A method for identifying arterial blood pressure waveform signal artifacts and evaluating signal quality

Abstract

The present invention provides a method for identifying arterial waveform signal artifacts of blood pressure, which can quickly and accurately identify arterial waveform signal artifacts in blood arterial waveform signal, and the result is accurate and not affected by personal factors of the analyst. The technical solution of the present application determines the scattering degree Sp of the arterial peak interval of the arterial waveform to be analyzed by the Lorenz scatter plot, and the irregular distribution abnormality of the arterial waveform peak on the time axis is judged; by finding the effective area of ​​the arterial waveform to be analyzed, the arterial waveform data to be analyzed with abnormal peak points and valley points is judged as an interference signal; by the mirror data of the data of the effective area of ​​the arterial waveform to be analyzed, the peak amplitude disturbance rate PError of the arterial waveform to be analyzed is calculated and determined, and the proportion of the number of abnormal peak points in the arterial waveform to be analyzed is determined; finally, the arterial waveform interference data is determined by the two conditions of Sp and PError. At the same time, the present invention also discloses a method for evaluating the quality of blood pressure arterial waveform signal.

Description

Technical Field

[0001] The present invention relates to the field of medical equipment and artificial intelligence technology, and in particular to a method for identifying arterial error of a blood pressure arterial waveform signal and evaluating signal quality. Background Art

[0002] An ambulatory blood pressure monitor is a medical instrument that measures the patient's blood pressure regularly in daily life. With the current development of mobile Internet, the combination of ambulatory blood pressure monitors and cloud service technologies has gradually realized home monitoring of cardiovascular diseases. However, at the same time, home monitoring of cardiovascular diseases brings a large amount of blood pressure data that needs to be diagnosed in time, which brings challenges to the timeliness of doctors' analysis services. Most of the electronic sphygmomanometers currently in use use the oscillometric principle. The inflation and deflation process of blood pressure measurement is easily affected by internal and external factors such as jitter, exercise conditions, and arrhythmias, resulting in abnormal arterial waveforms and invalid data. When analyzing the blood pressure arterial waveform signal, doctors need to manually identify the artifact signal; at the same time, they also need to judge the quality of the blood pressure arterial waveform signal. If there are too many artifact signals, it means that the waveform signal cannot express the accurate meaning, and the signal quality of this segment is poor and needs to be eliminated. This manual analysis process in massive data takes a long time, which greatly reduces the efficiency of doctors' analysis of blood pressure arterial waveform signals. At the same time, the manual analysis process requires high experience and physical strength of analysts, and the probability of error is high. Summary of the invention

[0003] In order to solve the problem that it takes a long time and is prone to errors to manually identify the artifact signals in the massive blood arterial waveform signals and judge the signal quality in the prior art, the present invention provides a blood pressure arterial waveform signal artifact recognition method, which can quickly and accurately identify the artifact signals in the blood arterial waveform signals, and the results are accurate and not affected by the personal factors of the analyst. At the same time, the present invention also discloses a blood pressure arterial waveform signal quality assessment method.

[0004] The technical solution of the present invention is as follows: a method for identifying arterial blood pressure waveform signal artifacts, characterized in that it comprises the following steps:

[0005] S1: Obtain the arterial waveform to be analyzed;

[0006] The horizontal axis of the arterial waveform diagram to be analyzed is the pulse pulsation position caused by the heartbeat, and the vertical axis is the pressure value collected by the dynamic blood pressure meter pressure sensor;

[0007] S2: marking the peaks and valleys of the arterial waveform to be analyzed;

[0008] Based on the arterial waveform to be analyzed, find all peak points and valley points; take the first peak point as the starting point and pair it with the adjacent valley point;

[0009] Assume that the arterial waveform to be analyzed includes N pairs of peak points and valley points, then the peak points and valley points included in the arterial waveform to be analyzed are expressed as:

[0010] (Peak1, Valley1), (Peak2, Valley2),..., (PeakN, ValleyN);

[0011] The horizontal coordinates of the peak points Peak1-PeakN form the sequence x1,x2,...x N ;

[0012] S3: Obtain the ordinates of each pair of peak points and valley points, and calculate the difference between the ordinates of each pair of peak points and valley points;

[0013] Then we get the sequence of the absolute value of the difference between the ordinates of each pair of peak points and valley points:

[0014] Recorded as difference sequence: h1,h2,....,h N ;

[0015] S4: Draw a Lorenz scatter plot. The specific steps are as follows:

[0016] a1: calculate the sequence x1,x2,...x N The difference sequence is recorded as Δ1=x2-x1, Δ2=x3-x2, ..., Δ N-1 =x N -x N-1 ;

[0017] △ i Represents the pulse interval or heartbeat interval, i is a natural number, i∈(1,N-1);

[0018] a2: The adjacent △ i Combine into pairs to form (△ i ,△ i+1 );

[0019] a3: Construct the coordinate axis, the units of the vertical and horizontal axes are both in ms;

[0020] Each group (△ i ,△ i+1 ) i is the horizontal axis, △ i+1 As the vertical axis, (△ i ,△ i+1 ) is plotted on the coordinate axis to obtain a Lorenz scatter plot;

[0021] S5: The dispersion degree Sp of the arterial waveform to be analyzed specifically comprises the following steps:

[0022] b1: construct a mapping grid;

[0023] Set the heartbeat interval threshold and reasonable interval threshold;

[0024] The heartbeat interval threshold represents a reasonable maximum value of the heartbeat interval data;

[0025] The reasonable interval threshold means that when the difference between two heartbeat intervals of the same data source is less than the reasonable interval threshold, the two heartbeat intervals are regarded as the same data during calculation;

[0026] The mapping grid includes: a mapping coordinate system and a grid;

[0027] The horizontal axis and the vertical axis of the mapping coordinate system are both set to the heartbeat interval, the starting point of the horizontal axis and the vertical axis is 0, and the maximum value is the heartbeat interval threshold;

[0028] Between the horizontal axis and the vertical axis, construct the grid in units of the interval allowable value;

[0029] b2: Take out each group (△ i ,△ i+1 ), judge △ i Relationship with heartbeat interval threshold;

[0030] If i >the heartbeat interval threshold, then (△ i ,△ i+1 ) does not participate in the calculation;

[0031] Otherwise, judge △ i+1 Relationship with heartbeat interval threshold;

[0032] If i+1 >the heartbeat interval threshold, then (△ i ,△ i+1 ) does not participate in the calculation;

[0033] Otherwise, execute step b3;

[0034] b3:(△ i ,△ i+1 ) i is the horizontal axis, △ i+1 The vertical coordinate is mapped into the grid of the mapping grid map;

[0035] b4:When all (△ i ,△ i+1 ) are all involved in the calculation, and the number of successfully mapped grids in the mapping grid map is counted;

[0036] Assuming that n grids in the mapping grid map are mapped successfully, calculate the scatter Sp of the arterial waveform map to be analyzed;

[0037] Sp = n / (N-1);

[0038] S6: Finding the effective area of ​​the arterial waveform to be analyzed, specifically comprising the following steps:

[0039] c1: the difference sequence in the sequence: h1,h2,....,h N Find the largest value and record it as: h Max ;

[0040] c2: Initialization number j = 1,

[0041] c3: h j 、h j+1 、h j+2 Respectively with C1*h Max Make comparisons;

[0042] If there is h j 、h j+1 、h j+2 Both are greater than C1*h Max , then h j Set to Pstart and execute step c6;

[0043] Otherwise, execute step c4;

[0044] c4: Determine whether j is greater than Max-2;

[0045] If j>Max-2, the arterial waveform data to be analyzed corresponding to the difference sequence is judged as pseudo-error interference data;

[0046] Otherwise, execute step c5;

[0047] c5: j=j+1, and steps c3 to c4 are executed repeatedly;

[0048] c6: Determine the relationship between j and N;

[0049] If j>N-2, the arterial waveform data to be analyzed corresponding to the difference sequence is judged as pseudo-error interference data;

[0050] Otherwise, execute step c7;

[0051] c7: j = j + 1;

[0052] c8: h j 、h j+1 、h j+2 Respectively with C1*h Max Make comparisons;

[0053] Among them, C1 is the effective peak-to-valley difference threshold;

[0054] If there is h j 、h j+1 、h j+2 Both are greater than C1*h Max , then h j+2 Set to Pend and execute step c10;

[0055] Otherwise, execute step c9;

[0056] c9: Determine whether j is greater than N-2;

[0057] If j>N-2, the arterial waveform data to be analyzed corresponding to the difference sequence is determined as an interference signal;

[0058] Otherwise, j=j+1, and steps c8 to c9 are executed repeatedly;

[0059] c10: the difference sequence: h1,h2,....,h N In the figure, the data points between Pstart and Pend are considered as the valid data area of ​​the arterial waveform to be analyzed;

[0060] S7: performing data fitting on the valid data region, specifically comprising the following steps:

[0061] d1: Take out the data included in Pstart~Pend from the difference sequence and record it as the valid data sequence,

[0062] The valid data sequence is: h Pstart ,h Pstart+1 ....h max .....h Pend-1 ,h Pend ;

[0063] d2: remove h max. Data on the left: h Pstart ,h Pstart+1 ....h max-1 , with h max. Mirror the center to get the fitted data sequence on the left: h Pstart ,h Pstart+1 ....h max-1 ,h max ,h max-1 ....h Pstart+1 ,h Pstart ;

[0064] The horizontal coordinate corresponding to the left fitting data sequence is: Pstart ,xPstart+1 ....x max-1 , x max ,x max- 1....x Pstart ;

[0065] d3: Remove h max. Data on the right: h max+1 .....h Pend-1 ,h Pend , with h max. Mirror the center to get the fitting data sequence on the right: h Pend ,h Pend-1 ....h max+1 ,h max ,h max+1 .....h Pend-1 ,h Pend ;

[0066] The horizontal coordinate corresponding to the right-side fitting data sequence is: Pend ,x Pend-1 ....x max+1 , x max ,x max+ 1....x Pend-1 ,x Pend ;

[0067] S8: Calculate the peak amplitude disturbance rate PError corresponding to the arterial waveform to be analyzed;

[0068] e1: Calculate the number of interference points on the left ErrorLeft;

[0069] The left fitting data sequence and the horizontal coordinates corresponding to the left fitting data sequence are used as the vertical coordinates and horizontal coordinates of the data points, and the quadratic fitting curve h=ax is obtained using the least squares method. 2 +bx+c;

[0070] The distance between the ordinate of the valid data point on the left and the fitting curve is greater than C2*h Max The number of points is recorded as the number of left interference points ErrorLeft;

[0071] Among them, C2 is the interference point judgment threshold;

[0072] e2: Calculate the number of interference points on the right ErrorRight;

[0073] The quadratic fitting curve h=ax is obtained by using the least squares method, with the right-side fitting data sequence and the horizontal coordinates corresponding to the right-side fitting data sequence as the vertical coordinates and horizontal coordinates of the data points. 2 +bx+c;

[0074] The distance between the vertical coordinate of the valid data point on the right and the fitting curve is greater than C2*h Max The number of points is recorded as the number of right interference points ErrorRight;

[0075] e3: Calculate the peak amplitude disturbance rate PError;

[0076] PError=(ErrorRight+ErrorLeft) / (Pend-Pstart+1);

[0077] S9: Determine the property of the arterial waveform to be analyzed;

[0078] f1: comparing the dispersion degree Sp of the arterial waveform to be analyzed with a preset dispersion degree threshold value C3;

[0079] f2: comparing the peak amplitude disturbance rate PError with a preset disturbance rate threshold C4;

[0080] f3: When the following two conditions are met at the same time, the arterial waveform to be analyzed is determined to be pseudo-error interference data;

[0081] Sp>C3, and PError>C4.

[0082] It is further characterized by:

[0083] In step S1, the horizontal axis of the arterial waveform to be analyzed is the pulse pulsation position caused by the heartbeat, which is expressed in the number of sampling points or sampling time;

[0084] The heartbeat interval threshold is set to 2000ms; the reasonable interval threshold is set to 100ms;

[0085] In step b3, when the horizontal axis of the arterial waveform diagram to be analyzed is represented by the number of sampling points, and the unit of the grid of the mapping grid diagram is ms,

[0086] For (Δ i ,Δ i+1 ), calculate RR respectively i and RR i+1 :

[0087]

[0088]

[0089] RR i and RR i+1 As the horizontal and vertical coordinates, (△ i ,△ i+1) is mapped to the grid of the mapping grid diagram; SAMPLE is the sampling rate corresponding to the arterial waveform diagram to be analyzed;

[0090] The effective peak-to-valley difference threshold C1 is 1 / 3; the interference point judgment threshold C2 is 1 / 8;

[0091] The scatter threshold C3 is 30%; the disturbance rate threshold C4 is 20%;

[0092] In step c1, if h Max If the number is greater than 1, the first maximum point is used for subsequent calculations.

[0093] A method for evaluating the quality of a blood pressure arterial waveform signal, comprising the following steps:

[0094] g1: Get the arterial waveform to be analyzed;

[0095] g2: Calculate the dispersion Sp and the peak amplitude disturbance rate PError corresponding to the obtained arterial waveform to be analyzed;

[0096] g3: judging whether the arterial waveform to be analyzed is pseudo-error interference data according to the scatter Sp and the peak amplitude disturbance rate PError;

[0097] If it is artifact interference data, the quality of the arterial waveform to be analyzed is set as data with poor indicators;

[0098] Otherwise, execute step g4;

[0099] g4: Calculate the treatment evaluation parameter Qua of the arterial waveform to be analyzed:

[0100] Qua=SP*α+PError*β;

[0101] Among them, α is the weight coefficient of the scatter degree, and β is the weight coefficient of the disturbance rate;

[0102] g5: Compare Qua with the preset quality judgment threshold QH;

[0103] When Qua is less than QH, the quality of the data corresponding to the arterial waveform graph to be analyzed is judged to be data of good quality; otherwise, the quality of the data corresponding to the arterial waveform graph to be analyzed is judged to be data of poor quality.

[0104] It is further characterized by:

[0105] The value of the dispersion weight coefficient α is 25%; the value of the disturbance rate weight coefficient β is 75%; and the value of the quality judgment threshold QH is 20%.

[0106] The present invention provides a method for identifying arterial waveform signal artifacts of blood pressure. The method comprises the following steps: judging the scattering degree Sp of the arterial peak interval of the arterial waveform to be analyzed by using a Lorenz scatter plot, and judging the irregular distribution abnormality of the arterial waveform peak on the time axis; judging the arterial waveform data to be analyzed with abnormal peaks and valleys as interference signals by searching the effective area of ​​the arterial waveform to be analyzed; obtaining a quadratic fitting curve based on the least squares method through the mirror data of the data in the effective area of ​​the arterial waveform to be analyzed, and then calculating and judging the peak amplitude disturbance rate PError of the arterial waveform to be analyzed, and determining the peak points and valleys of the arterial waveform to be analyzed. The pressure difference of the points is judged to determine the proportion of abnormal peak points in the arterial waveform to be analyzed; finally, the pseudo-error interference data is determined by the two conditions of Sp and PError; when calculating Sp, the Lorenz scatter plot is mapped to the mapping grid by constructing a mapping grid map, and the scattering degree Sp is obtained by calculating the area hit by the data points in the mapping grid map. There is no need to judge each data separately, which greatly improves the calculation efficiency; at the same time, when drawing the mapping grid map, the data accuracy in the calculation process of the scattering degree Sp is adjusted by a reasonable interval threshold. The method is simple and easy to implement, which further improves the calculation efficiency. The entire process of this application is based on the actual data of the arterial waveform to be analyzed, and is not affected by the personal factors of the analyst. It ensures the rapid and accurate identification of pseudo-error signals in the blood arterial waveform signal, and is especially suitable for the recognition of blood pressure arterial waveform signals in massive data, which greatly improves the pseudo-error recognition efficiency of blood pressure arterial waveform signals. At the same time, in the blood pressure arterial waveform signal quality assessment method provided by the present application, based on Sp and PError data, the data quality of the arterial waveform to be analyzed is judged from two angles: the degree of scattering of arterial peak intervals and the proportion of abnormal peak points. Quantitative data is used at the same time, without the need for personal subjective judgment by technicians, and the results are accurate and objective. At the same time, the quality judgment standard is adjusted according to the preset quality judgment threshold QH, so that the quality assessment of the present application is suitable for various application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 This is a flow chart of the method for identifying arterial blood pressure waveform signal artifacts in this application;

[0108] Figure 2 An embodiment of a blood pressure arterial waveform diagram;

[0109] Figure 3 An embodiment of the peak and valley points of a blood pressure arterial waveform diagram;

[0110] Figure 4 An embodiment of a mapping grid diagram;

[0111] Figure 5 An embodiment of a valid region of a blood pressure arterial waveform diagram;

[0112] Figure 6 is an example of a quadratic fitting curve. DETAILED DESCRIPTION

[0113] like Figure 1 As shown, the present invention provides a method for identifying arterial blood pressure waveform signal artifacts, which includes the following steps.

[0114] S1: Obtain the arterial waveform to be analyzed;

[0115] like Figure 2 As shown, it is an embodiment of a blood pressure arterial waveform graph as an arterial waveform graph to be analyzed, the horizontal axis of the arterial waveform graph to be analyzed is the pulse pulsation position caused by the heartbeat, and the vertical axis is the pressure value collected by the dynamic blood pressure meter pressure sensor. In actual work, the horizontal axis of the arterial waveform graph to be analyzed is the pulse pulsation position caused by the heartbeat, which is expressed in the number of sampling points or sampling time; the sampling rate of the arterial waveform graph to be analyzed is SAMPLE, and the sampling rate SAMPLE defines the number of samples extracted from the continuous signal per second and composed of discrete signals, in Hertz (Hz).

[0116] S2: Mark the peaks and valleys of the arterial waveform to be analyzed;

[0117] Based on the arterial waveform to be analyzed, find all peak points and valley points; starting from the first peak point, pair it with the adjacent valley points;

[0118] Assume that the arterial waveform to be analyzed includes N pairs of peak points and valley points. Then the peak points and valley points included in the arterial waveform to be analyzed are expressed as:

[0119] (Peak1, Valley1), (Peak2, Valley2),..., (PeakN, ValleyN);

[0120] The horizontal coordinates of the peak points Peak1-PeakN form the sequence x1,x2,...x N .

[0121] Regarding the method for confirming the peak and valley points of the arterial waveform, it can be completed based on existing technologies, such as the oscillometric blood pressure measurement algorithm, and the obtained (Peak1, Valley1) is as follows: Figure 3 shown.

[0122] S3: Obtain the ordinates of each pair of peak points and valley points, and calculate the difference between the ordinates of each pair of peak points and valley points;

[0123] Then we get the sequence of the absolute value of the difference between the ordinates of each pair of peak points and valley points:

[0124] Recorded as difference sequence: h1,h2,....,hN .

[0125] S4: draw Lorenz scatter plot;

[0126] a1: calculate the sequence x1,x2,...x N The difference sequence is recorded as Δ1=x2-x1, Δ2=x3-x2, ..., Δ N-1 =x N -x N-1 ;

[0127] △ i Represents the pulse interval or heartbeat interval, i is a natural number, i∈(1,N-1);

[0128] a2: The adjacent △ i Combine into pairs to form (△ i ,△ i+1 );

[0129] a3: Construct the coordinate axis, the units of the vertical and horizontal axes are both in ms;

[0130] Each group (△ i ,△ i+1 ) i is the horizontal axis, △ i+1 As the vertical axis, (△ i ,△ i+1 ) is plotted on the coordinate axis to obtain a Lorenz scatter plot;

[0131] The degree of distribution disorder of the Lorenz scatter plot is used to determine whether it is a serious rhythm disorder or interference. In this application, the degree of distribution disorder based on the Lorenz scatter plot is used as one of the evaluation bases for signal quality.

[0132] S5: The dispersion degree Sp of the arterial waveform to be analyzed specifically includes the following steps:

[0133] b1: construct a mapping grid;

[0134] Set the heartbeat interval threshold and reasonable interval threshold;

[0135] The heartbeat interval threshold represents the maximum value of reasonable heartbeat interval data;

[0136] The reasonable interval threshold means that when the difference between two heartbeat intervals of the same data source is less than the reasonable interval threshold, the two heartbeat intervals are regarded as the same data during calculation;

[0137] The mapping grid includes: mapping coordinate system and grid;

[0138] The horizontal and vertical axes of the mapping coordinate system are both set to the heartbeat interval, with the starting point of the horizontal and vertical axes being 0 and the maximum value being the heartbeat interval threshold;

[0139] A grid is constructed between the horizontal and vertical axes with the interval allowed value as the unit.

[0140] Because, in actual work, the horizontal axis of the arterial waveform to be analyzed is the position of the pulse pulsation caused by the heartbeat, which can be expressed by the number of sampling points or the sampling time, so the units of the heartbeat interval threshold and the reasonable interval threshold can be the sampling time or the number of sampling points; the specific values ​​of the heartbeat interval threshold and the reasonable interval threshold are set according to research needs. In this embodiment, in most analyses of arterial waveforms, the corresponding maximum value of the human heartbeat interval is 2000s, so the heartbeat interval threshold is set to 2000ms; and in normal research, when the difference between two heartbeat intervals is no more than 100ms, they are processed as the same trend data and can be mapped in the same grid. In this embodiment, the reasonable interval threshold is set to 100ms, and the constructed mapping grid diagram is as follows: Figure 4 As shown; Figure 4 The portion between 1000ms and 2000ms is omitted.

[0141] b2: Take out each group (△ i ,△ i+1 ), judge △ i Relationship with heartbeat interval threshold;

[0142] If i >heartbeat interval threshold, then (△ i ,△ i+1 ) does not participate in the calculation;

[0143] Otherwise, judge △ i+1 Relationship with heartbeat interval threshold;

[0144] If i+1 >heartbeat interval threshold, then (△ i ,△ i+1 ) does not participate in the calculation;

[0145] Otherwise, execute step b3;

[0146] b3:(△ i ,△ i+1 ) i is the horizontal axis, △ i+1 The vertical coordinate is mapped to the grid of the mapping grid map;

[0147] b4:When all (△ i ,△ i+1 ) are all involved in the calculation, and the number of successfully mapped grids in the mapping grid map is counted;

[0148] Assume that n grids are mapped successfully in the mapping grid diagram, and calculate the scatter Sp of the arterial waveform diagram to be analyzed;

[0149] Sp = n / (N-1).

[0150] In specific implementation, when the coordinate units of the horizontal and vertical axes of the constructed mapping grid are the same as the horizontal axis units of the arterial waveform to be analyzed, the Lorenz scatter plot corresponding to the arterial waveform to be analyzed can be directly mapped to the mapping grid. If the two are not consistent, the units need to be converted before mapping. The value range of Sp is When SP is When , it means all heartbeat intervals △ i All are mapped in the same grid, indicating that there are no abnormalities in the data source and data acquisition process, and no pseudo-error signals are generated; when SP is When , it means that all heartbeat intervals are different, the dispersion Sp of the arterial waveform to be analyzed is the largest, and this segment of data includes a large number of artifact signals.

[0151] For example, in step b3, when the horizontal axis of the arterial waveform to be analyzed is expressed in the number of sampling points, and the unit of the grid of the mapping grid is ms, it is necessary to first i ,△ i+1 ) Calculate RR i and RR i+1 :

[0152]

[0153]

[0154] RR i and RR i+1 As the horizontal and vertical coordinates, (△ i ,△ i+1 ) is mapped into the grid of the mapping grid map;

[0155] For example, round △1 / (0.1*SAMPLE) down to the nearest integer, record it as RR1, round △2 / (0.1*SAMPLE) down to the nearest integer, record it as RR2, and map (RR1, RR2) to a mapping grid map with a unit of ms.

[0156] S6: Find the effective area of ​​the arterial waveform to be analyzed, when the difference sequence is: h1,h2,....,h N The largest value is h Max ; In the difference sequence, find a continuous data segment Pstart~Pend, where Pstart and the two continuous data on the right are both greater than C1*h Max, Pend and the two consecutive data on the left are less than C1*h Max , then: the data points between Pstart and Pend are considered to be the valid data area of ​​the arterial waveform to be analyzed. Figure 5 shown.

[0157] The specific steps include:

[0158] c1: in the difference sequence: h1,h2,....,h N Find the largest value and record it as: h Max ;

[0159] In specific implementation, if h Max If the number is greater than 1, the first maximum point is used for subsequent calculations.

[0160] c2: Initialization number j = 1,

[0161] c3: h j 、h j+1 、h j+2 Respectively with C1*h Max Make comparisons;

[0162] If there is h j 、h j+1 、h j+2 Both are greater than C1*h Max , then h j Set to Pstart and execute step c6;

[0163] Otherwise, execute step c4;

[0164] c4: Determine whether j is greater than Max-2;

[0165] If j>Max-2, the arterial waveform data to be analyzed corresponding to the difference sequence is judged as an interference signal, that is, when the collected arterial waveform speed is too slow or too fast, the highest point h Max Insufficient data points on the left side result in measurement interference data. As long as Pstart cannot be found, the data can be judged as interference data.

[0166] Otherwise, execute step c5;

[0167] c5: j=j+1, and steps c3 to c4 are executed repeatedly;

[0168] c6: Determine the relationship between j and N;

[0169] If j>N-2, the arterial waveform data to be analyzed corresponding to the difference sequence is judged as an interference signal;

[0170] Otherwise, execute step c7;

[0171] c7: j = j + 1;

[0172] c8: h j 、h j+1 、h j+2 Respectively with C1*h Max Make comparisons;

[0173] Wherein, C1 is the effective peak-to-valley difference threshold, and the specific value is set according to the research needs; in this embodiment, the effective peak-to-valley difference threshold C1 is 1 / 3, that is, when the difference between the ordinates of the peak point and the valley point on the arterial waveform to be analyzed is less than the highest point h Max When the value is 1 / 3 of the value, the data point is considered invalid.

[0174] If there is h j 、h j+1 、h j+2 Both are greater than C1*h Max , then h j+2 Set to Pend and execute step c10;

[0175] Otherwise, execute step c9;

[0176] c9: Determine whether j is greater than N-2;

[0177] If j>N-2, the arterial waveform data to be analyzed corresponding to the difference sequence is judged as an interference signal, that is, when Pstart is found but Pend cannot be found, it means that the highest point h Max There are not enough data points on the right side, which are also judged as interference data;

[0178] Otherwise, j=j+1, and steps c8 to c9 are executed repeatedly;

[0179] c10: difference sequence: h1,h2,....,h N In the figure, the data points between Pstart and Pend are considered to be the valid data area of ​​the arterial waveform to be analyzed.

[0180] By h Max Find the abnormal disturbance caused by the deflation process of the detection tool or the shaking and movement of the measured person, which causes the collected arterial waveform to be too slow or too fast, resulting in insufficient data points on the left or right sides of the highest point, that is, the measurement interference data generated by the non-existence of Pstart or Pend during the calculation process. The interference data can be directly judged as pseudo-error data without subsequent calculation. If h MaxIf the number of data points on both sides is sufficient, the data points between Pstart and Pend are considered as the valid data area of ​​the arterial waveform to be analyzed, and subsequent calculations are performed based on the data points between Pstart and Pend.

[0181] S7: Figure 6 As shown, data fitting is performed on the valid data area, specifically including the following steps:

[0182] d1: Take out the data from Pstart to Pend in the difference sequence and record it as the valid data sequence.

[0183] The valid data sequence is: h Pstart ,h Pstart+1 ....h max .....h Pend-1 ,h Pend ;

[0184] d2: remove h max. Data on the left: h Pstart ,h Pstart+1 ....h max-1 , with h max. Mirror the center to get the fitted data sequence on the left: h Pstart ,h Pstart+1 ....h max-1 ,h max ,h max-1 ....h Pstart+1 ,h Pstart ;

[0185] The horizontal coordinate corresponding to the fitted data sequence on the left is: x Pstart ,x Pstart+1 ....x max-1 , x max ,x max- 1....x Pstart ;

[0186] d3: Remove h max. Data on the right: h max+1 .....h Pend-1 ,h Pend , with h max. Mirror the center to get the fitting data sequence on the right: h Pend ,h Pend-1 ....h max+1 ,h max ,h max+1 .....h Pend-1 ,h Pend ;

[0187] The horizontal coordinate corresponding to the fitted data sequence on the right is: xPend ,x Pend-1 ....x max+1 , x max ,x max+1 ....x Pend-1 ,x Pend .

[0188] S8: Calculate the peak amplitude disturbance rate PError corresponding to the arterial waveform to be analyzed;

[0189] e1: Calculate the number of interference points on the left ErrorLeft;

[0190] The left fitting data sequence and the horizontal coordinates corresponding to the left fitting data sequence are used as the vertical coordinates and horizontal coordinates of the data points, and the quadratic fitting curve h=ax is obtained using the least squares method. 2 +bx+c;

[0191] The distance between the ordinate of the valid data point on the left side and the fitting curve is greater than C2*h Max The number of points is recorded as the number of interference points on the left ErrorLeft;

[0192] Among them, C2 is the interference point judgment threshold. In actual work, not all data points can be strictly fitted into the fitting curve. For example, Figure 6 As shown, there are three points below the quadratic fitting curve that are not fitted into the curve; but the difference is very small and can also reflect the data trend, and can be used as valid data. In this application, the interference point judgment threshold C2 is used to control the judgment of the interference data point, ensuring that the technical solution of this application meets the actual production needs and is suitable for various application scenarios. The specific value of C2 is set according to research needs; in this embodiment, the interference point judgment threshold C2 is 1 / 8, that is, when the distance between the ordinate of the valid data point on the arterial waveform to be analyzed and the fitting curve is greater than C2*h Max The data point is judged as interference voltage only when

[0193] e2: Calculate the number of interference points on the right ErrorRight;

[0194] The quadratic fitting curve h=ax is obtained by using the least squares method, with the right-side fitting data sequence and the horizontal coordinates corresponding to the right-side fitting data sequence as the vertical coordinates and horizontal coordinates of the data points. 2 +bx+c;

[0195] The distance between the vertical coordinate of the valid data point on the right and the fitting curve is greater than C2*h Max The number of points is recorded as the number of interference points on the right side ErrorRight.

[0196] e3: Calculate the peak amplitude disturbance rate PError;

[0197] PError=(ErrorRight+ErrorLeft) / (Pend-Pstart+1).

[0198] The peak amplitude disturbance rate PError indicates the proportion of interference points in the valid data area. The larger the proportion of interference points, the greater the possibility that the arterial waveform to be analyzed is a pseudo-error interference signal.

[0199] S9: Determine the nature of the arterial waveform to be analyzed;

[0200] f1: compare the dispersion Sp of the arterial waveform to be analyzed with the preset dispersion threshold C3, and compare the peak amplitude disturbance rate PError with the preset disturbance rate threshold C4;

[0201] When the following two conditions are met at the same time, the arterial waveform to be analyzed is judged as pseudo-error interference data;

[0202] Sp>C3, and PError>C4.

[0203] The specific values ​​of the scatter degree threshold C3 and the disturbance rate threshold C4 are set according to actual research requirements. In this embodiment, the scatter degree threshold C3 is 30%; the disturbance rate threshold C4 is 20%.

[0204] The blood pressure arterial waveform signal quality assessment method implemented based on the blood pressure arterial waveform signal artifact recognition method includes the following steps.

[0205] g1: Get the arterial waveform to be analyzed;

[0206] g2: Calculate and obtain the dispersion Sp and peak amplitude disturbance rate PError corresponding to the arterial waveform to be analyzed;

[0207] g3: Determine whether the arterial waveform to be analyzed is pseudo-error interference data based on the scatter Sp and the peak amplitude disturbance rate PError;

[0208] If it is artifact interference data, the quality of the arterial waveform to be analyzed is set as data with poor indicators;

[0209] Otherwise, execute step g4;

[0210] g4: Calculate the treatment evaluation parameter Qua of the arterial waveform to be analyzed:

[0211] Qua=SP*α+PError*β;

[0212] Among them, α is the weight coefficient of the scatter degree, and β is the weight coefficient of the disturbance rate;

[0213] g5: Compare Qua with the preset quality judgment threshold QH;

[0214] When Qua is less than QH, the quality of the data corresponding to the arterial waveform graph to be analyzed is judged to be data of good quality, otherwise, the quality of the data corresponding to the arterial waveform graph to be analyzed is judged to be data of poor quality.

[0215] In this embodiment, the dispersion weight coefficient α is 25%; the disturbance rate weight coefficient β is 75%; even if the dispersion of the arterial waveform to be analyzed is relatively large, when its peak amplitude disturbance rate PError is relatively small, that is, the data point is relatively consistent with the fitting curve, it means that the waveform of the analyzed arterial waveform is relatively complete, and the corresponding data of the arterial waveform to be analyzed can reflect the heartbeat trend of the data source and can also be used as valid data for calculation.

[0216] The quality judgment threshold QH is 20%. A QH value of 100% represents a completely scattered rhythm (the degree of scatteredness Sp is the largest) and all points deviate from the fitting curve (the peak amplitude disturbance rate PError is the largest). Generally, a normal rhythm is relatively neat with a scattered degree sp<20%, and most data points (at least 80% of the data points) can fall on the fitting line, so the peak amplitude disturbance rate PError<20%. The QH value represents the combination of Sp and PError. Therefore, a QH value less than 20% represents good signal quality and is not a pseudo error. In actual applications, users can dynamically change the QH value threshold as needed to select data with better signal quality. This ensures that among the approximately 50 sets of blood pressure measurement records in a patient's case, at least 30 sets are normal data and the others are pseudo errors.

[0217] After using the technical solution of the present invention, the irregular distribution of arterial waveform peaks on the time axis caused by jitter, movement, severe arrhythmia or external vibration interference is evaluated through the hitting area of ​​the arterial peak interval on the Lorenz scatter plot; then the quadratic curve fitting is used to find the proportion of the number of abnormal peak points, and the abnormal peak-to-valley pressure of the arterial waveform caused by jitter, movement, and uneven inflation or deflation speed is evaluated; by quantitatively analyzing the features of the data points included in the arterial waveform diagram to be analyzed, the entire analysis process does not require the subjective judgment of the analyst, the result is accurate, and the calculation speed is fast, which is particularly suitable for the analysis process of massive blood pressure data, and can quickly find all the artifact interference point data, greatly improving the efficiency of data analysis.

Claims

1. A method for identifying arterial blood pressure waveform signal artifacts, characterized in that: It includes the following steps: S1: Obtain the arterial waveform to be analyzed; The horizontal axis of the arterial waveform diagram to be analyzed is the pulse pulsation position caused by the heartbeat, and the vertical axis is the pressure value collected by the dynamic blood pressure meter pressure sensor; S2: marking the peaks and valleys of the arterial waveform to be analyzed; Based on the arterial waveform to be analyzed, finding all peak points and valley points; Taking the first peak point as the starting point, pair it with the adjacent valley point; assuming that the arterial waveform to be analyzed includes N pairs of peak points and valley points, the peak points and valley points included in the arterial waveform to be analyzed are expressed as: (Peak1, Valley1), (Peak2, Valley2),..., (PeakN, ValleyN); The horizontal coordinates of the peak points Peak1-PeakN form the sequence x1,x2,...x N ; S3: Obtain the ordinates of each pair of peak points and valley points, and calculate the difference between the ordinates of each pair of peak points and valley points; Then we get the sequence of the absolute value of the difference between the ordinates of each pair of peak points and valley points: Recorded as difference sequence: h1,h2,....,h N ; S4: Draw a Lorenz scatter plot, which includes the following steps: a1: calculate the sequence x1,x2,...x N The difference sequence is recorded as Δ1=x2-x1, Δ2=x3-x2, ..., Δ N-1 =x N -x N-1 ; △ i Represents the pulse interval or heartbeat interval, i is a natural number, i∈(1,N-1); a2: The adjacent △ i Combine into pairs to form (△ i ,△ i+1 ); a3: construct the coordinate axis, the units of the horizontal and vertical axes are both in ms; Each group (△ i ,△ i+1 ) i is the horizontal axis, △ i+1 As the vertical axis, (△ i ,△ i+1 ) is plotted on the coordinate axis to obtain a Lorenz scatter plot; S5: Calculating the dispersion Sp of the arterial waveform to be analyzed, specifically comprising the following steps: b1: construct a mapping grid; Set the heartbeat interval threshold and reasonable interval threshold; The heartbeat interval threshold represents the maximum value of reasonable heartbeat interval data; The reasonable interval threshold means that when the difference between two heartbeat intervals of the same data source is less than the reasonable interval threshold, the two heartbeat intervals are regarded as the same data during calculation; The mapping grid includes: a mapping coordinate system and a grid; The horizontal axis and the vertical axis of the mapping coordinate system are both set to the heartbeat interval, the starting point of the horizontal axis and the vertical axis is 0, and the maximum value is the heartbeat interval threshold; The grid is constructed between the horizontal axis and the vertical axis in units of interval allowable values; b2: Take out each group (△ i ,△ i+1 ), judge △ i Relationship with heartbeat interval threshold; If i > the heartbeat interval threshold, then (△ i ,△ i+1 ) does not participate in the calculation; Otherwise, judge △ i+1 Relationship with heartbeat interval threshold; If i+1 > the heartbeat interval threshold, then (△ i ,△ i+1 ) does not participate in the calculation; Otherwise, execute step b3; b3:(△ i ,△ i+1 ) i is the horizontal axis, △ i+1 The vertical coordinate is mapped into the grid of the mapping grid map; b4:When all (△ i ,△ i+1 ) are all involved in the calculation, and the number of successfully mapped grids in the mapping grid map is counted; Assuming that n grids in the mapping grid map are mapped successfully, calculate the scatter Sp of the arterial waveform map to be analyzed; Sp = n / (N-1); S6: Finding the effective area of ​​the arterial waveform to be analyzed, specifically comprising the following steps: c1: in the difference sequence: h1,h2,....,h N Find the largest value and record it as: h Max ; c2: Initialization number j = 1, c3: h j 、h j+1 、h j+2 Respectively with C1*h Max Make comparisons; If there is h j 、h j+1 、h j+2 Both are greater than C1*h Max , then h j Set to Pstart and execute step c6; Otherwise, execute step c4; c4: Determine whether j is greater than Max-2; If j>Max-2, the arterial waveform data to be analyzed corresponding to the difference sequence is judged as pseudo-error interference data; Otherwise, execute step c5; c5: j=j+1, and steps c3 to c4 are executed repeatedly; c6: Determine the relationship between j and N; If j>N-2, the arterial waveform data to be analyzed corresponding to the difference sequence is judged as pseudo-error interference data; Otherwise, execute step c7; c7: j = j + 1; c8: h j 、h j+1 、h j+2 Respectively with C1*h Max Make comparisons; Among them, C1 is the effective peak-to-valley difference threshold; If there is h j 、h j+1 、h j+2 Both are greater than C1*h Max , then h j+2 Set to Pend and execute step c10; Otherwise, execute step c9; c9: Determine whether j is greater than N-2; If j>N-2, the arterial waveform data to be analyzed corresponding to the difference sequence is determined as an interference signal; Otherwise, j=j+1, and steps c8 to c9 are executed repeatedly; c10: the difference sequence: h1,h2,....,h N In the figure, the data points between Pstart and Pend are considered as the valid data area of ​​the arterial waveform to be analyzed; S7: performing data fitting on the valid data region, specifically comprising the following steps: d1: Take out the data included in Pstart~Pend from the difference sequence and record it as the valid data sequence, The valid data sequence is: h Pstart ,h Pstart+1 ....h max..... h Pend-1 ,h Pend ; d2: remove h max. Data on the left: h Pstart ,h Pstart+1 ....h max-1 , with h max. Mirror the center to get the fitted data sequence on the left: h Pstart ,h Pstart+1 ....h max-1 ,h max ,h max-1 ....h Pstart+1 ,h Pstart ; The horizontal coordinate corresponding to the left fitting data sequence is: Pstart ,x Pstart+1 ....x max-1 , x max ,x max- 1....x Pstart ; d3: remove h max. Data on the right: h max+1..... h Pend-1 ,h Pend , with h max. Mirror the center to get the fitting data sequence on the right: h Pend ,h Pend-1 ....h max+1 ,h max ,h max+1..... h Pend-1 ,h Pend ; The horizontal coordinate corresponding to the right-side fitting data sequence is: Pend ,x Pend-1 ....x max+1 , x max ,x max+1 ....x Pend-1 ,x Pend ; S8: Calculate the peak amplitude disturbance rate PError corresponding to the arterial waveform to be analyzed; e1: Calculate the number of interference points on the left ErrorLeft; The left fitting data sequence and the horizontal coordinates corresponding to the left fitting data sequence are used as the vertical coordinates and horizontal coordinates of the data points, and the quadratic fitting curve h=ax is obtained using the least squares method. 2 +bx+c; The distance between the ordinate of the valid data point on the left and the fitting curve is greater than C2*h Max The number of points is recorded as the number of left interference points ErrorLeft; Among them, C2 is the interference point judgment threshold; e2: Calculate the number of interference points on the right ErrorRight; The quadratic fitting curve h=ax is obtained by using the least squares method, with the right-side fitting data sequence and the horizontal coordinates corresponding to the right-side fitting data sequence as the vertical coordinates and horizontal coordinates of the data points. 2 +bx+c; The distance between the vertical coordinate of the valid data point on the right and the fitting curve is greater than C2*h Max The number of points is recorded as the number of right interference points ErrorRight; e3: Calculate the peak amplitude disturbance rate PError; PError=(ErrorRight+ErrorLeft) / (Pend-Pstart+1); S9: Determine the property of the arterial waveform to be analyzed; f1: comparing the dispersion degree Sp of the arterial waveform to be analyzed with a preset dispersion degree threshold value C3; f2: comparing the peak amplitude disturbance rate PError with a preset disturbance rate threshold C4; f3: When the following two conditions are met at the same time, the arterial waveform to be analyzed is determined to be pseudo-error interference data; Sp>C3, and PError>C4.

2. The method for identifying arterial blood pressure waveform signal artifacts according to claim 1, characterized in that: In step S1, the horizontal axis of the arterial waveform to be analyzed is the pulse pulsation position caused by the heartbeat, which is expressed in the number of sampling points or sampling time.

3. The method for identifying arterial blood pressure waveform signal artifacts according to claim 1, characterized in that: The heartbeat interval threshold is set to 2000ms; the reasonable interval threshold is set to 100ms.

4. The method for identifying arterial blood pressure waveform signal artifacts according to claim 1, characterized in that: In step b3, when the horizontal axis of the arterial waveform diagram to be analyzed is represented by the number of sampling points, and the unit of the grid of the mapping grid diagram is ms, For (Δ i ,Δ i+1 ), calculate RR respectively i and RR i+1 : RR i and RR i+1 As the horizontal and vertical coordinates, (△ i ,△ i+1 ) is mapped into a grid of the mapping grid map; SAMPLE is the sampling rate corresponding to the arterial waveform to be analyzed.

5. The method for identifying arterial blood pressure waveform signal artifacts according to claim 1, characterized in that: The effective peak-to-valley difference threshold C1 is 1 / 3; the interference point determination threshold C2 is 1 / 8.

6. The method for identifying arterial blood pressure waveform signal artifacts according to claim 1, characterized in that: The scatter degree threshold C3 is 30%; the disturbance rate threshold C4 is 20%.

7. The method for identifying arterial blood pressure waveform signal artifacts according to claim 1, characterized in that: In step c1, if h Max If the number is greater than 1, the first maximum point is used for subsequent calculations.

8. A method for evaluating the quality of a blood pressure arterial waveform signal based on the method for identifying arterial error of a blood pressure arterial waveform signal according to claim 1, characterized in that: It includes the following steps: g1: Get the arterial waveform to be analyzed; g2: Calculate the dispersion Sp and the peak amplitude disturbance rate PError corresponding to the obtained arterial waveform to be analyzed; g3: judging whether the arterial waveform to be analyzed is pseudo-error interference data according to the scatter Sp and the peak amplitude disturbance rate PError; If it is artifact interference data, the quality of the arterial waveform to be analyzed is set as data with poor indicators; Otherwise, execute step g4; g4: Calculate the treatment evaluation parameter Qua of the arterial waveform to be analyzed: Qua=Sp*α+PError*β; Among them, α is the weight coefficient of the scatter degree, and β is the weight coefficient of the disturbance rate; g5: Compare Qua with the preset quality judgment threshold QH; When Qua is less than QH, the quality of the data corresponding to the arterial waveform graph to be analyzed is judged to be data of good quality; otherwise, the quality of the data corresponding to the arterial waveform graph to be analyzed is judged to be data of poor quality.

9. A blood pressure arterial waveform signal quality assessment method according to claim 8, characterized in that: The value of the dispersion weight coefficient α is 25%; the value of the disturbance rate weight coefficient β is 75%; and the value of the quality judgment threshold QH is 20%.

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