CN112190268A - Physiological signal processing device - Google Patents
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Abstract
The application discloses a physiological signal processing device. The method comprises the steps of identifying characteristic points of physiological signals, judging whether abnormal characteristic points exist according to signal values of the characteristic points and/or the distances between the characteristic points and adjacent characteristic points of the characteristic points, removing abnormal signal segments corresponding to the abnormal characteristic points from the physiological signals if the abnormal characteristic points exist, splicing residual signals in the physiological signals to obtain a first signal, removing abnormal peak or valley segments generated by motion artifacts in the signals according to the characteristics of the physiological signals, removing the influence of the abnormal signal segments, namely effectively eliminating interference signals mixed into data, and improving the effectiveness of the collected physiological signals.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a physiological signal processing device.
Background
Common physiological signals such as electrocardiosignals, heart shock signals, pulse wave signals and the like are easily influenced by a plurality of factors such as human bodies, environments and the like, and have the following characteristics: the amplitude of physiological signals directly detected from a human body is generally small and is millivolt-level signals; because the physiological signal is weak and the human body is a very complex whole, the signal is easily interfered by noise, such as the noise of the sensor, the limb movement accompanying the detection, mental stress and the like; except for sound signals generated by human bodies, the frequencies of other physiological signals are generally lower and are generally between 0.01 and 100 Hz. After the physiological signal is sampled by the amplifying circuit, due to interference existing inside and outside the system and other noise influences, an interference signal can be mixed in the obtained data, so that the interference signal mixed in the data needs to be eliminated to the maximum extent by a certain method to ensure the validity of the collected data.
Digital filtering is an effective method for removing noise, but general digital filtering has a great disadvantage. The motion artifact is one of the main interference sources of the physiological signal, when the motion artifact occurs in the detection process, the abnormal peak or trough is easy to occur in the effective signal, and the traditional digital filtering can only carry out certain inhibition on the noise signal and cannot completely eliminate the interference caused by the noise signal.
Disclosure of Invention
The application provides a physiological signal processing device to solve the technical problem that the interference caused by the motion track cannot be completely eliminated by traditional filtering.
A physiological signal processing device is provided that includes a memory and one or more processors to execute one or more computer programs stored in the memory; the one or more computer programs are stored in the memory; the one or more processors, when executing the one or more computer programs, perform the steps of:
acquiring a physiological signal, wherein the physiological signal is a rhythmic human physiological signal;
identifying feature points of the physiological signal, wherein the feature points comprise peak feature points and valley feature points; acquiring a signal value of the characteristic point and/or a distance between the characteristic point and an adjacent characteristic point of the characteristic point;
judging whether an abnormal characteristic point exists according to the signal value of the characteristic point and/or the distance between the characteristic point and the adjacent characteristic point of the characteristic point;
if the abnormal characteristic points exist, after abnormal signal segments are removed from the physiological signals, residual signals in the physiological signals are spliced to obtain first signals; the abnormal signal segment is a peak segment or a trough segment which contains the abnormal feature point and does not contain a normal feature point, and the normal feature point is a feature point which is not the abnormal feature point.
The technical scheme has the following beneficial effects:
according to the method and the device, the characteristic points of the physiological signals are identified, whether abnormal characteristic points exist is judged according to the signal values of the characteristic points and/or the distances between the characteristic points and the adjacent characteristic points of the characteristic points, if the abnormal characteristic points exist, abnormal signal segments corresponding to the abnormal characteristic points are removed from the physiological signals, then the residual signals in the physiological signals are spliced to obtain the first signals, abnormal peaks or abnormal valley segments generated by motion artifacts in the signals can be removed according to the characteristics of the physiological signals, the influence of the abnormal signal segments can be removed, namely, interference signals mixed into data are effectively eliminated, and the effectiveness of the collected physiological signals is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
Fig. 1 is a hardware structure diagram of a physiological signal processing device according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a signal processing method according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a signal with anomalous spikes according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of a signal processing method according to an embodiment of the present application;
fig. 5 is a schematic diagram illustrating a comparison between the effects of normal filtering and zero-phase filtering provided in the embodiment of the present application;
fig. 6 is a schematic diagram illustrating phase distortion effects of normal filtering and zero-phase filtering according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The embodiments of the present application will be described below with reference to the drawings.
Referring to fig. 1, fig. 1 is a hardware structure diagram of a physiological signal processing device according to an embodiment of the present application, wherein the physiological signal processing device 100 may be any type of electronic device with computing capability, such as: smart phones, computers, palmtop computers, tablet computers, and the like.
Specifically, as shown in fig. 1, the physiological signal processing device 100 includes one or more processors 102 and a memory 104. One processor 102 is illustrated in fig. 1. The processor 102 and the memory 104 may be connected by a bus or other means, such as by a bus in FIG. 1.
The memory 104, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as programs, instructions, and modules corresponding to the signal processing methods. The processor 102 executes various functional applications and data processing of the electronic device by executing non-volatile software programs, instructions, and modules stored in the memory 104.
The memory 104 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the physiological signal processing apparatus, and the like. Further, the memory 104 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 104 optionally includes memory located remotely from the processor 102, which may be connected to the physiological signal processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The physiological signal processing equipment in the embodiment of the invention is used for processing the physiological signal so as to filter out an interference signal in the physiological signal. The memory 104 is used to store a computer implemented program of the signal processing method and the processor 102 is used to read and execute computer readable instructions. In particular, the processor 102 may be configured to invoke a computer implemented program of signal processing methods stored in the memory 104 and execute instructions contained in the computer implemented program to perform method steps related to the signal processing methods. With respect to the method steps of the signal processing method executed by the processor 102, reference may be made to the following descriptions of fig. 2 to fig. 6.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a signal processing method according to an embodiment of the present disclosure. The method can comprise the following steps:
201. acquiring physiological signals, wherein the physiological signals are rhythmic human physiological signals.
The embodiment of the application can process the rhythmic human physiological signals, and the rhythms are the phenomena of regular strength, length, up and down, existence and the like which alternately appear in the motion of some objects. The human body has many physiological rhythmic changes during life activities, and such rhythmic physiological signals are usually periodic or quasi-periodic. The rhythmic human physiological signals may include common physiological signals such as cardiac electrical signals, cardiac shock signals, pulse wave signals, and the like. The physiological signals can be collected by various collection devices, such as medical instruments or wearable devices with human physiological signal collection functions. All rhythmic physiological data can generate abnormal peaks and troughs in the acquired physiological signals due to movement or other reasons, and the effectiveness of the data is influenced. The embodiments of the present application aim to remove such interference using a reasonable algorithm.
202. Identifying characteristic points of the physiological signal, wherein the characteristic points comprise peak characteristic points and trough characteristic points; and acquiring the signal value of the characteristic point and/or the distance between the characteristic point and the adjacent characteristic point of the characteristic point.
For physiological signals, characteristic points, mainly peak and trough characteristic points, can be first identified. The peak mentioned in the embodiments of the present application refers to the maximum value of the amplitude of the wave in one oscillation period, and the relative minimum value is called the trough. The oscillation period refers to a time interval in which the periodic signal changes periodically and repeatedly. The wavelength refers to the distance of wave propagation in one oscillation period, that is, the distance between two points with 2 pi phase difference between two adjacent oscillation phases along the wave propagation direction. Taking transverse waves as an example, the highest point of a protrusion in a waveform of one oscillation period is a peak, and the lowest point of a depression is a trough.
Specifically, signal points higher than a specific threshold value are searched in each oscillation period of the physiological signal to be peak characteristic points, signal points lower than the specific threshold value are valley characteristic points, and the distance between each peak and each valley meets a preset heart rate monitoring range. Optionally, a mode of solving a function extremum by using a difference method may be used to obtain an extremum point in the physiological signal, and the actual characteristic points of the peak and the trough are screened out from the extremum point by combining the aforementioned limiting conditions such as the threshold value and the distance.
In the case where the feature point is identified, a signal value of the feature point and/or a distance between the feature point and an adjacent feature point of the feature point may be acquired, and these data may be information that has been obtained in the above-described procedure of identifying the feature point and is used for determining the feature point.
203. And judging whether an abnormal characteristic point exists or not according to the signal value of the characteristic point and/or the distance between the characteristic point and the adjacent characteristic point of the characteristic point.
In the case of identifying feature points of physiological signals, whether abnormal feature points exist or not can be determined, that is, whether peaks and valleys caused by motion exist or not can be found in the feature points, and the abnormal feature points can be eliminated.
In an optional implementation manner, the step 203 specifically includes:
and if the difference value between the signal value of the first characteristic point and the signal value of the second characteristic point is higher than a preset signal difference threshold value and/or the distance between the first characteristic point and the second characteristic point is higher than a preset distance threshold value, determining that the first characteristic point is the abnormal characteristic point and the second characteristic point is a characteristic point adjacent to the first characteristic point.
Specifically, the first feature point may be any one of the identified physiological signal feature points. Optionally, a difference between the feature point of the physiological signal and the signal value of the adjacent feature point of the feature point may be obtained, and it is determined whether the difference is higher than a preset signal difference threshold, if so, the feature point is determined to be an abnormal feature point, and if not, the feature point is determined to be a non-abnormal feature point. The feature point may have one adjacent feature point or two feature points, and in the case of two adjacent feature points, when at least one of the difference values between the signal values of the two adjacent feature points of the feature point is higher than a preset signal difference threshold, the feature point is determined to be an abnormal feature point; if not, the feature point is a non-abnormal feature point.
Optionally, the distance between the feature point of the physiological signal and its adjacent feature point may be obtained, and it is determined whether the distance is greater than a preset distance threshold, if so, the feature point is determined to be an abnormal feature point, and if not, the feature point is a non-abnormal feature point. The feature point may have one adjacent feature point or two feature points, and in the case of two adjacent feature points, the feature point may be determined to be an abnormal feature point when at least one of the distances between the feature point and the two adjacent feature points of the feature point is higher than a preset distance threshold; if not, the feature point is a non-abnormal feature point.
Through the steps, each feature point can be judged so as to screen out abnormal feature points. Other rules may also be set to screen the feature points to determine abnormal feature points, which is not limited in the embodiments of the present application.
204. If the abnormal characteristic points exist, after abnormal signal segments are removed from the physiological signals, residual signals in the physiological signals are spliced to obtain first signals; the abnormal signal segment is a peak segment or a trough segment that includes the abnormal feature point and does not include a normal feature point, and the normal feature point is a feature point that is not the abnormal feature point.
If the abnormal characteristic points exist in the physiological signals, the abnormal signal segments where the abnormal characteristic points are located can be removed from the physiological signals, and then the residual signals are spliced to obtain the signals (first signals) without the abnormal characteristic points.
The abnormal signal segment is a peak segment or a trough segment which contains the abnormal characteristic point and does not contain the normal characteristic point, and signals within a half wavelength of the abnormal characteristic point can be regarded as the abnormal signal segment. In the embodiment of the present application, a feature point other than the above-mentioned abnormal feature point is referred to as the above-mentioned normal feature point or non-abnormal feature point, and normal feature points (normal peak feature point and normal valley feature point) in the signal need to be retained.
It should be noted that if the abnormal feature point is located at the beginning and end of the physiological signal, only the abnormal signal segment needs to be proposed and splicing is not needed.
In an optional embodiment, after removing the abnormal signal segment from the physiological signal, splicing the remaining signals in the physiological signal to obtain a first signal includes:
if the signal value of the end point of a first signal segment before the abnormal signal segment is the same as the signal value of the start point of a second signal segment after the abnormal signal segment, the signal value of the end point of the first signal segment is used as the signal value of the start point of the second signal segment, and the second signal is connected after the first signal segment to obtain the first signal;
if the signal value of the end point of the first signal segment before the abnormal signal segment is different from the signal value of the start point of the second signal segment after the abnormal signal segment, the end point of the first signal segment is connected with the start point of the second signal segment, and the connection point retains the signal value of the end point of the first signal segment and the signal value of the start point of the second signal segment, so as to obtain the first signal.
The signal splicing method after the abnormal signal segments are removed is described above. In general, the rejected abnormal signal segment is a peak segment or a trough segment, and includes at least one abnormal feature point, and more than one abnormal feature point or abnormal signal segment may exist in one physiological signal. Specifically, an abnormal signal segment in the physiological signal is specifically described, and if the abnormal signal segment has two signal segments which are adjacent to each other in the front and back direction, the two signal segments are spliced, that is, the latter segment is moved and connected behind the former segment. For the joint points of the two signal segments, the joint points are merged into the same point if the signal values are the same, and both the joint points can be reserved if the signal values are different.
Optionally, due to the deleted signal segment, 0 may be complemented after the original signal, so as to keep the signal length unchanged.
For example, see fig. 3 for a schematic diagram of a signal with anomalous spikes. As shown in fig. 3, the signal includes six identified peaks 1-6 and seven troughs 1-7, where an excessive abnormal peak point 3 appears in the signal, the trough feature point 3 can be determined as an abnormal feature point through the foregoing steps, and belongs to a trough abnormality, a trough segment corresponding to the feature point 3 can be removed, two adjacent (2 nd, 3 rd) peaks are spliced, specifically, data after the 3 rd peak is directly connected to the back of the 2 nd peak, the data directly before the two peaks are covered, and 0 can be added to complement the data, where the signal values corresponding to the peaks 2, 3 are different and can both be retained.
According to the method and the device, the characteristic points of the physiological signals are identified, whether abnormal characteristic points exist is judged according to the signal values of the characteristic points and/or the distances between the characteristic points and the adjacent characteristic points of the characteristic points, if the abnormal characteristic points exist, abnormal signal segments corresponding to the abnormal characteristic points are removed from the physiological signals, then the residual signals in the physiological signals are spliced to obtain the first signals, abnormal peaks or abnormal valley segments generated by motion artifacts in the signals can be removed according to the characteristics of the physiological signals, the influence of the abnormal signal segments can be removed, namely, interference signals mixed into data are effectively eliminated, and the effectiveness of the collected physiological signals is improved. The method is suitable for all rhythmic one-dimensional physiological signals, and can also evaluate the signal quality through the identified characteristic points.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating a signal processing method according to an embodiment of the present disclosure. As shown in fig. 4, the method may specifically include:
401. acquiring physiological signals, wherein the physiological signals are rhythmic human physiological signals.
The embodiment of the application can process rhythmic human physiological signals, and the rhythmic human physiological signals can include common physiological signals such as electrocardiosignals, heart shock signals, pulse wave signals and the like. The physiological signals can be collected by various collection devices, such as medical instruments or wearable devices with human physiological signal collection functions.
The step 401 may also refer to the specific description in the embodiment shown in fig. 2, and is not described herein again.
402. Acquiring a threshold value of the physiological signal, wherein the threshold value is an energy average value of the physiological signal; acquiring a search interval of the physiological signal, wherein the starting point of the search interval is the starting point of the physiological signal, and the length of the search interval is less than or equal to one wavelength range.
Specifically, in this embodiment of the present application, an initial feature point may be first found, where the initial feature point is a first peak or trough feature point in a signal. The search interval can be preset according to needs, the starting point of the search interval is the starting point of the physiological signal, and the length of the search interval is usually within a wavelength range, so that the search interval is used for searching the initial characteristic point within a specific signal range. The wavelength refers to the distance of wave propagation in one oscillation period, that is, the distance between two points with 2 pi phase difference between two adjacent oscillation phases along the wave propagation direction. By within a wavelength range is meant a range of distances of one wavelength.
403. And acquiring an initial characteristic point in a search interval of the physiological signal and a signal value of the initial characteristic point, wherein the initial characteristic point is a characteristic point of a first minimum signal value or a maximum signal value in the search interval.
Specifically, a first extreme value may be searched in the search interval, and the extreme value point is determined as the initial feature point, for example, a first minimum value is searched for as a valley value or a first maximum value is searched for as a peak value in the search interval from the start point.
404. And identifying other characteristic points in the physiological signal according to the initial characteristic points and the threshold value so as to obtain all characteristic points of the physiological signal.
Specifically, other peak and valley feature points in the signal may be continuously searched for with reference to a preset threshold and the determined initial feature point. In the subsequent search, the signal value of the initial feature point and the threshold value can be used as the signal value reference standard when determining other feature points, so as to exclude some points suspected to be peaks or troughs.
In an optional implementation manner, the step 404 specifically includes:
acquiring a signal extreme value from a starting point of the search interval to the initial characteristic point, wherein the signal extreme value is a first maximum signal value or a minimum signal value in the search interval, and the polarity of the signal extreme value is opposite to that of the signal value of the initial characteristic point;
judging whether the absolute value of the extreme value of the signal is greater than the threshold value;
if the absolute value of the signal extreme value is larger than the threshold, judging whether the time interval between the signal point corresponding to the signal extreme value and the signal value of the initial characteristic point is in a preset interval range or not;
if so, determining the signal point corresponding to the signal extreme value as a characteristic point;
carrying out differential search by taking the initial characteristic points as starting points to obtain extreme points, and determining the characteristic points of the physiological signals from the extreme points; wherein the absolute value of the signal value of the feature point is greater than the threshold, the difference between the signal value of the feature point and the absolute value of the signal value of the initial feature point is less than a preset difference threshold, and the time interval between the valley feature point and the peak feature point is within the preset interval range.
Specifically, based on the initial feature point, i.e., the valley value, found in step 403, a maximum value is found from the starting point of the signal to the valley value, if the maximum value satisfies the peak threshold, the signal point is determined as the first peak, otherwise, it is determined that there is no peak in the search interval.
Further, subsequent peaks and troughs are searched, a differential search mode may be adopted, the signal value of each peak to be searched needs to satisfy the threshold, and the distance between each peak and each trough satisfies a preset distance range (in the processing of the heart rate signal, a normal heart rate monitoring range may be set).
The principle of the above-mentioned difference finding is to use the second derivative to find the function extremum, but for signal processing, the process is discrete. For example, f is first order conductive in some neighborhood of x0, second order conductive at x0, and f' (x)0)＝0,f″(x0)≠0；
(1) If f' (x)0) If < 0, f is at x0Obtaining a maximum value;
(1) if f' (x)0) > 0, f is at x0Obtaining minimumThe value is obtained.
Specifically, the method for performing the difference search on the signal mainly includes finding a difference function, which can be understood as a difference traversal vector method: the physiological signals (values of the projection curve) are abstracted into a set of one-dimensional vectors, and then the calculation of the first derivative is completed by using the first-order difference vector operation (it can be assumed that the sampling time interval dt is equal to 1 by default). And then, the sign operation is utilized to obtain the positive and negative conditions of the first-order derivative of the signal, and all points with the first-order derivative of 0 are set to be the same as the gradient (trend or trend) of the slope where the points are located. And finally, solving a first-order difference vector of the array, wherein the element is negative and is the peak (maximum) and the element is positive and is the valley (minimum).
One effect of the initial feature point is similar to a threshold value, in that the reliability of the subsequently found feature point is improved, and the preset difference threshold value can be used for comparing the difference between the suspected feature point and the initial feature point. Since the signal point (suspected feature point) found by the way of difference extremum calculation is not necessarily a true feature point, for example, it may be a signal value discontinuity point, by determining whether the difference between the signal value of the suspected feature point and the absolute value of the signal value of the initial feature point is smaller than a preset difference threshold, the signal value relatively close to the initial feature point can be further determined to determine the true feature point.
After all the feature points of the physiological signal are obtained through the above steps, step 405 may be performed.
405. Acquiring a signal value of the characteristic point and/or a distance between the characteristic point and an adjacent characteristic point of the characteristic point; and judging whether an abnormal characteristic point exists or not according to the signal value of the characteristic point and/or the distance between the characteristic point and the adjacent characteristic point of the characteristic point.
406. If the abnormal characteristic points exist, after abnormal signal segments are removed from the physiological signals, residual signals in the physiological signals are spliced to obtain first signals; the abnormal signal segment is a peak segment or a trough segment that includes the abnormal feature point and does not include a normal feature point, and the normal feature point is a feature point that is not the abnormal feature point.
In the case where the feature point is identified, a signal value of the feature point and/or a distance between the feature point and an adjacent feature point of the feature point may be acquired, and these data may be information that has been obtained in the above-described procedure of identifying the feature point and is used for determining the feature point.
The step 405 and the step 406 may refer to specific descriptions in the step 203 and the step 204 in the embodiment shown in fig. 2, and are not described herein again.
407. And performing zero-phase filtering processing on the first signal through a filter to obtain a target signal, wherein no additional phase exists between the target signal and the first signal on a frequency domain.
Specifically, the above steps adopt a feature point recognition algorithm to remove abnormal peaks or valleys generated by motion artifacts, and the processed signals can be subjected to zero-phase filtering.
Digital filtering is an effective method for eliminating noise, but general digital filtering has a great disadvantage, when motion artifacts appear in the detection process, abnormal peaks or troughs easily appear in effective signals, and due to the fact that abnormal frequencies have randomness, the effective signals cannot be processed by a trap filter similar to a trap filter for removing power frequency interference, and the traditional digital filtering can only carry out certain inhibition on the noise signals and cannot completely eliminate the interference caused by the noise signals. In addition, digital filters are designed in various ways, but the digital filters can be roughly divided into IIR filters and FIR filters, the IIR filters can obtain high selectivity with a lower order, and the IIR filters are widely applied to data processing of embedded platforms, and compared with the FIR filters, the IIR filters have the advantages of small memory units, small calculation amount and high efficiency.
The zero-phase filtering method in the embodiment of the application can solve the phase distortion problem of the IIR filter with less hardware overhead.
In an optional implementation manner, the performing zero-phase filtering processing on the first signal by using a filter to obtain a target signal includes:
forward filtering the first signal through the filter to obtain a second signal; then, the second signal is reversely filtered through the filter to obtain a third signal;
and the filter reversely outputs the third signal to obtain the target signal.
In the zero-phase filter, the input signal can be filtered in the forward direction, then the result after the forward filtering is filtered in the reverse direction, and then the obtained result is output in the reverse direction, so that the phase distortion generated by the filter can be eliminated. In an alternative embodiment, the time domain description of the filter includes:
y1(n)＝x(n)*h(n)，Y1(ejw)＝X(ejw)H(ejw)；
y2(n)＝y1(N-1-n)，Y2(ejw)＝e-jw(N-1)Y1(e-jw)；
y3(n)＝y2(n)*h(n)，Y3(ejw)＝Y2(ejw)H(ejw)；
y(n)＝y3(N-1-n)，Y(ejw)＝e-jw(N-1)Y3(e-jw)；
where x (n) represents the input signal sequence, h (n) is the impulse response sequence of the filter, y2(n) is y1(n) first reverse sequence, y3(n) is an intermediate signal sequence obtained by second filtering of the first inverted sequence by the filter, and y (n) is an output signal sequence which is an inverted sequence of the intermediate signal sequence.
The expression of the above formula can obtain: y (e)jw)＝X(ejw)|H(ejw)|2；
Visible, output Y (e)jw) And input X (e)jw) There is no additional phase, so that zero phase filtering is achieved.
To more clearly illustrate the effect of the zero-phase rate wave in the embodiment of the present application, see a diagram for comparing the effect of the normal filtering and the zero-phase filtering shown in fig. 5. The original signal a is an ECG signal, i.e., an Electrocardiogram (ECG) signal. And carrying out common band-pass filtering and zero-phase filtering on the original signal a to respectively obtain a unidirectional filtering signal b and a bidirectional filtering signal c. The band-pass filtering is to eliminate the interference of baseline drift and high-frequency noise. The filtering effect is shown in fig. 5, and it is obvious that the peak of the unidirectional filter b in fig. 5 has a significant displacement (relatively right) compared with the original waveform a, while the positions and forms of the peak and the trough in the bidirectional filter signal c have no significant change compared with the original signal a.
Reference may be made again to fig. 6, which is a schematic diagram illustrating phase distortion effects of a general filtering and a zero-phase filtering. Similar to fig. 5, the original signal a is subjected to ordinary band-pass filtering and zero-phase filtering to obtain a unidirectional filtered signal b and a bidirectional filtered signal c, respectively. Fig. 6 is a schematic diagram of a section of signal after the waveform of the signal is amplified compared with fig. 5, and it can be clearly seen that the peak of the unidirectional filter b has a significant displacement (relatively more right) compared with the original waveform a, and the form of the trough has a certain distortion compared with the original waveform, while the position and form of the peak and the trough in the bidirectional filter signal c have no significant change compared with the original signal a.
In summary, it can be seen that the zero-phase filtering in the embodiment of the present application reduces the phase distortion in the ordinary unidirectional filtering.
In an optional implementation manner, before the step 401, the method further includes:
the method comprises the steps of obtaining a first physiological signal to be processed, processing the first physiological signal to be processed through a notch filter, removing interference caused by preset frequency power frequency, and carrying out median filtering to remove interference caused by discontinuous impact.
In the embodiment of the present application, the whole algorithm flow for removing abnormal peaks or valleys caused by motion artifacts can be summarized as follows:
1. acquiring data: the data herein generally refers to physiological signals with rhythmicity, such as cardiac electrical signals, cardiac shock signals, pulse wave signals, and the like;
2. power frequency trapping: removing the interference generated by 50Hz power frequency;
3. median filtering: removing the interference caused by discontinuous impact;
4. removing motion artifacts: removing interference caused by motion artifacts by adopting a characteristic point identification algorithm;
5. zero-phase filtering: eliminating phase distortion caused by the traditional IIR filtering by using zero-phase filtering;
6. and (3) subsequent calculation: and further analyzing and calculating according to different signal characteristics and purposes.
According to the signal processing method in the embodiment of the application, initial characteristic points are determined through a threshold value and a search interval, all peak and trough characteristic points are further searched out, and whether abnormal characteristic points exist is determined according to signal values of the characteristic points and/or distances between the characteristic points and adjacent characteristic points of the characteristic points; if the abnormal signal segment exists, the abnormal signal segment corresponding to the abnormal characteristic point is removed from the physiological signal, and then the residual signals in the physiological signal are spliced to obtain a first signal, so that abnormal peak or valley segments generated by motion artifacts in the signal can be removed according to the characteristics of the physiological signal, the influence of the abnormal signal segment can be removed, and the interference signal mixed in the data can be more effectively eliminated; and then, through zero-phase filtering processing, phase distortion caused by the traditional IIR filtering is eliminated, and the effectiveness of the acquired physiological signals is improved. The method is suitable for all rhythmic one-dimensional physiological signals, can evaluate the signal quality through the identified characteristic points, and is convenient for further analysis and calculation aiming at different signal characteristics and purposes.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)), or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a read-only memory (ROM), or a Random Access Memory (RAM), or a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape, a magnetic disk, or an optical medium, such as a Digital Versatile Disk (DVD), or a semiconductor medium, such as a Solid State Disk (SSD).
Claims (8)
1. A physiological signal processing device comprising a memory and one or more processors to execute one or more computer programs stored in the memory; the one or more computer programs are stored in the memory; the one or more processors, when executing the one or more computer programs, perform the steps of:
acquiring a physiological signal, wherein the physiological signal is a rhythmic human physiological signal;
identifying feature points of the physiological signal, wherein the feature points comprise peak feature points and valley feature points; acquiring a signal value of the characteristic point and/or a distance between the characteristic point and an adjacent characteristic point of the characteristic point;
judging whether abnormal characteristic points exist in the physiological signals or not according to the signal values of the characteristic points and/or the distances between the characteristic points and the adjacent characteristic points of the characteristic points;
if the abnormal characteristic points exist, after abnormal signal segments are removed from the physiological signals, residual signals in the physiological signals are spliced to obtain first signals; the abnormal signal segment is a peak segment or a trough segment which contains the abnormal feature point and does not contain a normal feature point, and the normal feature point is a feature point which is not the abnormal feature point.
2. The device of claim 1, wherein the processor, prior to performing the identifying the characteristic points of the physiological signal, is further configured to perform the steps of:
acquiring a threshold value of the physiological signal, wherein the threshold value is an energy average value of the physiological signal;
the processor specifically executes the following steps in the process of identifying the characteristic points of the physiological signal:
acquiring a search interval of the physiological signal, wherein the starting point of the search interval is the starting point of the physiological signal, and the length of the search interval is less than or equal to a wavelength range;
acquiring initial characteristic points in a search interval of the physiological signals, wherein the initial characteristic points are characteristic points of a first minimum signal value or a maximum signal value in the search interval;
and identifying other characteristic points in the physiological signal according to the initial characteristic points and the threshold value so as to obtain all characteristic points of the physiological signal.
3. The device according to claim 2, wherein the processor performs the following steps in the process of performing the step of obtaining all feature points of the physiological signal by identifying other feature points in the physiological signal according to the initial feature point and the threshold value:
acquiring a signal extreme value from a starting point of the search interval to the initial characteristic point, wherein the signal extreme value is a first maximum signal value or a minimum signal value in the search interval, and the polarity of the signal extreme value is opposite to that of the signal value of the initial characteristic point;
judging whether the absolute value of the signal extreme value is greater than the threshold value;
if the absolute value of the signal extreme value is larger than the threshold, judging whether the time interval between the signal point corresponding to the signal extreme value and the initial characteristic point is in a preset interval range;
if so, determining the signal point corresponding to the signal extreme value as a characteristic point;
carrying out differential search by taking the initial characteristic points as starting points to obtain extreme points, and determining the characteristic points of the physiological signals from the extreme points; wherein an absolute value of the signal value of the feature point is greater than the threshold, a difference between the absolute value of the signal value of the feature point and the absolute value of the signal value of the initial feature point is less than a preset difference threshold, and a time interval between the valley feature point and the peak feature point adjacent to the valley feature point is within the preset interval range.
4. The device according to any one of claims 1 to 3, wherein the processor, in executing the step of determining whether there is an abnormal feature point according to the signal value of the feature point and/or the distance between the feature point and the adjacent feature point of the feature point, specifically executes the following steps:
and if the difference value between the signal value of the first characteristic point and the signal value of the second characteristic point is higher than a preset signal difference threshold value, and/or the distance between the first characteristic point and the second characteristic point is higher than a preset distance threshold value, determining that the first characteristic point is the abnormal characteristic point, and the second characteristic point is a characteristic point adjacent to the first characteristic point.
5. The apparatus according to claim 1, wherein the processor performs the following steps in the process of performing the step of splicing the remaining signals in the physiological signals to obtain the first signal after removing the abnormal signal segments from the physiological signals:
if the signal value of the end point of a first signal segment before the abnormal signal segment is the same as the signal value of the start point of a second signal segment after the abnormal signal segment, taking the signal value of the end point of the first signal segment as the signal value of the start point of the second signal segment, and connecting the second signal after the first signal segment to obtain the first signal;
and if the signal value of the end point of the first signal segment before the abnormal signal segment is different from the signal value of the start point of the second signal segment after the abnormal signal segment, connecting the end point of the first signal segment with the start point of the second signal segment, and reserving the signal value of the end point of the first signal segment and the signal value of the start point of the second signal segment by a connecting point to obtain the first signal.
6. The device of claim 5, wherein the processor, after performing the splicing of the remaining signals of the physiological signals to obtain the first signal, is further configured to perform the steps of:
and carrying out zero-phase filtering processing on the first signal through a filter to obtain a target signal, wherein no additional phase exists between the target signal and the first signal on a frequency domain.
7. The apparatus according to claim 6, wherein the processor performs the following steps in the process of performing the step of performing zero-phase filtering processing on the first signal by the filter to obtain the target signal:
forward filtering the first signal through the filter to obtain a second signal; then, the second signal is reversely filtered through the filter to obtain a third signal;
and the filter reversely outputs the third signal to obtain the target signal.
8. The device of any one of claims 5-7, wherein the processor, prior to performing the acquiring the physiological signal, is further configured to perform the steps of:
acquiring a first physiological signal to be processed, processing the first physiological signal to be processed through a notch filter, removing a signal with a preset frequency in the first physiological signal to be processed, and acquiring a second physiological signal to be processed;
and performing median filtering processing on the second physiological signal to be processed, and adjusting the signal value of each signal point in the second physiological signal to be processed to be the median of the signal values of all signal points in one neighborhood of the signal point to obtain the physiological signal.
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| CN112869765A (en) * | 2021-01-21 | 2021-06-01 | 宁波理得医疗科技有限公司 | Fetal heart rate calculation method and wearable heart-lung sound wireless monitoring system |
| CN112924519A (en) * | 2021-01-26 | 2021-06-08 | 南京腾森分析仪器有限公司 | Automatic peak-valley searching method, device, medium and electronic equipment |
| CN113317757A (en) * | 2021-04-30 | 2021-08-31 | 深圳麦格米特电气股份有限公司 | Method for acquiring vital sign data, optical fiber sensor and equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112869765A (en) * | 2021-01-21 | 2021-06-01 | 宁波理得医疗科技有限公司 | Fetal heart rate calculation method and wearable heart-lung sound wireless monitoring system |
| CN112924519A (en) * | 2021-01-26 | 2021-06-08 | 南京腾森分析仪器有限公司 | Automatic peak-valley searching method, device, medium and electronic equipment |
| CN112924519B (en) * | 2021-01-26 | 2023-04-28 | 南京腾森分析仪器有限公司 | Automatic peak and valley searching method and device, medium and electronic equipment |
| CN113317757A (en) * | 2021-04-30 | 2021-08-31 | 深圳麦格米特电气股份有限公司 | Method for acquiring vital sign data, optical fiber sensor and equipment |
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Application publication date: 20210108 |