阅读说明：本技术 一种穿戴式心律失常检测装置 (Wearable arrhythmia detection device ) 是由 刘光达 孙玉冰 蔡靖 胡新蕾 周子健 于 2021-09-08 设计创作，主要内容包括：本发明提供一种穿戴式心律失常检测装置,包括心电信号采集单元、呼吸信号采集单元、控制器和PC机,其特征在于,所述控制器对所述心电信号采集单元和所述呼吸信号采集单元进行控制,所述PC机对心电信号和呼吸信号进行处理,以实现对受试者心律失常的自动检测。本发明提出的一种穿戴式心律失常检测装置,利用穿戴式心电监测装置可以实时检测受试者的心电变化,并有效利用采集的心电信号,进行特征提取,最后利用深度神经网络对心律失常进行分类,从而实现穿戴式心律失常检测的目的。(The invention provides a wearable arrhythmia detection device, which comprises an electrocardiosignal acquisition unit, a respiration signal acquisition unit, a controller and a PC (personal computer), and is characterized in that the controller controls the electrocardiosignal acquisition unit and the respiration signal acquisition unit, and the PC processes the electrocardiosignal and the respiration signal so as to realize automatic detection of arrhythmia of a subject. The wearable arrhythmia detection device provided by the invention can be used for detecting the electrocardio change of a testee in real time, effectively utilizing the acquired electrocardio signals to extract the characteristics, and finally classifying arrhythmia by using the deep neural network, thereby realizing the purpose of wearable arrhythmia detection.)

1. The utility model provides a wearing formula arrhythmia detection device, includes electrocardiosignal acquisition unit, breathing signal acquisition unit, controller and PC, its characterized in that, the controller is right electrocardiosignal acquisition unit with breathing signal acquisition unit controls, the PC handles electrocardiosignal and breathing signal to the realization is to the automated inspection of experimenter arrhythmia.

2. The wearable arrhythmia detection device of claim 1, wherein an input end of the electrocardiosignal acquisition unit is connected to a three-lead electrocardio-electrode, an input end of the respiration signal acquisition unit is impedance between a positive electrode and a negative electrode of the electrocardiosignal, and output ends of the electrocardiosignal acquisition unit and the respiration signal acquisition unit are connected to an input end of the controller.

3. The wearable arrhythmia detection device of claim 2, further comprising a wireless communication unit, wherein the wireless communication unit is a bluetooth module, a transmitting end of the wireless communication unit is connected to the controller, a receiving end of the wireless communication unit is connected to the PC, and the PC displays the ecg and respiration waveforms of the subject.

4. The wearable arrhythmia detection device of claim 3, wherein the PC is configured to display, data process, and utilize a deep neural network to automatically classify arrhythmia in the form of a display of the subject's cardiac and respiratory waveforms.

5. The wearable arrhythmia detection device of claim 2, wherein the automatic classification of arrhythmia using the deep neural network is an automatic classification of arrhythmia using both a Forward Feedback Neural Network (FFNN) and a Convolutional Neural Network (CNN) model.

6. The wearable arrhythmia detection device of claim 1, wherein the FFNN comprises five layers, namely an input layer, a hidden layer 1, a hidden layer 2, a hidden layer 3 and an output layer, wherein full connections are adopted among the layers, the output layer adopts a softmax activation function, and other layers adopt a ReLU activation function.

7. The wearable arrhythmia detection device of claim 1, wherein the FFNN employs full connectivity between layers, and the forward calculation formula from layer n-1 to layer n is

X⁽ⁿ⁾＝f⁽ⁿ⁾(W⁽ⁿ⁾X^(n-1)+B⁽ⁿ⁾)

The output layer adopts a softmax activation function, and the expression is

The other layer adopts a ReLU activation function, and the expression is

f(x)＝max(0,x)

Wherein W is a linear relationship between layers, f⁽ⁿ⁾As a function of layer n-1 to layer n, X^(n-1)Is the output signal of the n-1 th layer, B is the bias, x_iTo output an input signal for layer i, j refers to traversal through n neurons.

8. The wearable arrhythmia detection device of claim 7, wherein the FFNN has an input of 200ms ECG segments and an output of detected R-wave position information, and is used for detecting the R-wave position of the ECG signal, and further using resampling calculation to obtain the characteristic value RR for automatic arrhythmia classification, wherein the formula is

RR＝Resample(ECGs[qrs_m-2:qrs_m],360)

Wherein ECGs represents the ECG buffer, qrs_m-2The previous R-wave point position, qrs, representing the current R-wave point_mThe position of the next R-wave point after the current R-wave point is indicated.

9. The wearable arrhythmia detection device of claim 8, wherein the CNN is configured to perform training and testing on the arrhythmia after extracting the ecg signal features RR, perform feature transformation on the feature values RR by connecting each convolutional layer and pooling layer in series, and measure loss between the output calculated for the training samples and the output of the real training samples.

10. The wearable arrhythmia detection device of claim 9, wherein the CNN has an output layer dimension of 1 x 13 and comprises 13 classifications of arrhythmia, namely, normal heartbeat N, left bundle branch block L, right bundle branch block R, (episodic) episodic escape J, atrial escape E, atrial premature a, heterotic atrial premature a, episodic (episodic) premature J, supraventricular premature or ectopic beat S, ventricular premature V, ventricular escape E, ventricular fused heartbeat F, and unclassified heartbeat Q.

Technical Field

The invention relates to the technical field of medical equipment, in particular to a wearable arrhythmia detection device.

Background

Along with the development of science and technology and the improvement of the living standard of people, the attention degree of people to the health condition is also being showing and is promoting, has more and more people hope to know the health condition of self to in time seek medical advice in finding the problem. The heart is a vital organ of a human body, however, because of the influence of epidemic situations, procedures for measuring electrocardio in a hospital are complicated, so that automatic detection related to heart diseases is also concerned widely, and an electrocardio sensor and a computer play a vital role in detection and diagnosis of heart diseases.

Arrhythmia refers to abnormal frequency and/or rhythm of heart beats caused by abnormal sinus node activation or activation generated outside the sinus node, such as by slow conduction, blocking or conduction through an abnormal channel, i.e., the origin of heart activity and/or conduction disorder. Genetic arrhythmia is mostly caused by gene channel mutation; acquired arrhythmias can be seen in various organic heart diseases, especially in the development of heart failure or acute myocardial infarction. Therefore, the method is particularly important for detecting arrhythmia, and can effectively detect and early warn various heart diseases.

Currently, most of known devices for detecting arrhythmia are large electrocardiosignal detection devices used in hospitals and the like, a subject is required to lie on a diagnosis and treatment bed in a test process, high requirements are imposed on test environment and the state of the subject, and judgment of arrhythmia depends on professional knowledge and experience of doctors, so that portable and automatic test cannot be realized.

The invention patent application with application publication number CN108937912A discloses an automatic heart rhythm market analysis method based on a deep neural network, which comprises the following steps: generating a multi-channel electrocardiogram sample by three sampling modes; splicing the obtained 600-dimensional electrocardiosignals along a second dimension, amplifying the electrocardiosignals from 600 × 1 dimensions to 600 × 3 dimensions, inputting the electrocardiosignals into a plurality of convolution layer units and LSTM layer units which are sequentially connected in series, wherein an entry layer is arranged between the convolution layer units and the LSTM layer units; the convolutional layer unit comprises a convolutional layer using one-dimensional convolution and an excitation unit operation and a pooling layer operation which are sequentially connected in series with the output end of the convolutional layer; the convolution layer is used for extracting the characteristics of the one-dimensional electrocardiosignals; the output of the LSTM layer unit is connected with a full connection layer of which the excitation unit is softmax in series; outputting; and learning parameters of the deep neural network, and automatically identifying the sample. The method has the defects that the data volume of a sample to be processed is large, the method for extracting the characteristics of the electrocardiosignal is complicated, the time consumption is long, and the arrhythmia detection efficiency of the system is influenced.

Disclosure of Invention

In order to solve the technical problems, the wearable arrhythmia detection device provided by the invention can detect the electrocardio change of a subject in real time by using the wearable arrhythmia monitoring device, effectively utilizes the acquired electrocardio signals to extract the characteristics, and finally classifies arrhythmia by using a deep neural network, thereby achieving the purpose of wearable arrhythmia detection.

The invention provides a wearable arrhythmia detection device, which comprises an electrocardiosignal acquisition unit, a respiration signal acquisition unit, a controller and a PC (personal computer), wherein the controller controls the electrocardiosignal acquisition unit and the respiration signal acquisition unit, and the PC processes the electrocardiosignal and the respiration signal so as to realize automatic detection of arrhythmia of a subject.

Preferably, the input end of the electrocardiosignal acquisition unit is connected to a three-lead electrocardio electrode, the input end of the respiratory signal acquisition unit is impedance between a positive electrode and a negative electrode of the electrocardiosignal, and the output ends of the electrocardiosignal acquisition unit and the respiratory signal acquisition unit are connected to the input end of the controller.

In any of the above aspects, preferably, the controller is configured to control acquisition of the cardiac and respiratory signals and communication of the transmission of the data.

In any of the above schemes, preferably, the apparatus further includes a wireless communication unit, the wireless communication unit is a bluetooth module, a transmitting end of the wireless communication unit is connected to the controller, a receiving end of the wireless communication unit is connected to the PC, and the PC displays the electrocardiographic and respiratory waveforms of the subject.

In any of the above aspects, preferably, the PC is used for display of the subject's cardiac and respiratory waveforms, data processing, and automatic classification of cardiac arrhythmias using a deep neural network.

In any of the above embodiments, preferably, the data processing is performed by filtering the cardiac signal and the respiratory signal using a butterworth filter.

In any of the above schemes, preferably, the automatic classification of the arrhythmia by using the deep neural network is to simultaneously use two models, namely a forward feedback neural network FFNN and a convolutional neural network CNN, to realize the automatic classification of the arrhythmia.

In any of the above schemes, preferably, the FFNN includes five layers, which are an input layer, a hidden layer 1, a hidden layer 2, a hidden layer 3 and an output layer, all layers are connected, the output layer uses a softmax activation function, and the other layers use a ReLU activation function.

In any of the above schemes, preferably, the FFNN adopts full connection between layers, and the forward calculation formula from the n-1 th layer to the n-th layer is

X⁽ⁿ⁾＝f⁽ⁿ⁾(W⁽ⁿ⁾X^(n-1)+B⁽ⁿ⁾)

The output layer adopts a softmax activation function, and the expression is

The other layer adopts a ReLU activation function, and the expression is

f(x)＝max(0,x)

Wherein W is a linear relationship between layers, f⁽ⁿ⁾As a function of layer n-1 to layer n, X^(n-1)Is the output signal of the n-1 th layer, B is the bias, x_iTo output an input signal for layer i, j refers to traversal through n neurons.

In any of the above schemes, preferably, the FFNN inputs 200ms ecg segments and outputs detected R-wave position information for detecting the R-wave position of the ecg signal, and further obtains the feature value RR of arrhythmia automatic classification by resampling calculation, where the formula is

RR＝Resample(ECGs[qrs_m-2:qrs_m],360)

Wherein ECGs represents the ECG buffer, qrs_m-2The previous R-wave point position, qrs, representing the current R-wave point_mThe position of the next R-wave point after the current R-wave point is indicated.

In any of the above schemes, preferably, the CNN is configured to extract the electrocardiosignal features RR, implement training and testing on arrhythmia, implement feature transformation of the feature values RR in a manner that each convolutional layer and pooling layer are connected in series, and measure loss between the output calculated for the training sample and the output of the real training sample.

In any of the above schemes, preferably, the output of each convolutional layer unit of the CNN is connected in series with a pooling layer operation, and the loss function is a cross-entropy function.

In any of the above schemes, it is preferable that the output layer dimension of the CNN is 1 × 13, and the CNN mainly includes 13 categories of arrhythmia, which are normal heartbeat N, left bundle branch block L, right bundle branch block R, (nodal) junctional escape J, atrial escape E, atrial premature a, heterosis atrial premature a, junctional (junctional) premature J, supraventricular premature or ectopic beat S, ventricular premature V, ventricular escape E, ventricular fusion heartbeat F, and unclassified heartbeat Q.

In any of the above schemes, preferably, the three-lead electrocardiograph electrode is detachable from the wearing band, and the band is also detachable from the controller.

The invention provides a wearable arrhythmia detection device, which is used for detecting electrocardio and respiratory signals of a testee in real time, monitoring physiological indexes of the testee, displaying electrocardio and respiratory waveforms of the testee and realizing automatic classification of arrhythmia.

Drawings

Fig. 1 is a block diagram of a preferred embodiment of a wearable arrhythmia detection device in accordance with the present invention.

Fig. 2 is a schematic diagram of an embodiment of a deep neural network classification process in a wearable arrhythmia detection device according to the present invention.

Fig. 3 is a basic block diagram of a preferred embodiment of a wearable arrhythmia detection device according to the invention.

Detailed Description

The invention is further illustrated with reference to the figures and the specific examples.

Example one

As shown in fig. 1, a wearable arrhythmia detection device includes a three-lead electrocardiograph electrode 100, an electrocardiograph signal acquisition unit 110, a respiration signal acquisition unit 120, a controller 130, a PC 140, and a wireless communication unit 150.

The controller 130 controls the electrocardiosignal acquisition unit 110 and the respiration signal acquisition unit 120, and the PC 140 processes the electrocardiosignals and the respiration signals so as to realize automatic detection of arrhythmia of the subject.

The input end of the electrocardiosignal acquisition unit 120 is connected to the three-lead electrocardio-electrode 100, the input end of the respiration signal acquisition unit 130 is the impedance between the positive electrode and the negative electrode of the electrocardiosignal, and the output ends of the electrocardiosignal acquisition unit 120 and the respiration signal acquisition unit 130 are connected to the input end of the controller 140.

The controller 120 is used to control the acquisition of the cardiac and respiratory signals, as well as the transmission of data.

The wireless communication unit 150 is a bluetooth module, the transmitting end of the wireless communication unit 150 is connected to the controller 130, the receiving end of the wireless communication unit 150 is connected to the PC, and the ecg and respiration waveforms of the subject are displayed on the PC 140.

The PC 140 is used for displaying electrocardio and respiration waveforms of a subject, processing data and automatically classifying arrhythmia by using a deep neural network, wherein the data processing is to filter electrocardio signals and respiration signals by using a Butterworth filter, and the automatic classification of arrhythmia by using the deep neural network is to realize the automatic classification of arrhythmia by simultaneously using a forward feedback neural network FFNN model and a convolutional neural network CNN model.

The FFNN comprises five layers which are an input layer, a hidden layer 1, a hidden layer 2, a hidden layer 3 and an output layer, wherein the layers are fully connected, the output layer adopts a softmax activation function, and other layers adopt a ReLU activation function. The layers of the FFNN are all connected, and the forward calculation formula from the n-1 th layer to the n-th layer is

X⁽ⁿ⁾＝f⁽ⁿ⁾(W⁽ⁿ⁾X^(n-1)+B⁽ⁿ⁾)

The output layer adopts a softmax activation function, and the expression is

The other layer adopts a ReLU activation function, and the expression is

f(x)＝max(0,x)

Wherein W is a linear relationship between layers, f⁽ⁿ⁾As a function of layer n-1 to layer n, X^(n-1)Is the output signal of the n-1 th layer, B is the bias, x_iTo output an input signal for layer i, j refers to traversal through n neurons.

The input of the FFNN is 200ms electrocardio segments, the output is detected R wave position information which is used for realizing the detection of the R wave position of the electrocardiosignal, and then a characteristic value RR of automatic arrhythmia classification is obtained by utilizing resampling calculation, and the formula is

RR＝Resample(ECGs[qrs_m-2:qrs_m],360)

Wherein ECGs represents the ECG buffer, qrs_m-2The previous R-wave point position, qrs, representing the current R-wave point_mThe position of the next R-wave point after the current R-wave point is indicated.

The CNN is used for training and testing arrhythmia after extracting electrocardiosignal features RR, realizing feature transformation of the feature value RR in a mode that each convolution layer is connected with a pooling layer in series, and measuring loss between output calculated by the training sample and real training sample output. The output of each convolutional layer unit of CNN is connected with a pooling layer operation in series, and the loss function selects the cross entropy function. The output layer dimension of CNN is 1 × 13, mainly comprising 13 classes of arrhythmia, namely normal heartbeat N, left bundle branch block L, right bundle branch block R, (episodic) junctional escape J, atrial escape E, atrial premature a, heterosis atrial premature a, junctional (junctional) premature J, supraventricular or ectopic beat S, ventricular premature V, ventricular escape E, ventricular fusion heartbeat F and unclassifiable heartbeat Q.

The three-lead electrocardio-electrode 100 can be detached from the wearing bandage, and the bandage can also be detached from the controller.

Example two

The wearable arrhythmia detection device provided by the invention specifically comprises an electrocardio and respiration signal acquisition unit, a controller and a PC:

the acquisition unit of the electrocardio and respiratory signals is used for contacting with a subject and acquiring the electrocardio and respiratory signals;

the controller is used for acquiring control signals and transmitting data;

the PC is used for receiving, processing and displaying the measured electrocardio and respiratory signals of the testee, and finally realizing automatic classification of arrhythmia.

The process of the controller for collecting the control signal and transmitting the data mainly comprises controlling an electrocardiosignal collecting chip and utilizing I²C, communication is carried out between the electrocardio acquisition chip and the controller, and communication between the controller and the PC is carried out by utilizing Bluetooth transmission.

The PC machine performs waveform display and data processing of the electrocardio and respiratory signals, and utilizes the deep neural network to realize automatic classification of arrhythmia.

The electrocardio acquisition unit comprises three electrocardio electrodes and a reference electrode, the input end of the electrocardio acquisition unit is contacted with the skin of the heart part of the human body, and the output end of the electrocardio acquisition unit is connected with the input end of the controller.

The respiration acquisition unit consists of an electrocardio-electrode A and a reference electrode, and the respiration rate is further obtained by calculating the impedance value between the electrocardio-electrode A and the reference electrode.

The respiratory impedance R_bCalculating according to formula (1):

in the formula of U_bIs an equivalent voltage applied to the human body; i is_bIs equivalent current loaded to human body; u shape₂Outputting a voltage for the impedance measurement circuit; r_SThe resistance is a fixed value reference resistance in an impedance measurement circuit.

In the process of detecting the electrocardiosignal by the electrocardio acquisition unit, the electrocardio electrode adopts an electrode with small contact impedance with the skin of a human body and can be connected with an external circuit board by a lead; the conducting wire adopts a signal transmission conducting wire which has a shielding function, excellent conductivity and durability and does not influence the comfort degree of a wearer.

The wireless communication module adopts a Bluetooth module, the input end of the wireless communication module is connected to the controller, the receiving end of the Bluetooth module is connected to a PC (personal computer), and the PC is used for displaying the electrocardio and respiration waveforms and realizing the automatic detection of arrhythmia.

The electrocardio acquisition unit and the respiration acquisition unit form a whole and are connected with a bandage of a wearable garment in a metal button mode to form a detachable device.

The PC waveform display interface comprises a serial port setting interface, a data transmission interface, a waveform display interface, data storage and processing, and judgment and classification of arrhythmia by using a deep neural network.

The data preprocessing adopts three groups of Butterworth filters, and mainly comprises a low-pass filter with the cut-off frequency of 100Hz, a band-pass filter with the cut-off frequency of 49-51Hz and a high-pass filter with the cut-off frequency of 0.2 Hz.

As shown in fig. 2, the deep neural network mainly adopts two models, namely a forward feedback neural network FFNN model and a convolutional neural network CNN model:

the FFNN comprises five layers which are an input layer, a hidden layer 1, a hidden layer 2, a hidden layer 3 and an output layer, wherein the layers are all connected, the output layer adopts a softmax activation function, and other layers adopt a ReLU activation function. The input of the FFNN is 200ms of electrocardiographic segments, the output is detected R-wave position information, which is used to realize the detection of the R-wave position of the electrocardiographic signals, and then the characteristic value RR of the arrhythmia automatic classification is obtained by utilizing resampling calculation (the sequence of the ECG signals between the previous R-wave point and the next R-wave point of the current R-wave point being resampled to a fixed length (360), as shown in formula (2)).

RR＝Resample(ECGs[qrs_m-2:qrs_m],360) (2)

In which ECGs represents the ECG buffer, qrs_m-2The previous R-wave point position, qrs, representing the current R-wave point_mThe position of the next R-wave point after the current R-wave point is indicated.

The CNN realizes the characteristic transformation of input signals by utilizing convolutional layers and pooling layers, the output end of each convolutional layer unit is connected with a pooling layer in series for operation, a cross entropy function is selected as a loss function, the dimension of the output layer of the CNN is 1 x 13, and the CNN mainly comprises 13 categories of arrhythmia, namely normal heartbeat N, left bundle branch conduction block L, right bundle branch conduction block R, (nodal) boundary escape J, atrial escape E, atrial premature A, heterosis atrial premature a, nodal (boundary) premature J, supraventricular premature or ectopic beat S, ventricular premature V, ventricular escape E, ventricular fusion heartbeat F and unsortendable heartbeat Q.

The CNN realizes a training set required in the arrhythmia classification process, and adopts an open source arrhythmia database provided in an MIT-BIH database.

Compared with the prior art, the invention has the beneficial effects that:

(1) the invention is a wearable device, which can detect the electrocardio and respiration signals of a testee in real time, monitor the physiological indexes of the testee, display the electrocardio and respiration waveforms of the testee and realize the automatic classification of arrhythmia;

(2) the wearable device is detachably designed, the electrocardio-electrode is connected with the wearable binding belt through the metal hidden button, and the wearable binding belt is connected with the controller through the metal hidden button;

(3) the communication transmission of the invention utilizes the Bluetooth module, reduces the use of wires, enlarges the activity range of the testee in the test process and reduces the influence on the actions of the testee.

EXAMPLE III

The arrhythmia detection device of the invention comprises an acquisition unit of electrocardiosignals and respiratory signals, a controller and a PC:

the acquisition unit of the electrocardiosignals and the respiratory signals is used for monitoring the electrocardiosignals and the respiratory signals, the input end of the acquisition unit is connected with the electrocardio-electrode, and the output end of the acquisition unit is connected with the input end of the controller;

the controller is used for realizing the control of the acquisition unit and the transmission of the acquired signals, and the data transmission between the controller and the PC is realized through the Bluetooth module;

the PC is used for acquiring waveform display, data processing and arrhythmia automatic classification of electrocardiosignals and respiratory signals.

The electrode arrangement mode and the design of the wearable binding belt are shown in figure 3, the electrocardio-electrode is connected with the wearable binding belt through a metal hidden button, the wearable binding belt is also connected with the controller through the metal hidden button, and the controller is kept to be positioned at the left side of the human body in the wearing process.

Because the electrocardiosignal is weak and is easily interfered by noise in the acquisition and transmission processes, the electrocardiosignal is required to be preprocessed after being acquired, and the noise is filtered on the premise of ensuring the integrity of the electrocardiosignal.

After preprocessing the electrocardiosignals, performing feature extraction and arrhythmia classification on the electrocardiosignals by using a deep neural network, wherein the deep neural network mainly comprises two neural network models: a feed-forward neural network FFNN and a convolutional neural network CNN. The FFNN is used for monitoring the position of the R wave of the electrocardiosignal, the input of the FFNN is 200ms of electrocardiosignal segments, and the output of the FFNN is the position information of the R wave. The layers of the FFNN are in full connection, and forward calculation formulas from the (n-1) th layer to the nth layer are shown as (3):

X⁽ⁿ⁾＝f⁽ⁿ⁾(W⁽ⁿ⁾X^(n-1)+B⁽ⁿ⁾) (3)

wherein W is the linear relationship between layers and B is the bias.

The output layer adopts a softmax activation function, and the expression of the softmax activation function is shown as (4):

the other layers adopt a ReLU activation function, and the expression of the ReLU activation function is shown as (5):

f(x)＝max(0,x) (5)

after the R-wave position is detected by using FFNN, the ECG signal between the previous R-wave point and the next R-wave point of the current R-wave point of the characteristic RR of the electrocardiograph signal is calculated by resampling to a sequence with a fixed length (360), as shown in formula (6).

RR＝Resample(ECGs[qrs_m-2:qrs_m],360) (6)

In which ECGs represents the ECG buffer, qrs_m-2The previous R-wave point position, qrs, representing the current R-wave point_mThe position of the next R-wave point after the current R-wave point is indicated.

The CNN is used for training and testing arrhythmia after extracting electrocardiosignal features RR, the feature transformation of RR is realized in a mode that each convolution layer is connected with a pooling layer in series, the loss between the output calculated for measuring a training sample and the output of a real training sample is measured, calculation and optimization are carried out in the CNN by using a loss function, namely, the loss value is expected to be minimized, and then program output is optimized.

For a better understanding of the present invention, the foregoing detailed description has been given in conjunction with specific embodiments thereof, but not with the intention of limiting the invention thereto. Any simple modifications of the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention. In the present specification, each embodiment is described with emphasis on differences from other embodiments, and the same or similar parts between the respective embodiments may be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The method, apparatus and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.