Abstract:
An ECG sensing apparatus and a method for removing a baseline drift in the clothing are provided. The ECG sensing apparatus filters ECG signals and filters a baseline drift noise which is caused by human body&#39;s motion and breathing. Accordingly, the baseline drift in the sensing apparatus is minimized even if there is a free motion.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority from Korean Patent Application No. 10-2012-0110506, filed on Oct. 5, 2012 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. 
     BACKGROUND 
     1. Field 
     Methods and apparatuses consistent with exemplary embodiments relate to an electrocardiogram (ECG) sensing apparatus, and more particularly, to an ECG sensing apparatus which is built in clothing. 
     2. Description of the Related Art 
     Convergence between small-scale personal area technologies and health care equipments enable medical health care monitoring to be performed on a real time basis, and thus is expected as a high value-added field that can accomplish treatment and diagnosis. 
     The small-scale personal area technologies include radio frequency identification (RFID), ultra wide band (UWB), Bluetooth, Zigbee, wireless sensor network, etc., and these technologies have been developed to replace wires in various fields and provide convenience to users. 
     As such, recent wearable health care documents or products have announced a biosensor node of a miniaturized wearable type that can measure ECG and photoplethysmograph (PPG) signals on a real time basis and transmit bio-signals using a local area network, or various related products have been released. 
     The ECG signals, which are the most representative of bio-signals, make it possible to extract a variety of bio-information such as a heartbeat, a stress index, a breathing rate, arrhythmia, etc., and thus may be regarded as an indicator for providing information such as patient&#39;s heart condition or normal health condition. 
     A health care system of such a bio-based wearable type may cause a baseline noise due to breathing and may cause a muscle sound which is generated by an effect of peripherals or patient&#39;s motion. 
     In particular, motion artifacts may be caused by a change in the impedance of electrodes when a user wears the electrodes and walks, runs, or breathes. Such a noise may frequently appear when the ECG signals are recorded, and it is difficult to analyze the signals and thus it is difficult to diagnose and analyze exactly without removing the noise appropriately. 
     As a solution to prevent the baseline drift, the ECG may be monitored using a high pass filter of a high cutoff frequency. However, in this case, there is a problem that an effective ECG signal is distorted. 
     In recent years, an adaptive filter is used to remove the baseline drift noise overlapping the ECG or PPG signals. This filter may show good performance, but should use an objective reference signal to remove a noise signal overlapping original signals. 
     However, when a wrong reference signal is used as input, no noise signal is removed or the ECG signal may be distorted. Also, since the baseline noise should be removed using a certain reference signal as input every time that a noise signal overlaps the ECG signal, it is not easy to require mobility. 
     SUMMARY 
     One or more exemplary embodiments may overcome the above disadvantages and other disadvantages not described above. However, it is understood that one or more exemplary embodiment are not required to overcome the disadvantages described above, and may not overcome any of the problems described above. 
     One or more exemplary embodiments provide an ECG sensing apparatus using a least mean square (LMS) algorithm-based adaptive linear prediction filter, which can minimize a baseline drift in a sensing apparatus even if there is a free motion, and a method thereof. 
     According to an aspect of an exemplary embodiment, there is provided an ECG sensing apparatus including: an ECG sensor configured to measure human body&#39;s ECG and generate an ECG signal; a pre-processor configured to perform filtering and amplification with respect to the ECG signal which is generated by the ECG sensor; an analog-digital converter (ADC) configured to convert the ECG signal which is output from the pre-processor from an analogue format into a digital format; a filter configured to filter the ECG signal applied by the ADC and filter a baseline drift noise which is caused by motion and breathing of the human body; and a communication module configured to wirelessly transmit the ECG signal which is filtered by the filter. 
     The filter may not use an additional reference signal other than the ECG signal that is input from the ADC. 
     The filter may include: an inputter configured to receive the ECG signal which is converted into the digital signal by the ADC; an adaptive linear prediction filter configured to remove the baseline drift noise which exists in the ECG signal input to the inputter; a buffer configured to temporarily store the filtered ECG signal which is output from the adaptive linear prediction filter; and an outputter configured to transmit the ECG signal temporarily stored in the buffer to the communication module. 
     The adaptive linear prediction filter may include: a sub delayer which includes a plurality of delayers configured to shift the ECG signals input to the inputter in sequence; a main delayer which includes a plurality of delayers configured to shift the ECG signals output from the sub delayers in sequence; a gain unit configured to multiply the ECG signals temporarily stored in the delayers of the main delayer by corresponding coefficients, and output the ECG signals; and an adder configured to add up all of the outputs of the gain unit and generate a filtered ECG signal. 
     A number of delayers provided in the sub delayer may be smaller than a number of delayers provided in the main delayer. 
     The adaptive linear prediction filter may further include: a subtractor configured to subtract the filtered ECG signal which is generated by the adder from the ECG signal input to the inputter, and generate an error signal; and a coefficient controller configured to update the coefficients of the gain unit using the error signal which is output from the subtractor and the ECG signals which are temporarily stored in the delayers of the main delayer. 
     The coefficient controller may update the coefficients of the gain unit using the following equation:
 
 w[k]=w[k]+μεx[k+ delay]
 
     wherein w[k] is a coefficient of a k-th amplifier of the gain unit, μ is a convergence constant, ε is an error signal, x[k+delay] is an ECG signal which is temporarily stored in a k-th delayer of the main delayer, and delay is a number of delayers provided in the sub delayer. 
     According to an aspect of another exemplary embodiment, there is provided a method for sensing ECG, the method including: measuring human body&#39;s ECG and generating an ECG signal; pre-processing to perform filtering and amplification with respect to the ECG signal which is generated in the generating operation; converting the ECG signal which is output in the pre-processing operation from an analogue format into a digital format; filtering the ECG signal which is converted in the converting operation and filtering a baseline drift noise which is caused by motion and breathing of the human body; and wirelessly transmitting the ECG signal which is filtered in the filtering operation. 
     According to the exemplary embodiments as described above, the ECG sensing apparatus using the LMS algorithm-based adaptive linear prediction filter can minimize a baseline drift in the sensing apparatus even if there is a free motion. Also, the delayers are distinguished so that interrelationship between the noise of the ECG signal which is input to the adaptive linear prediction filter, and the noise of the ECG signals which are temporarily stored in the main delayer can be eliminated when the coefficient is updated. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The above and/or other aspects will be more apparent by describing in detail exemplary embodiments, with reference to the accompanying drawings, in which: 
         FIG. 1  is a block diagram illustrating an ECG sensing apparatus according to an exemplary embodiment; 
         FIG. 2  is a detailed block diagram of a filter of  FIG. 1 ; 
         FIG. 3  is a detailed block diagram of an adaptive linear prediction filter of  FIG. 2 ; and 
         FIG. 4  is a flowchart to illustrate a filtering process of the adaptive linear prediction filter of  FIG. 3 . 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     Hereinafter, exemplary embodiments will be described in greater detail with reference to the accompanying drawings. 
     In the following description, same reference numerals are used for the same elements when they are depicted in different drawings. The matters defined in the description, such as detailed construction and elements, are provided to assist in a comprehensive understanding of exemplary embodiments. Thus, it is apparent that exemplary embodiments can be carried out without those specifically defined matters. Also, functions or elements known in the related art are not described in detail since they would obscure the exemplary embodiments with unnecessary detail. 
       FIG. 1  is a block diagram illustrating an ECG sensing apparatus according to an exemplary embodiment. The ECG sensing apparatus  100  according to an exemplary embodiment is an apparatus that measures an ECG signal on a real time basis, and has a limited processing ability but can remove a baseline noise from the ECG signal effectively. 
     Therefore, when the ECG sensing apparatus  100  is built in clothing, the ECG sensing apparatus  100  according to an exemplary embodiment can minimize motion artifacts. The ECG sensing apparatus  100  may be manufactured to be attachable to or detachable from clothing or a chest belt, and measured ECG information may be transmitted to a base station (not shown) through a relay node (not shown). 
     The ECG sensing apparatus  100 , which performs the above-described function, includes an ECG sensor  110 , a pre-processor  120 , an analog-digital converter (ADC)  130 , a filter  140 , and a radio frequency (RF) module  150 . 
     The ECG sensor  110 , which is a conductive textile electrode, measures human body&#39;s ECG and generates an ECG signal, and transmits the generated ECG signal to the pre-processor  120 . 
     The pre-processor  120  performs pre-processing such as amplification and filtering with respect to the ECG signal generated by the ECG sensor  110 . Specifically, the pre-processor  120  removes a low frequency noise included in the ECG signal using a high pass filter (HPF), amplifies the ECG signal from which the low frequency noise has been removed, removes a high frequency noise included in the ECG signal using a low pass filter (LPF), amplifies the ECG signal from which the high frequency noise has been removed again, and outputs the ECG signal. 
     The ADC  130  converts the ECG signal which is output from the pre-processor  120  from an analogue format to a digital format, and applies the ECG signal to the filter  140 . 
     The filter  140  filters the digital ECG signal which is applied by the ADC  130 , and filters a baseline drift noise which is caused by motion and breathing of a user who wears the ECG sensing apparatus  100 . 
     In particular, the filter  140  does not use an additional reference signal other than the input ECG signal. A configuration of the filter  140  will be explained in detail. 
     The RF module  150  wirelessly transmits the ECG signal which has been filtered by the filter  140  to the relay node. 
     Hereinafter, the above-described filter  140  will be explained in detail with reference to  FIG. 2 .  FIG. 2  is a detailed block diagram of the filter  140  of  FIG. 1 . As shown in  FIG. 2 , the filter  140  includes an inputter  141 , an adaptive linear prediction filter  143 , a buffer  145 , and an outputter  147 . 
     The inputter  141  receives the ECG signal which has been converted into the digital signal by the ADC  130 , and transmits the ECG signal to the adaptive linear prediction filter  143 . 
     The adaptive linear prediction filter  143  removes the baseline drift noise which exists in the ECG signal input through the inputter  141  based on an LMS algorithm. 
     The buffer  145  is a space that temporarily stores the filtered ECG signal which is output from the adaptive linear prediction filter  143 . 
     The outputter  147  extracts the ECG signals when the ECG signals are stored in all of the storage spaces of the buffer  145  (a buffer full state), and transmits the ECG signals to the RF module  150 . 
     Hereinafter, the adaptive linear prediction filter of  FIG. 2  will be explained in detail with reference to  FIG. 3 .  FIG. 3  is a detailed block diagram of the adaptive linear prediction filter  143  of  FIG. 2 . 
     As shown in  FIG. 3 , the adaptive linear prediction filter  143  includes a sub delayer  210 , a main delayer  220 , a gain unit  230 , an adder  240 , a subtractor  250 , and a coefficient controller  260 . 
     The sub delayer  210  includes 5 delayers (Z −1 ) and the main delayer  220  includes 12 delayers (Z −1 ). The ECG signal (x[n]) which is input to the adaptive linear prediction filter  143  is shifted to the delayers (Z −1 ) of the main delayer  220  through the delayers (Z −1 ) of the sub delayer  210  in sequence. 
     The number of delayers (Z −1 ) of the sub delayers  210  is smaller than the number of delayers (Z −1 ) of the main delayer  220 . 
     The delayers  210  and  220  are divided into the sub delayer  210  and the main delayer  220  in order to eliminate interrelationship between the noise of the ECG signal which is input to the adaptive linear prediction filter  143 , and the noise of the ECG signals which are temporarily stored in the main delayer  220  when the coefficient is updated. 
     The gain unit  230  includes 12 amplifiers, and multiplies the 12 ECG signals which have been output from the sub delayer  210  and temporarily stored in the main delayer  220  by corresponding coefficients (w 0 , w 1 , w 2 , w 3 , w 4 , . . . , w 11 ), and outputs the ECG signals. 
     The adder  240  adds up the 12 ECG signals which have been multiplied by the coefficients (w 0 , w 1 , w 2 , w 3 , w 4 , . . . w 11 ) in the gain unit  230 . The added ECG signal (y) which is output from the adder  240  is stored in the buffer  145  as output from the adaptive linear prediction filter  143 . 
     The subtractor  250  subtracts the filtered ECG signal (y) which is the output from the adder  240  from the ECG signal (x[n]) which is input to the adaptive linear prediction filter  143 , and generates an error signal (ε). 
     The coefficient controller  260  updates the coefficients (w 0 , w 1 , w 2 , w 3 , w 4 , . . . , w 11 ) of the gain unit  230  using the error signal (ε) which is output from the subtractor  250  and the 12 ECG signals which are temporarily stored in the main delayer  220 . 
     Specifically, the coefficient controller  260  updates the coefficients (w 0 , w 1 , w 2 , w 3 , w 4 , . . . , w 11 ) of the gain unit  230  so that the error signal (ε) can be minimized. The coefficient controller  260  may predict future output based on the past input by minimizing the error between the output which is sufficiently delayed by the delayers (Z −1 ) of the sub delayer  210  and the main delayer  220 , and the input ECG signal that is not delayed. In other words, the coefficient controller  260  may predict the future output using the delayed input and output the same output as an input waveform. 
     The filtering process of the adaptive linear prediction filter  143  of  FIG. 3  will be explained in detail with reference to  FIG. 4 . 
     As shown in  FIG. 4 , an ECG signal is input to the adaptive linear prediction filter  143  and delayed first, and is temporarily stored in sequence and shifted in the delayers (Z −1 ) constituting the sub delayer  210  and the main delayer  220  (S 310 ). 
     Then, the gain unit  230  multiplies the 12 ECG signals which are temporarily stored in the delayers (Z −1 ) constituting the main delayer  220  in operation S 310  by corresponding coefficients (w 0 , w 1 , w 2 , w 3 , w 4 , . . . , w 11 ), and the adder  240  adds up the 12 ECG signals which have bee multiplied by the corresponding coefficients (w 0 , w 1 , w 2 , w 3 , w 4 , . . . , w 11 ) (S 320  and S 330 ). 
     Operation S 320  may be expressed by following equation 1: 
     
       
         
           
             
               
                 
                   y 
                   = 
                   
                     y 
                     + 
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           0 
                         
                         11 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         
                           x 
                           ⁡ 
                           
                             [ 
                             
                               k 
                               + 
                               delay 
                             
                             ] 
                           
                         
                         ⁢ 
                         
                           w 
                           ⁡ 
                           
                             [ 
                             k 
                             ] 
                           
                         
                       
                     
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   ] 
                 
               
             
           
         
       
     
     wherein delay is a number of delayers (Z −1 ) constituting the sub delayer  210  and is 5 in the present exemplary embodiment. 
     Then, the subtractor  250  subtracts the filtered ECG signal (y) which is the output from the adder  240  from the ECG signal (x[0]) which is input to the adaptive linear prediction filter  143  most recently, and generates an error signal (ε) (S 340 ). 
     Next, the coefficient controller  260  updates the coefficients (w 0 , w 1 , w 2 , w 3 , w 4 , . . . , w 11 ) of the gain unit  230  using the error signal (ε) which is generated in operation S 340  and the 12 ECG signals (x[k+delay]) which are temporarily stored in the main delayer  220  (S 350  and S 360 ). 
     Operation S 350  may be expressed by following equation 2:
 
 w[k]=w[k]+μεx[k+ delay]  [Equation 2]
 
     wherein μ is a convergence constant and may be experimentally determined. 
     After that, an ECG signal is newly input, and is delayed and shifted in sequence by 17 delayers (Z −1 ) of the sub delayer  210  and the main delayer  220  (S 370  and S 380 ). 
     On the other hand, when the filtered ECG data (y) is stored in all of the storage spaces of the buffer  145  (S 390 ), the outputter  147  extracts the filtered ECG data and transmits the same to the RF module  150  (S 395 ). 
     The number of delayers (Z −1 ) constituting the sub delayer  210  and the main delayer  220  of the adaptive linear prediction filter  143  may be determined according to properties of the noise or other specifications. 
     The foregoing exemplary embodiments and advantages are merely exemplary and are not to be construed as limiting the present inventive concept. The exemplary embodiments 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.