CN112450941A - Electrocardiosignal compression sampling device and method based on random demodulation structure - Google Patents
Electrocardiosignal compression sampling device and method based on random demodulation structure Download PDFInfo
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Abstract
The invention relates to the field of medical electronic appliances, in particular to an electrocardiosignal compression sampling device and method based on a random demodulation structure. The invention comprises an FPGA controller, a pseudo-random signal generator, a frequency mixer, a filter, an AD converter, an FIFO memory, a data transmission chip, an upper computer OMP algorithm and the like. The invention adopts an FPGA control circuit as a main controller to control a pseudo-random signal generator to generate a pseudo-random sequence signal, the electrocardiosignal and the pseudo-random sequence signal pass through a frequency mixer simultaneously, the frequency components of the electrocardiosignal are distributed on the whole frequency axis, the lower frequency components are intercepted by a low-pass filter, finally AD low-frequency sampling is realized, correct information can be obtained, the sampling information is stored and then transmitted to an upper computer terminal of a computer, and the electrocardiosignal can be accurately reconstructed by an OMP algorithm. The device has the advantages of reducing the power consumption of electrocardiosignal sampling, improving the endurance time of sampling equipment, reducing the storage capacity requirement of a memory in the equipment and reducing the storage cost.
Description
Technical Field
The invention relates to the technical field of medical electronic appliances, in particular to an electrocardiosignal compression sampling device and method based on a random demodulation structure.
Background
Wearable electrocardio monitoring facilities are often used for gathering human electrocardio signal in order to monitor psychological function, respond to in time for emergency. The human body electrocardiosignal frequency range is 0.05Hz to 100Hz, in order to improve the sampling precision, the sampling frequency of the existing electrocardio monitoring equipment is generally higher than 250Hz, the electrocardio monitoring equipment needs to sample continuously for a long time at high frequency, higher requirements are put forward for the analog-to-digital converter (ADC) of the sampling equipment, the storage capacity of a storage and the like, and the sampling difficulty is increased.
In order to solve the problems, the electrocardiosignal compression sampling device and the electrocardiosignal compression sampling method based on the random demodulation structure are provided, and the device and the method can reduce the AD sampling frequency in the electrocardiosignal sampling so as to reduce the sampling power consumption of the electrocardiosignal, improve the endurance time of sampling equipment, reduce the storage capacity requirement of a memory in the equipment and reduce the storage cost. The electrocardiosignal acquisition device can be used for patients or healthy people, and is suitable for families, schools, hospitals and communities.
Disclosure of Invention
Technical problem to be solved
The invention provides an electrocardiosignal compression sampling device and method based on a random demodulation structure, aiming at the problems that a large amount of data can be obtained in the long-time uninterrupted sampling process of electrocardio monitoring equipment, and the sampling power consumption of the equipment is high.
(II) technical scheme
The technical scheme of the invention is as follows: the utility model provides an electrocardiosignal compression sampling device based on random demodulation structure, including FPGA controller, pseudo-random signal generator, the mixer, analog filter, the AD sample thief, the memory, data transmission chip and host computer electricity are connected in proper order, the FPGA controller is connected with the analog filter electricity for the SPI initialization, the FPGA controller is connected with the AD sample thief electricity for the sampling clock, the FPGA controller is connected with memory, data transmission chip and host computer electricity respectively for control storage output.
The sampling method comprises the following steps:
step one, adopting an FPGA controller, storing a section of pseudo-random sequence in a ROM, circularly reading out and generating an analog pseudo-random sequence signal P (t) by a DA;
step two, the pseudo random sequence signal P (t) and the heartThe electric signal X (t) is connected to a mixer to obtain a mixing signal Y1(t) distributing the frequency components of the electrocardiosignal X (t) over the frequency axis;
thirdly, intercepting lower frequency components through low-pass filtering of the analog filter to obtain a filtered signal Y2(t)；
Step four, utilizing the AD sampler to carry out filtering on the signal Y2(t) carrying out low-frequency uniform sampling to obtain an observed value vector Y (m);
and fifthly, storing the sampling information and transmitting the sampling information to an upper computer end of a computer, wherein the electrical signal can be accurately reconstructed by an Orthogonal Matching Pursuit (OMP) algorithm.
(III) advantageous effects
The invention has the advantages that: the sampling frequency of the electrocardiosignal sampling device can be reduced, so that the sampling frequency of the AD converter is far lower than that of the conventional electrocardiosignal sampling device; secondly, the AD power consumption, the data storage power consumption and the data transmission power consumption in the electrocardiosignal sampling device are reduced, and the endurance time of the electrocardiosignal sampling device is prolonged; and finally, the storage capacity requirement of the memory is reduced, and the storage cost is reduced.
Drawings
FIG. 1 is a data flow diagram illustrating the operation of the present invention.
FIG. 2 is a diagram illustrating the effect of Butterworth filters of different orders and different cut-off frequencies on reconstruction errors.
FIG. 3 is a graph of the results of the effects of the setting of single reconstruction of the length of the electrocardiographic signal and the sparsity in the OMP algorithm on the reconstruction error.
FIG. 4 is a block flow diagram of the process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
an electrocardiosignal compression sampling method based on a random demodulation structure comprises the following steps:
s1, an FPGA controller is adopted, a section of +/-1 distributed pseudo-random signal P (n) is stored in a ROM, and an analog pseudo-random signal P (t) is output through an AD 9708.
S2, connecting the pseudo-random sequence signal P (t) and the electrocardiosignal X (t) into the input end of a mixer, wherein the mixer is an AD835 analog multiplier to obtain a mixing signal Y1(t)。
S3, intercepting lower frequency components through low-pass filtering of the analog filter to obtain a filtered signal Y2And (t), wherein the low-pass filter is a MAX262 analog filter, the FPGA controller initializes the MAX262 analog filter into a second-order low-pass Butterworth filter by using the SPI time sequence, the unit impulse response of the filter is h (t), and the unit impulse response h (t) of the analog filter is discretized by using a bilinear transformation method to obtain a vector h (n).
And S4, obtaining an observation value vector Y (m) by the filtered signal through a low-speed AD sampler (the model is AD9280), wherein the sampling frequency of the AD sampler is 180 Hz.
And S5, storing the sampling data in an FIFO memory, and transmitting the sampling data to an upper computer end of a computer through a UART transmitter.
S6, according to the characteristics of the AD9280 device, an upper computer end converts an unsigned binary observation value vector Y (m) into a decimal Y (m), and reconstructs an electrocardiosignal waveform by an Orthogonal Matching Pursuit (OMP) algorithm.
Specifically, the principle of generating the pseudo-random sequence p (n) in S1 is as follows:
wherein, an∈{+1,-1}(n∈Z)，anIs the value range of P (n), Q is the time range T epsilon [0, T]Number of partitions of the inner pseudorandom sequence P (n).
P (n) is stored in ROM, the hopping frequency of P (t) is higher than twice of the highest frequency component in X (t), and the hopping frequency of AD9708 for generating P (t) controlled by the FPGA controller can be set to 360 Hz.
Specifically, the mixer in S2 is an AD835 analog multiplier, and provides a mixing function of multiplying p (t) by x (t) in four quadrants, and the time domain form of mixing the pseudo-random sequence signal p (t) with the cardiac signal x (t) is:
Y1(t)＝X(t)×P(t)
specifically, in S3, the cut-off frequency of the second-order low-pass Butterworth filter is 90Hz, the unit impulse response is h (t), and the low-pass filtering of the mixing signal is equivalent to Y1(t) convolved with the unit impulse response h (t), the frequency domain is then in the form:
specifically, the AD9280 sampler in S4 samples Y at twice the low-pass cutoff frequency2(t) obtaining an mth observation vector y (m) as follows:
wherein, TSIn order to be the sampling interval of the sample,
is the sampling frequency.
Specifically, in S5 and S6, binary observation value vector y (m) is stored in FIFO memory and transmitted to the host computer, and the host computer converts unsigned binary observation value vector y (m) into decimal y (m).
Assuming that the elements in the pseudo-random sequence P (t) are distributed in sequence P (1), P (2.) and P (N), mixing the pseudo-random sequence and the source signal by a mixer, and constructing a diagonal matrix P according to a multiplication process as follows:
discretizing the unit impulse response H (t) of the analog filter by a bilinear transformation method to obtain a vector H (n), wherein the discretization frequency is consistent with the hopping frequency of a pseudorandom sequence P (t), and assuming that the element sequence in H (n) is H (1), H (2.) the. H (N), a unit impulse response matrix H can be constructed by a convolution process as follows:
constructing an observation matrix
The sparse matrix psi is the inverse fourier transform matrix, theta is the perceptual matrix,
the error between the reconstructed signal and the original signal is characterized by a root mean square deviation percentage,
wherein X is an experimental electrocardiosignal and is obtained by sampling at 360Hz,
the signal is reconstructed for the OMP.
The frequency response curve in the passband of the Butterworth filter is flat to the maximum extent, and gradually drops to zero in the stopband, so that the Butterworth filter has a good low-pass filtering effect, and is selected.
As shown in fig. 2, it is shown that the results of the effects of the Butterworth filters of different orders and different cut-off frequencies on the reconstruction errors are obtained, and it can be known from fig. 2 that the reconstruction errors of the electrocardiograph signal compression sampling apparatus constructed by the second-order filter are lower and better than those of the Butterworth filters of other orders.
As shown in fig. 3, the result of the influence of the length of the single reconstruction of the electrocardiographic signal and the sparsity in the OMP algorithm on the reconstruction error is shown, and as can be seen from fig. 3, when the time of the single reconstruction of the signal is within 1.5 to 2s or more than 2.8s, the reconstruction error is low and the value is stable, and the sparsity threshold γ can be set to 0.03.
Specifically, the increase of the single reconstruction time causes the increase of the dimension of the correlation matrix in the reconstruction, which makes the calculation complicated and the time consumption long, which is not favorable for the real-time performance of sampling the electrocardiosignals, and the time can be set to 2.845s, that is, the original electrocardiosignals are reconstructed from 512 bytes of data once.
The electrocardiosignal compression sampling device with the random demodulation structure adopts a second-order Butterworth filter, the time for reconstructing electrocardiosignals in one time is 2.845s, the sparsity threshold gamma is set to be 0.03, when an OMP algorithm is used for reconstruction, the reconstruction error is about 4 percent, namely the reconstruction precision is as high as 96 percent, and the original electrocardiosignals can be reconstructed without distortion.
Fig. 4 is a block diagram of a flow of a process in compressive sampling according to the present invention, which is specifically as follows:
(1) the program initializes the filter, sets the analog filter as a second-order low-pass Butterworth filter, and the low-pass cut-off frequency is 90 Hz.
(2) When the device needs to start to collect electrocardiosignals, the FPGA controller circularly reads out a pseudo-random sequence in the ROM and converts the pseudo-random sequence into a 360Hz analog pseudo-random signal, and the FPGA controller simultaneously generates an AD sampling clock and an FIFO read-write clock.
(3) The electrocardiosignal and the pseudo-random sequence signal are simultaneously input into a mixer to obtain a mixing signal.
(4) The mixed signal is passed through a low-pass filter to obtain a filtered signal.
(5) And sampling the filtered signal by a low-speed AD sampler, wherein the sampling frequency is 180 Hz.
(6) The AD sampler stores sampled data in an FIFO memory, the FPGA controller starts one-time transmission after detecting that the FIFO stores 512 bytes of information, according to the performance of the AD9280 device, an upper computer end converts unsigned binary data into decimal data, and the upper computer accurately reconstructs electrocardiosignals through an OMP algorithm. And simultaneously detecting whether the electrocardiosignal sampling is stopped or not, if so, stopping, and otherwise, returning to the step two to continue the compression sampling and reconstruction process.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The utility model provides an electrocardiosignal compression sampling device based on random demodulation structure, its characterized in that, including FPGA controller, pseudo-random signal generator, the mixer, analog filter, the AD sample thief, the memory, data transmission chip and host computer electricity are connected in proper order, FPGA controller is connected with the analog filter electricity for SPI initialization, FPGA controller is connected with the AD sample thief electricity for the sampling clock, FPGA controller is connected with the memory, data transmission chip and host computer electricity respectively, be used for control storage output.
2. An electrocardiosignal compression sampling method based on a random demodulation structure is characterized by comprising the following steps:
step one, adopting an FPGA controller, storing a section of pseudo-random sequence in a ROM, and circularly reading a generated analog pseudo-random sequence signal P (t);
secondly, the pseudo-random sequence signal P (t) and the electrocardiosignal X (t) are accessed into a mixer to obtain a mixing signal Y1(t) distributing the frequency components of the cardiac signal x (t) over the frequency axis;
thirdly, intercepting frequency components through low-pass filtering of the analog filter to obtain a filtered signal Y2(t)；
Step four, utilizing the AD sampler to carry out filtering on the signal Y2(t) carrying out uniform sampling to obtain an observed value vector Y (m);
and fifthly, storing and transmitting the sampled information to an upper computer end of a computer, and accurately reconstructing the electric signal by an orthogonal matching tracking algorithm.
3. The electrocardiosignal compression sampling method based on the random demodulation structure as claimed in claim 2, characterized in that: in step one, the pseudo-random sequence signal p (t) is ± 1 elements distributed randomly, and the frequency of element jump is higher than twice of the highest frequency distribution of the electrocardiosignal x (t).
4. The electrocardiosignal compression sampling method based on the random demodulation structure as claimed in claim 2, characterized in that: in the second step, the time domain form of the mixing of the pseudo-random sequence signal p (t) and the electrocardiosignal x (t) is: y is1(t)＝X(t)×P(t)；
In step three, the unit impulse response of the low-pass filter is h (t), and the low-pass filtering of the mixing signal is equivalent to Y1(t) convolved with the unit impulse response h (t) in the frequency domain:
5. the electrocardiosignal compression sampling method based on the random demodulation structure as claimed in claim 2, characterized in that: in step four, the mth observation vector y (m) is:
wherein, TSIn order to be the sampling interval of the sample,
is the sampling frequency.
6. The electrocardiosignal compression sampling method based on the random demodulation structure as claimed in claim 2, characterized in that: in the fifth step, the accurate reconstruction of the electric signal by the orthogonal matching pursuit algorithm comprises the following steps:
s1, sampling by an AD sampler to obtain unsigned binary data, and converting the unsigned binary data into an observed value vector Y (m) in a decimal form according to AD characteristics;
s2, assuming that the element distribution sequence in the pseudo-random sequence P (t) is P (1), P (2.) and P (N), mixing the pseudo-random sequence and a source signal by a mixer, and constructing a diagonal matrix P according to the multiplication process as follows:
s3, discretizing the unit impulse response H (t) of the analog filter by using a bilinear transformation method to obtain a vector H (n), wherein the discretization frequency is consistent with the hopping frequency of the pseudorandom sequence P (t), and assuming that the element sequence in H (n) is H (1) and H (2).
S4, constructing an observation matrix
7. A random demodulation structure based heart as claimed in claim 6The electric signal compression sampling method is characterized in that: computing a perception matrix
Wherein the sparse matrix psi is an inverse Fourier transform matrix,
is an observation matrix.
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