CN115120242A - Method for extracting signal from noise by active medical instrument - Google Patents
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Abstract
The invention provides a method for extracting signals from noise by active medical equipment, which is characterized in that the signals which are collected by the active medical equipment and are doped with noise do not pass through hardware filtering, the digital quantity signals which are doped with noise are directly sent to a data processing module for processing, and the data processing module identifies effective pulse signals in the input digital quantity signals by using an embedded signal extraction algorithm, so that the waveform and parameters of a bioelectric signal after noise is removed are obtained. On the basis of no need of complex hardware filtering, the invention mainly carries out specific algorithm operation on the signal which is sampled by the AD converter and is mixed with noise from a receiving channel through an embedded software algorithm of an MCU in a circuit, and finally obtains all information parameters of the signal. The invention extracts signals from noise without complex active filtering, thereby greatly simplifying the hardware structure of the circuit, saving the hardware cost and simultaneously enabling the circuit to be more stable.
Description
Technical Field
The invention relates to a method for extracting effective signals by eliminating noise in signals obtained by active medical instruments.
Background
Electrocardio and myoelectric parameters are commonly used monitoring parameters in the medical field, and because related original human body bioelectric signals are very weak, voltage signals obtained through electrodes or transducers are often submerged in noise, as shown in fig. 1, the noise is sensed by a circuit or is generated by components. Since the amplitude of noise is not always smaller than that of a signal, it is difficult to distinguish a signal from noise only according to the magnitude of the amplitude. But noise is not superimposed on the signal during the signal generation phase, which is determined by the characteristics of the circuit and the associated devices themselves. How to better extract the bioelectric signal to be detected from the noise is an important performance index of the medical instrument. Traditional medical monitoring equipment usually realizes making an uproar to fall through various hardware filter circuit, often needs multistage filtering and enlargies, and this makes circuit structure become complicated, is unfavorable for the miniaturization of instrument, has also influenced the reliability of circuit and equipment to the cost has been increased to a certain extent.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the traditional medical monitoring equipment usually realizes noise reduction through various hardware filter circuits, so that the cost is increased, and the reliability of the circuits and the equipment is also influenced.
In order to solve the above technical problems, the technical solution of the present invention is to provide a method for extracting a signal from noise by an active medical device, which is characterized in that a signal acquired by the active medical device and doped with noise is not subjected to hardware filtering, but is amplified by a signal amplifier, and then the amplified signal is sampled by an analog-to-digital converter according to a fixed sampling time interval to form a digital quantity signal doped with noise, the digital quantity signal is directly sent to a data processing module for processing, and the data processing module identifies an effective pulse signal in the input digital quantity signal by using an embedded signal extraction algorithm, so as to obtain a waveform and parameters of a bioelectric signal from which noise is removed, wherein the signal extraction algorithm comprises the following steps:
step 1, initializing a data group number n to 1, timing a sampling moment of the analog-digital converter by using a timer, receiving data of each sampling point in sequence by a data processing module according to a time sequence, and entering step 2;
step 2, initializing a counting variable i to 0;
step 3, the data processing module continuously receives K sampling point data from the current moment as nth data, wherein K is more than or equal to 5;
step 4, the data processing module calculates the accumulated value V (n) of the nth data, and compares the absolute value | V (n) | of V (n) with the threshold value VT: if | V (n) | < VT, go to step 8, if | V (n) | ≧ VT, go to step 5;
step 5, if i is i +1, entering step 6;
step 6, if i is equal to 1, the Kxn-A sampling point is the starting point of the effective pulse signal, the sampling time corresponding to the Kxn-A sampling point is obtained as the starting time point of the effective pulse signal, the signal amplitude value of each sampling point is obtained from the Kxn-A sampling point, and the step 7 is entered, wherein A is an adjusting constant; if i is greater than 1, generating an alarm and exiting from a signal extraction algorithm;
step 7, updating the current time to the current time plus K sampling time intervals, wherein n is n +1, and returning to the step 3;
step 8, if i is equal to 1, the Kxn-A sampling point is an end point of the effective pulse signal, the sampling time corresponding to the Kxn-A sampling point is obtained and is used as an end time point of the effective pulse signal, the acquisition of the signal amplitude value of each sampling point starting from the Kxn-A sampling point is stopped, and the step 9 is entered; if i is 0, jumping to step 7; if i is not equal to 1 and i is not equal to 0, generating an alarm and exiting the signal extraction algorithm;
and 9, updating the current time to the current time plus K sampling time intervals, wherein n is n +1, and returning to the step 2.
Preferably, after the data processing module stores the nth data in the memory, the signal extraction algorithm is used to process the nth data, after the processing is completed, the nth data stored in the memory is covered by the (n + 1) th data, and then the signal extraction algorithm is used to process the (n + 1) th data until the data processing module completes the processing of all digital quantity signals sent from the analog-to-digital converter.
The noise makes the pulses with very short duration, the positive and negative polarities of the pulses are approximately equal and distributed more discretely. The waveform of the signal pulse is continuous, so that under the condition of high enough sampling frequency, a considerable part of the noise is sampled to be zero, the positive polarity and the negative polarity of the sampled noise amplitude are approximately equally distributed, a considerable part can be cancelled by accumulation, and the signal has no problem. Therefore, the signal and the noise can be distinguished from each other based on the accumulated value.
On the basis of no need of complex hardware filtering, the invention mainly carries out specific algorithm operation on the signal which is sampled by the AD converter and is mixed with noise from a receiving channel through an embedded software algorithm of an MCU in a circuit, and finally obtains all information parameters of the signal. The invention extracts signals in noise without complex active filtering, can greatly simplify the hardware structure of the circuit, saves the hardware cost and simultaneously ensures that the circuit is more stable.
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FIG. 1 is a schematic diagram of a bioelectric signal detected by an electrode or transducer of a medical device mixed with noise;
FIG. 2 is a hardware block diagram of the present invention;
FIG. 3 is a flowchart of the MCU embedded program algorithm of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
As can be seen from fig. 1, the pulse width of the random noise is much smaller than that of the effective signal, and the distribution on the time axis is more discrete. If the valid signal and the noise signal are integrated over the same length of time period intercepted, respectively, the integrated value of the valid signal is much larger than the noise. According to this characteristic, the noisy signal shown in fig. 1 is subjected to integration (accumulation) processing in time sequence by taking 10 sampling points as a group, after taking an absolute value for each group of integrated values, the absolute value is compared with a specific threshold value, if the absolute value is smaller than the threshold value, the noisy signal is considered to be noise, then the integration is continued by taking 10 sampling points as a group, the absolute value is compared with the threshold voltage until a time point when the absolute value of the integration is greater than or equal to the threshold value is found, then the sampling value is considered to be a valid signal, meanwhile, the integration operation is continued, when a certain group of integrated values is smaller than the threshold value again, an end time point of the valid signal is found, and then the noise is received subsequently. If a plurality of pulse signals are received, the above operation process is repeated. Thus, the signal can be extracted from the noise more completely.
As shown in FIG. 2, the input end of the received original bioelectric signal is directly connected with an amplifier, and the amplifier simultaneously amplifies the effective signal and the noise signal in the signal. The output end of the amplifier is connected with the input end of the AD converter, and the AD converter carries out AD conversion on the input signal which is mixed with noise, converts the signal into digital quantity and outputs the digital quantity through the digital output port. And the digital output port of the AD converter is connected with the digital input port of the MCU and outputs the converted digital quantity to the MCU. The MCU receives data through the digital input port and calculates input quantity through a specific algorithm. The method comprises the steps that a timer of the MCU starts timing at a starting time point of receiving input quantity, when a calculation result reaches a signal pulse starting point condition, the current time point is set as the signal pulse starting point, then the input digital quantity is used as amplitude values of different time points of a signal pulse, meanwhile, the input digital quantity is continuously calculated according to a specific algorithm, when the calculation result reaches a signal pulse ending condition, the time point at the moment is used as a signal pulse ending time point, and the following digital input quantity is noise.
The specific algorithm adopted by the MCU is shown in fig. 3, and the MCU performs an integration operation on the input digital quantity, which is actually an accumulation operation on the sampling values of the noise-doped signals sampled by the AD converter at fixed time intervals. In this embodiment, each group of data is accumulated for every 10 sampling points according to the sampling time sequence, and the accumulated value of each group is obtained and the absolute value is taken. The obtained absolute value is compared with the threshold value VT. For example, the accumulated value v (n) of the nth data is compared with the threshold value VT after taking the absolute value, and if | v (n) | < VT, the nth data and the previously obtained data are all considered to be noise values. And taking 10 sampling points after the nth group, performing the operation and the comparison, continuing to perform the operation and the comparison if the sampling points are smaller than the threshold value VT until the kth group of data meet | V (K) | or more VT, and considering that the sampling points start from the 10 XK-4 points and the sampling points after the sampling are pulse signals. While continuing the aforementioned 10-bin accumulation and taking the absolute value. For signals, the accumulated value should not be smaller than VT after taking the absolute value until the accumulated value of the Mth group of data, if | V (M) | < VT is satisfied, it is considered to start from the 10 XM-4 th point, and then noise. That is, the values obtained during the time from the 10 XK-4 time point to the 10 XM-4 time point are the amplitude values of the pulse signal. Thus, there are both the start and end time points of the pulse signal and the signal amplitude value at each sampling time point during the pulse signal duration, so that the waveform and parameters of the bioelectric signal can be obtained.
Each set of 10 data values is to be stored. Since the time points at which the start and end of the signal are judged are delayed from the time points at which the signal actually starts and ends according to the aforementioned method. Once it is detected that the pulse signal has started, each data of the set of data starting from the 6 th point is called for in order to determine the amplitude value of the signal at the 5 th time point before the starting time. Similarly, once the pulse signal is detected to have ended, the actual ending point should be the 5 th time point of the group of data, the sampled values at the following 5 time points are all noise values, and the actual signal should be removed from the 5 values. During the storage process, the latter group of 10 data can overwrite the former group of 10 data, that is, only 10 data storage units are needed, and useful data is not lost. The VT is chosen to ensure that the signal and noise are correctly distinguished, depending on the actual situation.
The invention is used for the electrocardio and myoelectricity tester circuit, is used as a module for reducing noise and improving signal to noise ratio, and is realized by describing a hardware structure and a software algorithm.
Claims (2)
1. A method for extracting signals from noise by active medical instruments is characterized in that the signals which are collected by the active medical instruments and are doped with noise do not pass through hardware filtering, but pass through a signal amplifier to amplify the signals, an analog-to-digital converter samples the amplified signals according to a fixed sampling time interval to form digital quantity signals which are doped with noise, the digital quantity signals are directly sent to a data processing module to be processed, and the data processing module identifies effective pulse signals in the input digital quantity signals by using an embedded signal extraction algorithm so as to obtain waveforms and parameters of bioelectric signals after the noise is removed, wherein the signal extraction algorithm comprises the following steps:
step 1, initializing a data group number n to 1, timing a sampling moment of the analog-digital converter by using a timer, receiving data of each sampling point in sequence by a data processing module according to a time sequence, and entering step 2;
step 2, initializing a counting variable i to 0;
step 3, the data processing module continuously receives K sampling point data from the current moment as nth data, wherein K is more than or equal to 5;
step 4, the data processing module calculates the accumulated value V (n) of the nth data, and compares the absolute value | V (n) | of V (n) with the threshold value VT: if | V (n) | < VT, go to step 8, if | V (n) | ≧ VT, go to step 5;
step 5, if i is i +1, entering step 6;
step 6, if i is equal to 1, the Kxn-A sampling point is the starting point of the effective pulse signal, the sampling time corresponding to the Kxn-A sampling point is obtained as the starting time point of the effective pulse signal, the signal amplitude value of each sampling point is obtained from the Kxn-A sampling point, and the step 7 is entered, wherein A is an adjusting constant; if i is greater than 1, generating an alarm and exiting the signal extraction algorithm;
step 7, updating the current time to the current time plus K sampling time intervals, wherein n is n +1, and returning to the step 3;
step 8, if i is equal to 1, the Kxn-A sampling point is an end point of the effective pulse signal, the sampling time corresponding to the Kxn-A sampling point is obtained as an end time point of the effective pulse signal, the signal amplitude value of each sampling point starting from the Kxn-A sampling point is stopped to be obtained, and the step 9 is entered; if i is equal to 0, jumping to step 7; if i is not equal to 1 and i is not equal to 0, generating an alarm and exiting the signal extraction algorithm;
and 9, updating the current time to the current time plus K sampling time intervals, wherein n is n +1, and returning to the step 2.
2. The method of claim 1, wherein the data processing module stores the nth data in the memory, processes the nth data using the signal extraction algorithm, and after the processing, the nth data stored in the memory is overwritten by the (n + 1) th data, and then the signal extraction algorithm processes the (n + 1) th data until the data processing module completes processing all the digital signals transmitted from the analog-to-digital converter.
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