#39;s pumping action, to acquire a bio-signal. 
     The processor  220  may generate an envelope signal from the acquired bio-signal and detect a feature of the bio-signal using difference between the generated envelope signal and the bio-signal. In this case, the envelope signal may be divided into an upper envelope signal generated based on a peak point of the bio-signal and a lower envelope signal generated based on a valley point of the bio-signal. 
     Meanwhile, the feature may be defined as a point that represents a reflection wave component (e.g., the reflection wave components  120  and  130  of  FIG.  1   ) constituting the bio-signal. 
     Hereinafter, an exemplary embodiment using the upper envelope signal, an exemplary embodiment using the lower envelope signal, and an exemplary embodiment using both the upper and lower envelope signals will be separately described. 
     &lt;Exemplary Embodiment Using an Upper Envelope Signal&gt; 
     The processor  220  may determine an effective range of the bio-signal. In this case, the effective range has a minimum point of the bio-signal as a start point and the last zero crossing point or the last valley point of the bio-signal as an end point. That is, the processor  220  may detect the minimum point and the last zero crossing point or the last valley point of the bio-signal, and determine that a range from the minimum point to the last zero crossing point or the last valley point as the effective range for detecting a feature. 
     The processor  220  may detect at least one peak point from the effective range of the bio-signal and generate the upper envelope signal by linearly connecting the start point of the effective range, the detected at least one peak point and the end point of the effective range. 
     The processor  220  may calculate a difference between the upper envelope signal and the bio-signal. For example, the processor  220  may calculate the difference between the upper envelope signal and the bio-signal by subtracting the bio-signal from the upper envelope signal. In this case, the processor  220  may correct the difference between the upper envelope signal and the bio-signal using a scaling function. The scaling function may be generated based on probability information on which a feature may appear, and then be stored in an internal/external memory of the apparatus  200  for detecting a feature of a bio-signal. 
     The processor  220  may divide the effective range of the bio-signal into a plurality of sections based on the peak points within the effective range, and calculate an area of each of the sections by summing absolute values of the differences between the upper envelope signal and the bio-signal in each section. For example, when two peak points, a first peak point and a second peak point, are present within the effective range, the processor  220  may divide the effective range into three sections, a first section starting from the start point of the effective range to the first peak point, a second section from the first peak point to the second peak point, and a third section from the second peak point to the end point of the effective range, and calculate the area of each of the sections by summing absolute values of the differences between the envelope signal and the bio-signal in each section. 
     The processor  220  may extract a section (hereinafter referred to as a “maximum area section”) having the largest area from the plurality of segmented sections based on the calculated area of each of the sections and detect a start point (peak point) and/or a valley point of the extracted maximum area section as a feature of the bio-signal. 
     &lt;Exemplary Embodiment Using a Lower Envelope Signal&gt; 
     The processor  220  may determine an effective range of the bio-signal. In this case, the effective range may have a minimum point of the bio-signal as a start point and the last zero crossing point or the last valley point of the bio-signal as an end point. That is, the processor  220  may detect a minimum point and the last zero-crossing point or the last valley point of the bio-signal, and determine a range from the minimum point to the last zero crossing point or the last valley point as the effective range for detecting a feature of the bio-signal. 
     The processor  220  may detect at least one valley point within the effective range of the bio-signal and generate a lower envelop signal by linearly connecting the start point of the effective range, the detected at least one valley point, and the end point of the effective range. 
     The processor  220  may calculate a difference between the bio-signal and the lower envelope signal. For example, the processor  220  may calculate the difference between the bio-signal and the lower envelope signal by subtracting the lower envelope signal from the bio-signal. In this case, the processor  220  may correct the difference between the bio-signal and the lower envelope signal using a scaling function. 
     The processor may divide the effective range into a plurality of sections based on the valley points within the effective range, and calculate the area of each of the sections by summing absolute values of the differences between the envelope signal and the bio-signal in each section. For example, when there are two valley points, a first valley point and a second valley point, in the effective range, the processor  220  may divide the effective range into three sections, a first section from the start point of the effective range to the first valley point, a second section from the first valley point to the second valley point, and a third section from the second valley point to the end point of the effective range, and calculate the area of each of the sections by summing absolute values of the differences between the envelope signal and the bio-signal in each section. 
     The processor  220  may extract a maximum area section from among the plurality of sections based on the calculated area of each of the sections, and detect a peak point and/or an end point (valley point) of the extracted maximum area as a feature of the bio-signal. 
     &lt;Exemplary Embodiment Using an Upper Envelope Signal and a Lower Envelope Signal&gt; 
     The processor  220  may determine an effective range of a bio-signal. In this case, the effective range may have a minimum point of the bio-signal as a start point and the last zero crossing point or the last valley point of the bio-signal as an end point. That is, the processor  220  may detect the minimum point and the last zero-crossing point or the last valley point of the bio-signal, and determine a range from the minimum point to the last zero crossing point or the last valley point as the effective range for detecting a feature of the bio-signal. 
     The processor  220  may detect at least one peak point from the effective range of the bio-signal, and generate an upper envelope signal by linearly connecting the start point of the effective range to the detected at least one peak point, and then to the end point of the effective range. The processor  220  may generate a lower envelope signal by linearly connecting the start point of the effective range to the detected at least one peak point, and then to the end point of the effective range. 
     The processor  220  may calculate a difference between the upper envelope signal and the bio-signal and a difference between the bio-signal and the lower envelope signal. For example, the processor  220  may calculate the difference between the upper envelope signal and the bio-signal by subtracting the bio-signal from the upper envelope signal and calculate the difference between the bio-signal and the lower envelope signal by subtracting the lower envelope signal from the bio-signal. In this case, the processor  220  may correct the difference between the upper envelope signal and the bio-signal and the difference between the lower envelope signal and the bio-signal using a scaling function. 
     The processor  220  may divide the effective range of the bio-signal into a plurality of sections (hereinafter referred to as “peak-based sections”) based on the peak points within the effective range, and calculate an area of each of the sections (hereinafter referred to as “an area of each peak-based section”) by summing absolute values of the differences between the upper envelope signal and the bio-signal in each section. In addition, the processor  220  may divide the effective range of the bio-signal into a plurality of sections (hereinafter referred to as “valley-based sections”) based on the valley points within the effective range and calculate an area of each of the sections (hereinafter referred to as “an area of each valley-based section”) by summing absolute values of the differences between the lower envelope signal and the bio-signal in each section. 
     The processor  220  may calculate an area of each integrated section by integrating the area of each peak-based section and the area of each valley-based section. 
     According to one exemplary embodiment, the processor  220  may calculate an area of each integrated section by applying a first weight (e.g., 0.6) to the area of each peak-based section, applying a second weight (e.g., 0.4) to the area of each valley-based section, and summing the areas of the mutually corresponding sections. In this case, the first weight and the second weight may be experimentally determined and the (n+1)th peak-based section and the n th  valley-based section may mutually correspond to each other. For example, it is assumed that, in a case in which three peak-based sections (e.g., a first peak-based section to a third peak-based section) and two valley-based sections (e.g., a first valley-based section and a second valley-based section) are present, an area of the first peak-based section is 10, an area of the second peak-based section is 520, an area of the third peak-based section is 100, an area of the first valley-based section is 300, an area of the second valley-based section is 200, the first weight is 0.6, and the second weight is 0.4. In this case, the processor  220  may apply the first weight of 0.6 to the area of 10 of the first peak-based section and add 0 to the resulting value, given that there is no valley-based section corresponding to the first peak-based section, to calculate an integrated area of the first peak-based section, which has a result value of 6 as follows: 
       10×0.6+0=6.
 
     In addition, the processor  220  may apply the first weight of 0.6 to the area of 520 of the second peak-based section, apply the second weight of 0.4 to the area of 300 of the first valley-based section, and sum the weighted areas to calculate an integrated area of the second peak-based section (or the first valley-based section), which has a result value of 432 as follows: 
       (520×0.6)+(300×0.4)=432
 
     Also, the processor  220  may apply the first weight of 0.6 to the area of 100 of the third peak-based section, apply the second weight of 0.4 to the area of 200 of the second valley-based section and sum the weighted areas to calculate an integrated area of the third peak-based section (or the second valley-based section), which has a result value of 140 as follows: 
       (100×0.6)+(200×0.4)=140
 
     The processor  220  may extract a section having the largest integrated area (hereinafter referred to as a “maximum integrated area section”) from among the plurality of sections, and detect a peak point and/or a valley point of the extracted maximum integrated area section as a feature of the bio-signal. For example, in the above example, the processor  220  may extract the second peak-based section (or the first valley-based section) whose integrated area is the largest and detect a start point (peak point) and/or a valley point of the second peak-based section (or a peak point and/or a valley point of the first valley-based section) as the feature of the bio-signal. 
     Meanwhile, the detected features may be used to estimate a blood pressure of the subject from which the bio-signal is measured. For example, various characteristic values (e.g., time, amplitude, etc.) of the bio-signal may be calculated using the features detected by the apparatus  200  for detecting a bio-signal feature and it is possible to estimate the blood pressure of the subject using the various calculated characteristic values and a pre-stored blood pressure estimation equation. 
       FIG.  3    is a block diagram illustrating a processor according to one exemplary embodiment. A processor  300  of  FIG.  3    may be one exemplary embodiment of the processor  220  of  FIG.  2   . 
     Referring to  FIG.  3   , the processor  300  includes an effective range determiner  310 , an envelope signal generator  320 , an area calculator  330 , and a feature detector  340 . 
     The effective range determiner  310  may determine an effective range of a bio-signal. For example, the effective range has a minimum point of the bio-signal as a start point and the last zero crossing point or the last valley point of the bio-signal as an end point. The effective range determiner  310  may detect the minimum point and the last zero crossing point or the last valley point of the bio-signal, and determine that a range from the minimum point to the last zero crossing point or the last valley point as the effective range for detecting a feature. 
     The envelope signal generator  320  may generate an envelope signal of the bio-signal. 
     For example, the envelope signal generator  320  may detect at least one peak point from the effective range of the bio-signal and generate an upper envelope signal by linearly connecting the start point of the effective range to the detected at least one peak point, and then to the end point of the effective range. The envelope signal generator  320  may generate a lower envelope signal by connecting the start point of the effective range to the detected at least one valley point, and then to the end point of the effective range. 
     The area calculator  330  may calculate a difference between the bio-signal and at least one of the upper envelope signal and the lower envelope signal. 
     According to one exemplary embodiment, in the case in which the envelope signal generator  320  generates the upper envelope signal, the area calculator  330  may calculate a difference between the upper envelope signal and the bio-signal by subtracting the bio-signal from the upper envelope signal. 
     According to another exemplary embodiment, in a case in which the envelope signal generator  320  generates the lower envelope signal, the area calculator  330  may calculate a difference between the lower envelope signal and the bio-signal by subtracting the lower envelope signal from the bio-signal. 
     Meanwhile, the area calculator  330  may correct the difference between the upper envelope signal and the bio-signal and the difference between the bio-signal and the lower envelope signal using a scaling function. 
     The area calculator  330  may divide the effective range based on the peak points or the valley point within the effective range and calculate an area of each section by summing absolute values of the differences between the envelope signal and the bio-signal in each section. 
     According to one exemplary embodiment, in a case in which the envelope signal generator  320  generates the upper envelope signal, the area calculator  330  may divide the effective range of the bio-signal into a plurality of sections based on the peak points within the effective range, and calculate an area of each of the sections by summing absolute values of the differences between the upper envelope signal and the bio-signal in each section. 
     According to another exemplary embodiment, in a case in which the envelope signal generator  320  generates the lower envelope signal, the area calculator  330  may divide the effective range into a plurality of sections on the valley point within the effective range, and calculate the area of each of the sections by summing the differences between the envelope signal and the bio-signal in each section. 
     According to still another exemplary embodiment, in a case in which the envelope signal generator  320  generates both the upper envelope signal and the lower envelope signal, the area calculator  330  may divide the effective range into a plurality of peak-based sections and into a plurality of valley-based sections, respectively, calculate an area of each peak-based section by summing differences between the upper envelope signal and the bio-signal in each peak-based section, and calculate an area of each valley-based section by summing differences between the bio-signal and the lower envelope signal in each valley-based section. In addition, the area calculator  330  may calculate an area of each integrated section by applying a first weight to the area of each peak-based section, applying a second weight to the area of each valley-based section, and thereafter, summing areas of the mutually corresponding sections. 
     The feature detector  340  may select a maximum area section based on the calculated areas for each section and detect a peak point and/or a valley point of the selected maximum area section as a feature of the bio-signal. 
     According to one exemplary embodiment, the feature detector  340  may select a maximum area section from the plurality of peak-based sections and detect a start point (peak point) of the selected maximum area section and/or a valley point thereof as a feature of the bio-signal. 
     According to another exemplary embodiment, the feature detector  340  may select a maximum area section from the plurality of valley-based sections and detect a peak point of the selected maximum area section and/or an end point (valley point) thereof as a feature of the bio-signal. 
     According to still another exemplary embodiment, the feature detector  340  may extract a maximum integrated area section from the plurality of sections (the plurality of peak-based sections and the valley-based sections) and detect a peak point of the extracted maximum integrated area section and/or a valley point thereof as a feature of the bio-signal. 
       FIG.  4    is a diagram for describing an example of detecting a feature using an upper envelope signal. In  FIG.  4   , a bio-signal  410  represents a second-order differential signal of a pulse wave signal. 
     Referring to  FIGS.  3  and  4   , the effective range determiner  310  detects a minimum point a and the last zero crossing point f from the bio-signal  410  and determines a range from the minimum point a to the last zero crossing point f as an effective range. 
     The envelope signal generator  320  detects peak points b and d in the effective range and generate an upper envelope signal  420  by connecting the start point a of the effective range, the peak points b and d, and the end point f of the effective range. 
     The area calculator  330  calculates a difference between the upper envelope signal  420  and the bio-signal  410  by subtracting the bio-signal  410  from the upper envelope signal  420 . In this case, the area calculator  330  may correct the difference between the upper envelope signal  420  and the bio-signal  410  using a scaling function. 
     The area calculator  330  divides the effective range into three sections (a first section, a second section, and a third section) based on the peak points b and d in the effective range and calculates an area of each section by summing absolute values of the differences between the upper envelope signal  420  and the bio-signal  410  in each section. 
     The feature detector  340  selects a third section that is a maximum area section from among the three sections (i.e., the first section to the third section) based on the calculated areas for each section, and detects a start point d of the third section and/or a valley point e as a feature of the bio-signal  410 . 
       FIG.  5    is a diagram for describing an example of detecting a feature using a lower envelope signal. In  FIG.  5   , a bio-signal  510  represents a second-order differential signal of a pulse wave signal. 
     Referring to  FIGS.  3  and  5   , the effective range determiner  310  detects a minimum point a and the last zero crossing point f from the bio-signal  510  and determine a range from the minimum point a to the last zero crossing point f as an effective range. 
     The envelope signal generator  320  detects valley points c and e in the effective range and generates a lower envelope signal  520  by connecting the start point a of the effective range, the valley points c and e in the effective range, and the end point f of the effective range. 
     The area calculator  330  calculates a difference between the bio-signal  510  and the lower envelope signal  520  by subtracting the lower envelope signal  520  from the bio-signal  510 . In this case, the area calculator  330  may correct the difference between the bio-signal  510  and the lower envelope signal  520  using a scaling function. 
     The area calculator  330  divides the effective range into three sections (a first section, a second section, and a third section) based on the valley points c and e in the effective range and calculates an area of each section by summing absolute values of the differences between the bio-signal  510  and the lower envelope signal  520  in each section. 
     The feature detector  340  selects the second section that is a maximum area section from among the three sections (i.e., the first section to the third section) based on the calculated areas for each section, and detects a peak point d of the second section and/or an end point e thereof as a feature of the bio-signal  510 . 
       FIGS.  6 A and  6 B  are diagrams for describing an example of detecting a feature using both an upper envelope signal and a lower envelope signal. In  FIGS.  6 A and  6 B , a bio-signal  610  represents a second-order differential signal of a pulse wave signal. 
     Referring to  FIGS.  3 ,  6 A and  6 B , the effective range determiner  310  detects a minimum point a and the last zero crossing point f of the bio-signal  610  and determines a range from the minimum point a to the last zero crossing point f as an effective range. 
     The envelope signal generator  320  detects peak points b and d in the effective range, and generates an upper envelope signal  620  by connecting the start point a of the effective range, the peak points b and d, and the end point f of the effective range, with reference to  FIG.  6 A . The envelope signal generator  320  detects valley points c and e in the effective range, and generates a lower envelope signal  520  by connecting the start point a of the effective range, the valley points c and e in the effective range, and an end point f of the effective range, with reference to  FIG.  6 B . 
     The area calculator  330  calculates a difference between the upper envelope signal  620  and the bio-signal  610  by subtracting the bio-signal  610  from the upper envelope signal  620 , with reference to  FIG.  6 A . In addition, the area calculator  330  calculates a difference between the bio-signal  610  and the lower envelope signal  630  by subtracting the lower envelope signal  630  from the bio-signal  610 , with reference to  FIG.  6 B . The area calculator  330  may correct the difference between the upper envelope signal  620  and the bio-signal  610  and the difference between the bio-signal  610  and the lower envelope signal  630  using a scaling function. 
     The area calculator  330  divides the effective range into three sections (e.g., a first peak-based section, a second peak-based section, and a third peak-based section) based on the peak points b and d in the effective range and calculates an area of each section by summing absolute values of the differences between the upper envelope signal  620  and the bio-signal  610  in each peak-based section, with reference to  FIG.  6 A ). In addition, the area calculator  330  divides the effective range into three sections (e.g., a first valley-based section, a second valley-based section, and a third valley-based section) based on the valley points c and e in the effective range and calculates an area of each section by summing the differences between the bio-signal  610  and the lower envelope signal  630  in each valley-based section, with reference to  FIG.  6 B . 
     The area calculator  330  calculates an area of each integrated section by applying a first weight to the area of each peak-based section, applying a second weight to the area of each valley-based section, and thereafter, summing areas of the mutually corresponding sections. In this case, the second peak-based section corresponds to the first valley-based section, and the third peak-based section corresponds to the second valley-based section. On the other hand, there is no valley-based section that corresponds to the first peak-based section and there is no peak-based section that corresponds to the third valley-based section. 
     The feature detector  340  selects the third peak-based section that is a maximum integrated area section from among the three peak-based sections (i.e., the first peak-based section, the second peak-based section, and the third peak-based section) based on the calculated areas of each of integrated sections, and detects a start point d of the third peak-based section and/or a valley point e thereof as a feature of the bio-signal  610 . Alternatively, or in addition to the third peak-based section, the feature detector  340  may select the second valley-based section that is a maximum integrated area section from among the three valley-based sections (i.e., the first valley-based section, the second valley-based section, and the third valley-based section) based on the calculated areas of each of integrated sections, and detect a peak point d of the second valley-based section and/or an end point e thereof as a feature of the bio-signal  610 . 
       FIG.  7    is a diagram for describing a method of generating a scaling function. 
     A feature according to one exemplary embodiment may mostly appear near an inflection point of a lower envelope signal. Therefore, a scaling function may be generated based on such a characteristic. 
     With reference to  FIG.  2   , the processor  220  or an external device that communicates with the processor may generate a scaling function. Hereinafter, it is assumed that the processor  220  generates the scaling function and a bio-signal  710  is a second-order differential signal of a pulse wave signal. 
     Referring to  FIG.  7   , the processor  220  may form a best-fit line that passes through a start point and two valley points of the bio-signal to generate a lower envelope signal  720  in a curved-shape. The processor  220  may generate a scaled lower envelope signal  730  by scaling the lower envelope signal  720  to have a range of 0 to 1. 
     The processor  220  may generate a signal  740  that is horizontally symmetric to the lower envelope signal  730  scaled based on a time central axis  770 . 
     The processor  220  may generate a signal based on the scaled lower envelope signal  730  and the horizontally symmetric signal  740  and generate a scaling function  760  by scaling the generated signal  750  to have a range of 0 to 1. 
       FIG.  8    is a block diagram illustrating a processor according to another exemplary embodiment. A processor  800  of  FIG.  8    may be one exemplary embodiment of the processor  220  of  FIG.  2   . 
     Referring to  FIG.  8   , the processor  800  includes an effective range determiner  310 , an envelope signal generator  320 , an area calculator  330 , a feature detector  340 , and a smoother  810 . Here, the effective range determiner  310 , the envelope signal generator  320 , the area calculator  330 , and the feature detector  340  have been described with reference to  FIG.  3   , and hence detailed descriptions thereof will be omitted. 
     The smoother  810  may smooth a bio-signal. For example, the smoother  810  may perform single smoothing by removing noise from the bio-signal using a low-pass filter (e.g., a moving average filter). 
       FIG.  9    is a block diagram illustrating a processor according to still another exemplary embodiment. A processor  900  of  FIG.  9    may be one exemplary embodiment of the processor  220  of  FIG.  2   . 
     Referring to  FIG.  9   , the processor  900  includes an effective range determiner  310 , an envelope signal generator  320 , an area calculator  330 , a feature candidate detector  910 , a feature detector  920 , and a priori information updater  930 . Here, the effective range determiner  310 , the envelope signal generator  320 , and the area calculator  330  have been described with reference to  FIG.  3   , and hence detailed descriptions thereof will be omitted. 
     The feature candidate detector  910  may select top N sections (N is an arbitrary natural number) having large areas based on areas of each section calculated by the area calculator  330 , and detect peak points and/or valley points in the selected N sections as feature candidates. For example, an effective range may be divided into five sections (e.g., a first section to a fifth section), and relative sizes of areas of each of the sections are expressed as follows: the third section &gt;the fourth section &gt;the second section &gt;the first section &gt;the fifth section, wherein N is 2. In this case, the feature candidate detector  910  may detect a peak point and/or a valley point of the third section and a peak point and/or a valley point of the fourth section as feature candidates. 
     The feature detector  920  may detect one of the feature candidates as a feature based on pre-stored priori information. In this case, the priori information may be information about a position from which the feature is detected. The priori information may be derived by learning feature detection results. According to one exemplary embodiment, the feature detector  920  may calculate distances between the priori information and each of the feature candidates, and detect the feature candidate that is closest to the priori information as a feature of the bio-signal. 
     The priori information updater  930  may update the pre-stored priori information based on position information of the detected feature. 
       FIG.  10    is a block diagram illustrating an apparatus for detecting a bio-signal feature according to another exemplary embodiment. 
     Referring to  FIG.  10   , an apparatus  1000  for detecting a bio-signal feature includes a bio-signal acquirer  210 , a processor  220 , an input interface  1010 , a storage  1020 , a communication interface  1030 , and an output interface  1040 . In this case, the bio-signal acquirer  210  and the processor  220  have been described with reference to  FIG.  2   , and thus detailed descriptions thereof will be omitted. 
     The input interface  1010  may receive various operation signals input by a user. According to one exemplary embodiment, the input interface  1010  may include a key pad, a dome switch, a capacitive or resistive touch pad, a jog wheel, a jog switch, a hardware button, and the like. In particular, when the touch pad has a layered structure with a display, this structure may be referred to as a touch screen. 
     The storage  1020  may store programs or instructions for operations of the apparatus  1000  for detecting a bio-signal feature and store data input to the apparatus  1000  and data output from the apparatus  1000 . In addition, the storage  1020  may store bio-signal data acquired through the bio-signal acquirer  210  and feature data detected by the processor  220 . 
     The storage  1020  may include at least one type of storage medium, such as a flash memory, a hard disk, a micro type multimedia card, and a card type memory (e.g., secure digital (SD) or XD memory), a random access memory (RAM), a static random access memory (SRAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a programmable read only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. In addition, the apparatus  1000  may operate an external storage medium, such as a web storage serving a storage function. 
     The communication interface  1030  may communicate with an external device. For example, the communication interface  1030  may transmit the bio-signal data acquired through the bio-signal acquirer  210  and the feature data detected by the processor  220  to the external device and receive various pieces of data helpful for detecting a feature of the bio-signal from the external device. 
     In this case, the external device may be a medical device using the acquired bio-signal data and/or the bio-signal feature data, a printer to output results, or a display device to display the bio-signal data and the bio-signal feature data. In addition, the external device may be a digital TV, a desktop computer, a mobile phone, a smart phone, a tablet computer, a notebook computer, a PDA, a PMP, a navigation system, an MP3 player, a digital camera, a wearable device, or the like, but is not limited thereto. 
     The communication interface  1030  may communicate with the external device via Bluetooth communication, Bluetooth low energy (BLE) communication, near-field communication (NFC), wireless local area network (WLAN) communication, ZigBee communication, infrared data association (IrDA) communication, Wi-Fi direct (WFD) communication, ultra-wideband (UWB) communication, Ant+ communication, Wi-Fi communication, radio frequency identification (RFID) communication, 3G communication, 4G communication, 5G communication, or the like. However, these are merely examples and the type of communication is not limited thereto. 
     The output interface  1040  may output the bio-signal data and/or the bio-signal feature data. According to one exemplary embodiment, the output interface  1040  may output the bio-signal data and/or the bio-signal feature data using at least one of an audible method, a visual method, and a tactile method. To this end, the output interface  1040  may include a display, a speaker, a vibrator, and the like. 
       FIG.  11    is a flowchart illustrating a method of detecting a bio-signal feature according to one exemplary embodiment. The method shown in  FIG.  11    may be performed by the apparatus  200  for detecting a bio-signal feature of  FIG.  2   . 
     Referring to  FIGS.  2  and  11   , the apparatus  200  for detecting a bio-signal feature acquires a bio-signal of one period, in operation  1110 . In this case, the bio-signal may be a pulse wave signal (e.g., a PPG signal or a pressure pulse wave signal) a first-order differential signal of a pulse wave signal, or a second-order differential signal of a pulse wave signal. For example, the apparatus  200  may acquire the bio-signal from an external device configured to sense and/or store the bio-signal or may directly acquire the bio-signal using various sensors, such as a PPG sensor, configured to sense the bio-signal. 
     The apparatus  200  may generate an envelope signal of the bio-signal from the acquired bio-signal, in operation  1120 . In this case, the envelope signal may be classified into an upper envelope signal generated based on a peak point of the bio-signal and a lower envelope signal generated based on a valley point of the bio-signal. 
     The apparatus  200  may detect a feature of the bio-signal using a difference between the generated envelope signal and the bio-signal, in operation  1130 . In this case, the feature may be defined as a point that represents a reflection wave component (e.g., the reflection wave components  120  and  130  of  FIG.  1   ) constituting the bio-signal. 
       FIG.  12    is a flowchart illustrating an operation  1120  of generating an envelope signal of a bio-signal according to one exemplary embodiment. 
     Referring to  FIGS.  2  and  12   , the apparatus  200  for detecting a bio-signal feature may determine an effective range of the bio-signal, in operation  1210 . For example, the apparatus  200  may detect a minimum point and the last zero crossing point or the last valley point of the bio-signal, and determine that a range from the minimum point to the last zero crossing point or the last valley point as the effective range of the bio-signal. 
     The apparatus  200  may detect at least one peak point or at least one valley point within the effective range of the bio-signal, in operation  1220 . 
     The apparatus  200  may generate an envelope signal of the bio-signal based on the detected at least one peak point and/or the detected at least one valley point, in operation  1230 . For example, the apparatus  200  may generate an upper envelope signal by connecting a start point of the effective range, the detected at least one peak point, and an end point of the effective range, or may generate a lower envelope signal by connecting the start point of the effective range, the detected at least one valley point, and the end point of the effective range. 
       FIG.  13    is a flowchart illustrating an operation  1130  of detecting a feature according to one exemplary embodiment. 
     Referring to  FIGS.  2  and  13   , the apparatus  200  for detecting a bio-signal feature may divide an effective range into a plurality of sections based on the peak point or the valley point within the effective range, in operation  13010 . For example, the apparatus  200  may divide the effective range into a plurality of sections based on the peak point within the effective range (when using an upper envelope signal), divide the effective range into a plurality of sections based on the valley point within the effective range (when using a lower envelope signal), or may divide the effective range into a plurality of sections based on the peak point within the effective range and also divide the effective range into a plurality of sections based on the valley point within the effective range (when using both the upper envelope signal and the lower envelope signal). 
     The apparatus  200  may calculate a difference between the bio-signal and the envelope signal, and calculate an area of each of the sections by summing the differences between the bio-signal and the envelope signal in each section, in operation  1320 . For example, the apparatus  200  may calculate a difference between the upper envelope signal and the bio-signal by subtracting the bio-signal from the upper envelope signal and calculate an area of each of the sections by adding absolute values of the differences between the upper envelope signal and the bio-signal in each section (when using the upper envelope signal). In addition, the apparatus  200  may calculate a difference between the bio-signal and the lower envelope signal by subtracting the lower envelope signal from the bio-signal and calculate an area of each of the sections by adding absolute values of the differences between the bio-signal and the lower envelope signal in each section (when using the lower envelope signal). Also, the apparatus  200  may calculate an area of each peak-based section by summing absolute values of differences between the upper envelope signal and the bio-signal in each peak-based section, calculate an area of each valley-based section by summing absolute values of differences between the bio-signal and the lower envelope signal in each valley-based section, and calculate an area of each integrated section by applying a first weight to the area of each peak-based section, applying a second weight to the area of each valley-based section, and thereafter, summing areas of the mutually corresponding sections (when using both the upper envelope signal and the lower envelope signal). 
     In this case, the apparatus  200  may correct the difference between the upper envelope signal and the bio-signal and the difference between the bio-signal and the lower envelope signal using a scaling function. 
     The apparatus  200  may select a maximum area section based on the calculated areas for each section and detect a peak point and/or a valley point of the selected maximum area section as a feature of the bio-signal, in operation  1330 . For example, the apparatus  200  may select a maximum area section from the plurality of peak-based sections and detect a start point (peak point) of the selected maximum area section and/or a valley point thereof as a feature of the bio-signal (when using the upper envelope signal). In addition, the apparatus  200  may select a maximum area section from the plurality of valley-based sections and detect a peak point of the selected maximum area section and/or an end point (valley point) thereof as a feature of the bio-signal (when using the lower envelope signal). Also, the apparatus  200  may select a maximum integrated area section from a plurality of peak-based sections or from a plurality of valley-based sections and detect a peak point and/or a valley point of the extracted maximum integrated area section as a feature of the bio-signal. 
       FIG.  14    is a flowchart illustrating an operation  1130  of detecting a feature according to another exemplary embodiment. 
     Referring to  FIGS.  2  and  14   , the apparatus  200  for detecting a bio-signal feature may divide an effective range into a plurality of sections based on a peak point or a valley point within the effective range, in operation  1410 . 
     The apparatus  200  may calculate a difference between the bio-signal and an envelope signal and calculate an area of each of the sections by summing absolute values of the differences between the bio-signal and the envelope signal in each section, in operation  1420 . 
     In this case, the apparatus  200  may correct the difference between the upper envelope signal and the bio-signal and the difference between the bio-signal and the lower envelope signal using a scaling function. 
     The apparatus  200  may select the top N sections (N is an arbitrary natural number) having large areas based on calculated areas of each section, and detect peak points and/or valley points in the selected N sections as feature candidates, in operation  1440 . 
     The apparatus  200  may detect one of the feature candidates as a feature based on pre-stored priori information, in operation  1440 . In this case, the priori information may be information about a position from which the feature is detected. For example, the apparatus  200  may calculate distances between the priori information and each of the feature candidates, and detect the feature candidate that is closest to the priori information as a feature of the bio-signal. 
     The apparatus  200  may update pre-stored priori information based on position information of the detected feature, in operation  1450 . 
     Meanwhile, the detected features may be used to estimate a blood pressure of the subject from which the bio-signal is measured. For example, various characteristic values (e.g., time, amplitude, etc.) of the bio-signal may be calculated using the features detected by the apparatus  200  for detecting a bio-signal feature and it is possible to estimate the blood pressure of the subject using the various calculated characteristic values and a pre-stored blood pressure estimation equation. 
     While not restricted thereto, an exemplary embodiment can be embodied as computer-readable code on a computer-readable recording medium. The computer-readable recording medium is any data storage device that can store data that can be thereafter read by a computer system. Examples of the computer-readable recording medium include read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, and optical data storage devices. The computer-readable recording medium can also be distributed over network-coupled computer systems so that the computer-readable code is stored and executed in a distributed fashion. Also, an exemplary embodiment may be written as a computer program transmitted over a computer-readable transmission medium, such as a carrier wave, and received and implemented in general-use or special-purpose digital computers that execute the programs. Moreover, it is understood that in exemplary embodiments, one or more units of the above-described apparatuses and devices can include circuitry, a processor, a microprocessor, etc., and may execute a computer program stored in a computer-readable medium. 
     The foregoing exemplary embodiments are merely examples and are not to be construed as limiting. The present teaching can be readily applied to other types of apparatuses. Also, the description of the exemplary embodiments is intended to be illustrative, and not to limit the scope of the claims, and many alternatives, modifications, and variations will be apparent to those skilled in the art.