CN108113665A - A kind of automatic noise-reduction method of electrocardiosignal - Google Patents
A kind of automatic noise-reduction method of electrocardiosignal Download PDFInfo
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Abstract
The invention discloses a kind of automatic noise reduction algorithm of electrocardiosignal, processing procedure is：A）It establishes echo state network and initializes；B）It obtains human ecg signal and builds training set on this basis；C）Using the on-line training Algorithm for Training echo state network based on recurrent least square method, trained echo state network is obtained；D）Test set is built, test set is inputted into trained echo state network, obtains clean electrocardiosignal.Processing through the method for the present invention, the clean electrocardiosignal after denoising not only effectively filter out noise, and have recovered electrocardiosignal characteristics of low-frequency ripple, remain the effective information of electrocardiosignal.
Description
Technical Field
The invention relates to an automatic electrocardiosignal detection and analysis method, in particular to an automatic electrocardiosignal noise reduction method.
Background
Cardiovascular diseases become the healthy first killer of people, and have high morbidity, high disability rate and high mortality. The low-frequency component of the electrocardiosignal is very important for the intelligent diagnosis of cardiovascular diseases, for example, the ST section which is one of the low-frequency components represents the potential change condition in a period of time after the ventricular depolarization is finished and after the ventricular repolarization is started, and the ST section is an important index for diagnosing myocardial ischemia. At the same time, however, the electrocardiographic signal itself has the commonalities of bioelectric signals: the electrocardiosignal acquisition device has the characteristics of weak amplitude, low frequency, large impedance, randomness and the like, and the electrocardiosignal is very easy to be interfered by various noises in the acquisition process. Particularly, in the current remote medical background, the noise during the acquisition of the electrocardiosignals presents more complex characteristics, so that the low-frequency part of the electrocardiosignals is easily submerged by the complex noise, and the phenomenon of effective information loss occurs.
There are three methods commonly used today: first, Empirical Mode Decomposition (EMD) is to extract the Intrinsic Mode Function (IMF) to reduce the noise of a noisy ECG signal, resulting in the removal of 95% white gaussian noise. However, EMD does not separate out signals that are similar in frequency to ECG signals. Therefore, this noise reduction method may filter out P-waves and T-waves from the signal, resulting in misdiagnosis. Second, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are blind source separation techniques used to find independent source signals from non-gaussian noise signals, with significant advantages for in-band filtering. However, the model is very sensitive to slight changes of the noisy ECG signal, and is not suitable for the case where the ECG signal contains more complicated noise. Thirdly, the wavelet denoising method achieves good results in terms of removing high-frequency noise, but the selection of wavelet basis and the setting of threshold value need to be determined through a large amount of experiments, and has direct influence on the final filtering effect. Therefore, the existing algorithm is easy to lose effective low-frequency information of the electrocardiosignals in the noise reduction process, and the judgment of a doctor is influenced.
Disclosure of Invention
The invention aims to provide an automatic noise reduction algorithm for an electric signal, which is used for realizing noise reduction of the electrocardiosignal and simultaneously reserving low-frequency characteristic waves and solving the problem that the effective low-frequency information of the electrocardiosignal is easily lost in the noise reduction process of the existing algorithm.
The purpose of the invention is realized as follows:
an automatic noise reduction algorithm for electrocardiosignals comprises the following steps:
A) establishing an echo state network and initializing;
B) acquiring electrocardiosignals of a human body and constructing a training set on the basis of the electrocardiosignals;
C) training an echo state network by using an online training algorithm based on a recursive least square method, and obtaining and storing the trained echo state network;
D) and constructing a test set by using the electrocardiosignals next to the training set, and inputting the test set into the trained echo state network to obtain clean electrocardiosignals.
The automatic noise reduction algorithm for the electrocardiosignals comprises the following steps of A) initializing a weight matrix and initializing and setting network parameters, wherein:
the process of initializing the weight matrix is as follows:
weight matrix W connecting input unit with the interior of reserve tankinConnected with neurons in the reserve poolRandomly initializing a weight matrix W to make the weight matrix W subjected to uniform distribution;
connecting weight matrix W of output unit and reserve poolbackThe reserve pool is connected with the output unit to form a weight matrix WoutInitializing to a zero matrix;
the initialization setting process of the network parameters comprises the following steps:
the number of input neurons is set to be 4, the number of output neurons is set to be 1, and the number of neurons in a reserve pool is set to be N which is 1000 +/-300;
the sparsity degree SD of the reserve pool is m/N, wherein m is the number of the neurons which are connected with each other in the reserve pool, and the value range of SD is 1-5%;
debugging the value of the spectrum radius SR of the reserve pool according to the step length of 0.1 within the range of 0-1, and taking the SR value corresponding to the optimal result as a final value, wherein the SR is max { abs (characteristic value of W) };
the value of the input unit dimension IS of the reserve pool IS debugged in a range of-1 to 1 by taking 0.01 as a step length, and the corresponding IS value when the result IS optimal IS taken as a final value.
The automatic noise reduction algorithm for the electrocardiosignal comprises the following specific steps in the step B):
firstly, a plurality of continuous heart beats x (n) are intercepted from the acquired human body electrocardiosignal data, wherein n is 1,2, …, M is 55 +/-2, and then the input signal x (n) of the echo state network is formed by the noisy electrocardiosignal x (n) at the current moment, the noisy electrocardiosignal x (n-1) at the previous moment, the first derivative x '(n) of the noisy electrocardiosignal at the current moment and the second derivative x' (n) of the noisy electrocardiosignal at the current momentr(n) is represented by
Defining clean heart beat data corresponding to the electrocardiosignal x (n) containing noise at the current moment as d (n);
from xr(n) and d (n) form a training set (x)r(n),d(n),n＝1,2,…,M)；
definition of-Wherein g (-) is an output neuron activation function selected as an identity function, i.e., the output neuron is a linear neuron, and in the function, the matrix v (n) is defined as Wherein r (n) is a reserve pool state variable;
(iii) defining the error e (n) ═ d (n) -y (n).
The automatic noise reduction algorithm for the electrocardiosignal comprises the following specific steps in the step C):
selecting a network, setting the initial value of a reserve pool state variable to be 0, namely, setting r (0) to be 0, and training set (x)rSample x in (n), d (n), n ═ 1,2, …, M)r(n) passing through WinD (n) passing through Wbackadding into stock tank respectively, and using formula r (n) ═ 1- α [ f (W.r (n-1) + Win·u(n)+Wback·d(n))]+ alpha r (n-1) to update the reserve pool state variable r (n);
wherein f (-) is a reservoir neuron activation function selected from a non-linear tanh (-) function;
u (n) ═ ψ (n-1) · v (n), ψ (n) is a correlation coefficient matrix of v (n) and is formed by the formula To perform the update, wherein,λ is a forgetting factor, whichDebugging is carried out in a range of 0.95-1 by taking 0.0001 as a step length, a lambda value is taken as a final value when the result is optimal, psi (0) is delta · I, I is an identity matrix, delta is a very small positive number, debugging is carried out in a range of 0-0.001 by taking 0.00001 as the step length, and the delta value is taken as the final value when the result is optimal;
α is forgetting rate, the value of the α is debugged in a step length of 0.1 within the range of 0-1, and the α value with the optimal result is taken as the final value;
② according to the formula Wout(n)＝Wout(n-1) + k (n) e (n) to output the weight WoutUpdating of (1);
③ by updating Wout(n) and r (n) approximate y (n) to the clean ECG signal d (n) so that the error e (n) can approximate 0, resulting in a trained echo state network.
The automatic noise reduction algorithm for the electrocardiosignal comprises the following specific steps in the step D):
constructing a test set (X) by using the T heart beats next to the M heart beats intercepted during the construction of the training setr(n), n ═ 1,2, …, T ═ 200 ± 10), where,namely, the electrocardiosignal X (n) at the current moment, the previous electrocardiosignal X (n-1), the first derivative X '(n) and the second derivative X' (n) are included;
test set (X)rX in (n), n-1, 2, …, T)r(n) inputting the trained echo state network, and outputting a clean electrocardiosignal Y (n) by the network.
By the processing of the method, the denoised clean electrocardiosignal not only effectively filters noise, but also recovers the low-frequency characteristic wave of the electrocardiosignal and retains the effective information of the electrocardiosignal.
Drawings
FIG. 1 is a flow chart of the process of carrying out the method of the present invention.
Fig. 2 is a schematic diagram of an echo state network structure.
Fig. 3 is a schematic diagram of the waveform structure of human electrocardiosignals.
Fig. 4 is a graph of a noisy cardiac signal prior to filtering.
FIG. 5 is a graph of a filtered clean cardiac signal.
Detailed Description
The embodiment is realized in a computer with an Intel Xeon CPU E5-2697@2.70GHz and a 128.00GB Win7 internal memory 64-bit operating system, and the whole electrocardiosignal automatic classification algorithm is realized by adopting Matlab language.
With reference to fig. 1, the implementation of the present invention is as follows:
A) establishing an Echo State Network (ESN):
firstly, initializing a weight matrix:
weight matrix W for random initialization input unit and internal connection of reserve tankinConnecting the weight matrix W with neurons in the reserve pool, and enabling the weight matrix W and the neurons to be uniformly distributed;
connection weight matrix W of output unit and reserve poolbackThe reserve pool is connected with the output unit to form a weight matrix WoutInitializing to a zero matrix;
②, setting parameters:
the number of input neurons of ESN is set to 4, the number of output neurons is set to 1, and the reserve pool size N, which is the number of neurons in the reserve pool, is 1000;
the reservoir sparsity degree SD of the ESN is calculated according to the formula SD ═ m/N, where m is the number of neurons connected to each other in the reservoir, and in this example, m is taken as m ═ 20, and SD is 2%;
the spectrum radius SR of the reserve pool is defined as SR ═ max { abs (characteristic value of W) }, because ESN has the property of echo state when SR is less than 1, in this example, the SR value is debugged by taking 0.1 as the step length according to the range of 0-1, and the SR ═ 0.4 is taken when the result is optimal;
definition of WSFor a randomly generated sparse matrix with uniformly distributed sparsity degree SD, the neuron connection weight matrix W in the reserve pool is SR.WSEnsuring the spectrum radius of W as SR;
the input unit scale IS of the reserve pool IS a scale factor which needs to be multiplied before an input signal IS connected to the reserve pool, the input signal IS multiplied by the input factor, so that the input signal IS transformed into an input range of a neuron activation function of the reserve pool, the input range of the neuron activation function IS [ -1, 1], in the embodiment, the value of IS IS debugged in a range of-1 to 1 by taking 0.01 as a step length, and the IS IS [0.01 ] when the result IS optimal; 0.01; 0.9; 0.8 ];
B) collecting human body electrocardio original signals at a collection frequency of 250Hz, wherein the waveform structure is shown in figure 3 and is stored in a data form of a TXT document, reading the electrocardio original signal data stored in the TXT document into a computer by Matlab software to be used as original electrocardiosignals, and constructing a training set as shown in figure 4:
definition of xr(n) is an input signal of ESN, which is obtained by extracting a series of M ═ 55 cardiac beats x (n) from the original electrocardiographic signal, where n ═ 1,2, …,55, and is composed of a noisy electrocardiographic signal x (n) at the current time, a noisy electrocardiographic signal x (n-1) at the previous time, a first derivative x' (n) of the noisy electrocardiographic signal at the current time, and a second derivative x ″ (n) of the noisy electrocardiographic signal at the current time, and is expressed asIs an Nx 4 matrix; defining clean heart beat data corresponding to the current heart beat as d (n); from xr(n) and d (n) form a training set (x)r(n),d(n),n＝1,2,…,55)。
Definition ofWherein g (-) is an output neuron activation function selected as an identity function, i.e., the output neuron is a linear neuron, and in the function, the matrix v (n) is defined asWherein r (n) IS a state variable of the reserve pool, and diag (IS) represents that an IS IS taken as a diagonal line to generate a diagonal matrix;
defining an error e (n) ═ d (n) -y (n);
C) training an echo state network by using an online training algorithm based on a recursive least square method:
① initial value of ① state variable of ① reserve pool of ① selected network is 0, namely r (0) is 0, and ① training set (x)rSample x in (n), d (n), n ═ 1,2, …,55)r(n) passing through WinD (n) passing through Wbackadding into stock tank respectively, and using formula r (n) ═ 1- α [ f (W.r (n-1) + Win·u(n)+Wback·d(n))]+ α · r (n-1) updates the reserve pool state r (n):
wherein f (-) is a reserve pool neuron activation function, and is selected as a nonlinear tanh (-) function due to the fact that strong nonlinearity is needed when the network is modeled;
in the function, u (n) ═ ψ-1(n-1) · v (n), ψ (n) being an inverse matrix of the correlation coefficient matrix of v (n) and updated by the following expression:
wherein,λ is forgetting factor, defined as a constant with a range of 0.95 < λ < 1, and its value is adjusted by using 0.0001 as step length, and when the result is optimum, the value of λ is taken as final value, in this example, λ is 0.9999, and ψ (0) is equal to ψ (0)δ-1I, I is an identity matrix, δ is a very small positive number, the value range of δ is 0-0.001, the debugging is performed by using 0.00001 as a step length, and the δ value is a final value when the result is optimal (that is, when input signals in a training set are put into the network again and the error between y (n) and d (n) is approximately 0), in this example, δ is 0.00172;
in the function, α is a forgetting rate, defined as a positive number smaller than 1, and has a value range of 0 to 1, and the function is debugged with a step size of 0.1, and the value α when the result is optimal is taken as a final value, in this example, the value α is 0.8.
② according to the formula Wout(n)＝Wout(n-1) + k (n) e (n) to output the weight WoutAnd (4) updating.
③ by updating Wout(n) and r (n) approximate y (n) to a clean ECG signal d (n) so that the error e (n) can be approximated to 0, resulting in and preserving a well-trained echo state network.
When y (n) is close to d (n), namely the error e (n) is close to 0, the network is in a stable state, and the trained state-returning network is stored and can be used for denoising the electrocardiosignals.
D) And (3) verification:
constructing a test set by using the immediately adjacent T (200) heartbeats after the 55 heartbeats intercepted in the process of constructing the training set, wherein the test set is a test set (X)r(n), n is 1,2, …,200), wherein,namely, the electrocardiosignal X (n) at the current moment, the previous electrocardiosignal X (n-1), the first derivative X '(n) and the second derivative X' (n) are included;
test set (X)rX in (n), n is 1,2, …,200)r(n) inputting the trained echo state network, and outputting a clean electrocardiosignal Y (n) by the network, as shown in FIG. 5.
From the above processing results, it can be seen that the low-frequency characteristic waves of the electrocardiosignals T and P are seriously distorted under the interference of noise when the electrocardiosignals containing the noise (as shown in FIG. 4) are compared with the waveforms of the normal electrocardiosignals (as shown in FIG. 2), and the clean electrocardiosignals (as shown in FIG. 5) after being denoised not only effectively filter the noise, but also recover the low-frequency characteristic waves of the electrocardiosignals and retain the effective information of the electrocardiosignals after being processed by the method of the present invention.
Claims (5)
1. An automatic noise reduction algorithm for electrocardiosignals is characterized by comprising the following steps:
A) establishing an echo state network and initializing;
B) acquiring electrocardiosignals of a human body and constructing a training set on the basis of the electrocardiosignals;
C) training an echo state network by using an online training algorithm based on a recursive least square method, and obtaining and storing the trained echo state network;
D) and constructing a test set by using the electrocardiosignals next to the training set, and inputting the test set into the trained echo state network to obtain clean electrocardiosignals.
2. The automatic noise reduction algorithm for electrocardiosignals according to claim 1, wherein the step A) comprises initializing a weight matrix and initializing and setting network parameters, wherein:
the process of initializing the weight matrix is as follows:
weight matrix W connecting input unit with the interior of reserve tankinRandomly initializing a weight matrix W connected with neurons in the reserve pool to ensure that the weights are uniformly distributed;
connecting weight matrix W of output unit and reserve poolbackThe reserve pool is connected with the output unit to form a weight matrix WoutInitializing to a zero matrix;
the initialization setting process of the network parameters comprises the following steps:
the number of input neurons is set to be 4, the number of output neurons is set to be 1, and the number of neurons in a reserve pool is set to be N which is 1000 +/-300;
the sparsity degree SD of the reserve pool is m/N, wherein m is the number of the neurons which are connected with each other in the reserve pool, and the value range of SD is 1-5%;
debugging the value of the spectrum radius SR of the reserve pool according to the step length of 0.1 within the range of 0-1, and taking the SR value corresponding to the optimal result as a final value, wherein the SR is max { abs (characteristic value of W) };
the value of the input unit dimension IS of the reserve pool IS debugged in a range of-1 to 1 by taking 0.01 as a step length, and the corresponding IS value when the result IS optimal IS taken as a final value.
3. The automatic noise reduction algorithm for electrocardiosignals according to claim 2, wherein the step B) comprises the following steps:
firstly, a plurality of continuous heart beats x (n) are intercepted from the acquired human body electrocardiosignal data, wherein n is 1,2, M is 55 +/-2, and then the noise-containing electrocardiosignal x (n) at the current moment, the noise-containing electrocardiosignal x (n-1) at the previous moment, the first derivative x' (n) of the noise-containing electrocardiosignal at the current moment and the second derivative of the noise-containing electrocardiosignal at the current moment are taken as the basisThe number x' (n) constitutes the input signal x of the echo state networkr(n) is represented by
Defining clean heart beat data corresponding to the electrocardiosignal x (n) containing noise at the current moment as d (n);
from xr(n) and d (n) form a training set (x)r(n)，d(n)，n＝1，2，...，M)；
definition of-Wherein g (-) is an output neuron activation function selected as an identity function, i.e., the output neuron is a linear neuron, and in the function, the matrix v (n) is defined as Wherein r (n) is a reserve pool state variable;
(iii) defining the error e (n) ═ d (n) -y (n).
4. The automatic noise reduction algorithm for electrocardiosignals according to claim 3, wherein the specific process of the step C) is as follows:
selecting a network, setting the initial value of a reserve pool state variable to be 0, namely, setting r (0) to be 0, and training set (x)rSample x in (n), d (n), n ═ 1, 2.., M)r(n) passing through WinD (n) passing through Wbackadding into stock tank respectively, and using formula r (n) ═ 1- α [ f (W.r (n-1) + Win·u(n)+Wback·d(n))]+ alpha r (n-1) to update the reserve pool state variable r (n);
wherein f (-) is a reservoir neuron activation function selected from a non-linear tanh (-) function;
u (n) ═ ψ (n-1) · v (n), ψ (n) is a correlation coefficient matrix of v (n) and is formed by the formula To perform the update, wherein,lambda is a forgetting factor, the value of the lambda is adjusted in a range of 0.95-1 by taking 0.0001 as a step length, the lambda value is a final value when the result is optimal, psi (0) is delta.I, I is an identity matrix, delta is a very small positive number, the value of the lambda is adjusted in a range of 0-0.001 by taking 0.00001 as the step length, and the delta value when the result is optimal is a final value;
α is forgetting rate, the value of the α is debugged in a step length of 0.1 within the range of 0-1, and the α value with the optimal result is taken as the final value;
② according to the formula Wout(n)＝Wout(n-1) + k (n) e (n) to output the weight WoutUpdating of (1);
③ by updating Wout(n) and r (n) approximate y (n) to the clean ECG signal d (n) so that the error e (n) can approximate 0, resulting in a trained echo state network.
5. The automatic noise reduction algorithm for electrocardiosignals according to claim 4, wherein the specific process of the step D) is as follows:
constructing a test set (X) by using the T heart beats next to the M heart beats intercepted during the construction of the training setr(n), n ═ 1, 2., T ═ 200 ± 10), where,namely, the electrocardiosignal X (n) at the current moment, the previous electrocardiosignal X (n-1), the first derivative X '(n) and the second derivative X' (n) are included;
test set (X)rX in (n), n ═ 1, 2.., T)r(n) inputting the trained echo state network, and outputting a clean electrocardiosignal Y (n) by the network.
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| CN110169768A (en) * | 2019-07-08 | 2019-08-27 | 河北大学 | A kind of automatic noise-reduction method of electrocardiosignal |
| CN111209853A (en) * | 2020-01-05 | 2020-05-29 | 天津大学 | Optical fiber sensing vibration signal mode identification method based on AdaBoost-ESN algorithm |
| CN111860306A (en) * | 2020-07-19 | 2020-10-30 | 陕西师范大学 | Electroencephalogram signal denoising method based on width depth echo state network |
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| CN116304777B (en) * | 2023-04-12 | 2023-11-03 | 中国科学院大学 | Self-adaptive electrocardiosignal denoising method and system based on reference signal during rest |
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