Method and device for detecting a reichert's notch and medical device

Abstract

The present application relates to the technical field of biomedical signal processing, and particularly relates to a detection method and device of heavy-Bo incision and medical equipment, the method comprises: acquiring a pulse signal, and determining a heavy-Bo detection interval in the pulse signal; extracting a difference sequence corresponding to the heavy-Bo detection interval based on a detection step; performing minimum value analysis on each point in the difference sequence to determine a slope variation point; filtering the difference sequence according to the slope variation point to determine a selectable heavy-Bo incision point; determining a skew state weight corresponding to each time point in the heavy-Bo detection interval based on a current skew degree; mapping the selectable heavy-Bo incision point and the skew state weight based on the time point of the selectable heavy-Bo incision point to determine a target heavy-Bo incision point. The difference sequence is extracted in the manner of the detection step, the processing of complex algorithms is avoided, and the analysis of the slope variation point is performed on this basis, so that the signal with unobvious or weak heavy-Bo characteristics can be effectively and accurately recognized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomedical signal processing, and in particular to a detection method and device of a dicrotic notch and a medical device. BACKGROUND

[0002] Blood circulation in the cardiovascular system relies on the alternating activities of heart contraction and diastole. The so-called systole and diastole usually refer to the systole and diastole of the ventricle. During the rapid ejection period of the ventricle, blood rapidly enters the artery, and the blood vessel wall expands accordingly. During the slow ejection period, the ejection velocity slows down, and the blood vessel wall elastically retracts. At the beginning of the diastole period of the ventricle, blood flows back, and the blood vessel wall expands temporarily. When the ventricle is completely in the diastole period, the blood vessel wall gradually returns to the initial position. Thus, even if the collection site and method are different, a typical physiological signal as shown in FIG. 1 can usually be obtained, where D is the demarcation point between the systole and diastole, commonly known as the dicrotic notch. Figure 1

[0003] The detection of the dicrotic notch is the premise for calculating most of the pulse contour cardiac output (C.O.), stroke volume variation (SVV) and other hemodynamic parameters for the pulse signal. For the pulse wave signal, since it can reflect the function of the aortic valve, it is beneficial to the evaluation of the health level of the cardiovascular system.

[0004] At present, the dicrotic notch detection methods with better performance include the same sampling judgment method, the wavelet transform method and the like. The same sampling judgment method acquires the pulse signal and the electrocardiogram signal synchronously, and detects the dicrotic notch by using the relative position information of the two. This method needs to acquire the electrocardiogram synchronously, and the additional electrocardiogram electrode and acquisition device make the method not economical, and make the detection operation cumbersome. The wavelet transform method selects a wavelet to perform wavelet transform on the signal to highlight the local features, and then detects the dicrotic notch on a certain scale coefficient. This method needs to select a wavelet basis, and the uncertainty thereof will affect the detection accuracy, and the calculation is complex, which is not conducive to the embedded system design and implementation. SUMMARY

[0005] Therefore, the embodiments of the present application provide a detection method and device of a dicrotic notch and a medical device to solve the problems of cumbersome and low accuracy of the dicrotic notch detection.

[0006] According to a first aspect, the embodiments of the present application provide a detection method of a dicrotic notch, comprising:

[0007] acquiring a pulse signal, and determining a dicrotic detection interval in the pulse signal;

[0008] extracting a difference sequence corresponding to the dicrotic detection interval based on a detection step;

[0009] ​performing minimum value analysis on each point in the difference sequence to determine a slope variation point;

[0010] filtering the difference sequence according to the slope variation point to determine a selectable heavy cut-in point;

[0011] determining a skew state weight corresponding to each time point in the heavy cut-in detection interval based on a current skew degree;

[0012] mapping the selectable heavy cut-in point and the skew state weight based on the time point of the selectable heavy cut-in point to determine a target heavy cut-in point.

[0013] The heavy cut-in detection method provided by the embodiments of the present application extracts a difference sequence in a detection step, avoids complex algorithm processing, analyzes a slope variation point on this basis, can detect points with unobvious wave peak and wave valley changes, takes the points as selectable heavy cut-in points, thereby effectively and accurately identifies signals with unobvious or weak heavy features, and finally introduces a current skew degree to determine a target heavy cut-in point, which can ensure the accuracy of the determined target heavy cut-in point.

[0014] With reference to the first aspect, in a first implementation manner of the first aspect, the minimum value analysis on each point in the difference sequence to determine a slope variation point comprises:

[0015] querying the amplitudes of the points in the difference sequence to determine minimum value points with amplitudes greater than zero;

[0016] determining a target minimum value point with the minimum amplitude among the minimum value points;

[0017] screening each minimum value point based on the amplitude of the target minimum value point to determine the slope variation point.

[0018] The heavy cut-in detection method provided by the embodiments of the present application can remove minimum value points caused by fluctuations, on the one hand, can reduce data processing amount, and on the other hand, can improve detection accuracy.

[0019] With reference to the first aspect and the first implementation manner, in a second implementation manner of the first aspect, the screening each minimum value point based on the amplitude of the target minimum value point to determine the slope variation point comprises:

[0020] querying each minimum value point with an amplitude greater than a preset multiple of the minimum amplitude among the minimum value points;

[0021] deleting the queried minimum value point among the minimum value points to determine the slope variation point.

[0022] The detection method of the heavy cut notch provided by the embodiment of the application simplifies the data processing process by setting a preset multiple to delete the minimum value points, considering the change of the signal slope.

[0023] In a third implementation of the first aspect, the detection step length is determined based on the length of the heavy cut detection interval.

[0024] The detection step length is determined based on the length of the heavy cut detection interval.

[0025] The differential sequence corresponding to the heavy cut detection interval is extracted based on the detection step length.

[0026] The detection method of the heavy cut notch provided by the embodiment of the application determines the detection step length based on the length of the heavy cut detection interval, that is, the detection step length changes with the length of the heavy cut detection interval, avoiding too few data points in the differential sequence caused by using a longer detection step length for a shorter heavy cut detection interval, or too many data points in the differential sequence caused by using a shorter detection step length for a longer heavy cut detection interval, thereby improving the detection efficiency.

[0027] In a fourth implementation of the first aspect, the detection step length is determined based on the length of the heavy cut detection interval.

[0028] The length of the heavy cut detection interval is compared with a preset length range to determine a target preset length range.

[0029] The step length corresponding to the target preset length range is determined as the detection step length.

[0030] The detection method of the heavy cut notch provided by the embodiment of the application sets a corresponding step length for different preset length ranges, and the detection step length can be determined through comparison, thereby improving the efficiency of detection step length determination.

[0031] In a fifth implementation of the first aspect, the differential sequence is filtered based on the slope variation point to determine optional heavy cut notch points.

[0032] The extreme value pair is determined through extreme value pair analysis of the differential sequence.

[0033] The slope variation point in the differential sequence and the extreme value pair are determined as the optional heavy cut notch points.

[0034] The detection method of the heavy cut notch provided by the embodiment of the application determines the extreme value pair, and the extreme value pair is a point with obvious heavy cut characteristics, so that the determination of the extreme value pair can improve the comprehensiveness and accuracy of the optional heavy cut notch points.

[0035] With reference to the first aspect, in a sixth implementation form of the first aspect, the determining of the skewness weight corresponding to each time point in the heavy-hammer detection interval based on the current skewness degree comprises:

[0036] determining a current skewness time point based on the current skewness degree and a length of the heavy-hammer detection interval;

[0037] determining the skewness weight corresponding to each time point in the heavy-hammer detection interval based on the current skewness time point and the length of the heavy-hammer detection interval.

[0038] With reference to the sixth implementation form of the first aspect, in a seventh implementation form of the first aspect, the current skewness degree is an initial fixed value.

[0039] The detection method of the heavy-hammer notch provided in the embodiment of the application can simplify the detection process by using the initial fixed value as the current skewness degree.

[0040] With reference to the seventh implementation form of the first aspect, in an eighth implementation form of the first aspect, the determination manner of the current skewness degree comprises:

[0041] obtaining a previous skewness degree and a historical skewness degree before the previous skewness degree;

[0042] determining the current skewness degree based on a weighted combination of the previous skewness degree and the historical skewness degree.

[0043] The detection method of the heavy-hammer notch provided in the embodiment of the application combines the historical skewness degree in the calculation process of the current skewness degree, so as to correct the influence of the physiological state of the blood vessel on the heavy-hammer notch and improve the accuracy of the detection of the heavy-hammer notch.

[0044] With reference to the eighth implementation form of the first aspect, in a ninth implementation form of the first aspect, the determination manner of the historical skewness degree comprises:

[0045] performing signal quality analysis on each historical pulse signal, and obtaining skewness degrees of historical pulse signals with high signal quality;

[0046] performing statistical analysis on the obtained skewness degrees to determine the historical skewness degree.

[0047] The detection method of the heavy-hammer notch provided in the embodiment of the application uses the skewness degrees corresponding to the historical pulse signals with high signal quality, so that the calculation of the historical skewness degree is based on reliable historical pulse signals, and the accuracy of the historical skewness degree can be ensured.

[0048] With reference to the seventh implementation form of the first aspect or the eighth implementation form of the first aspect, in a tenth implementation form of the first aspect, the determination manner of the initial fixed value comprises:

[0049] acquiring a physiological parameter of a target patient, the physiological parameter comprising at least one of gender, age, weight, and height;

[0050] determining the initial fixed value based on the physiological parameter.

[0051] The method for detecting the notching point of the dicrotic notch provided by the embodiments of the present application can ensure the reliability of the initial fixed value, because the typical time corresponding to the notching point of the dicrotic notch of the target patient corresponding to different physiological parameters is different.

[0052] With reference to the first aspect, in a first aspect eleventh embodiment, the method further comprises:

[0053] determining the weight corresponding to the time point of the selectable notching point of the dicrotic notch in the skewness weight based on the time point of the selectable notching point of the dicrotic notch, so as to determine the weight of each selectable notching point of the dicrotic notch.

[0054] determining the selectable notching point of the dicrotic notch with the largest weight as the target notching point of the dicrotic notch.

[0055] The method for detecting the notching point of the dicrotic notch provided by the embodiments of the present application can ensure the accuracy of the determined target notching point of the dicrotic notch by determining the selectable notching point of the dicrotic notch with the largest weight as the target notching point of the dicrotic notch.

[0056] With reference to the first aspect, in a first aspect twelfth embodiment, the pulse signal is expressed in a pulse waveform, and the method further comprises:

[0057] superimposing and displaying the target notching point of the dicrotic notch on the pulse waveform;

[0058] determining an adjusted notching point of the dicrotic notch in response to an adjustment operation on the target notching point of the dicrotic notch.

[0059] determining an adjusted skewness based on the position of the adjusted notching point of the dicrotic notch in the pulse waveform.

[0060] detecting the notching point of the dicrotic notch based on the adjusted skewness for a current pulse waveform.

[0061] displaying the detection result of the notching point of the dicrotic notch of the current pulse waveform.

[0062] The method for detecting the notching point of the dicrotic notch provided by the embodiments of the present application can ensure the accuracy of the subsequent detection result by adjusting the automatically determined notching point of the dicrotic notch in a man-machine interactive manner to modify the skewness.

[0063] With reference to the twelfth embodiment of the first aspect, in a thirteenth embodiment of the first aspect, after the target heavy cut point is adjusted, the method further comprises:

[0064] displaying prompt information for prompting that the heavy cut point is detected based on the adjusted skewness.

[0065] With reference to the thirteenth embodiment of the first aspect, in a fourteenth embodiment of the first aspect, the prompt information comprises a transition time, and the displaying prompt information comprises:

[0066] in response to a setting operation of the transition time, determining the transition time;

[0067] displaying a change of the transition time.

[0068] According to the second aspect, the embodiments of the present application further provide a heavy cut point detection device, comprising:

[0069] an acquisition module configured to acquire a pulse signal and determine a heavy detection interval in the pulse signal;

[0070] an extraction module configured to extract a difference sequence corresponding to the heavy detection interval based on a detection step;

[0071] an analysis module configured to perform minimum value analysis on each point in the difference sequence to determine a slope variation point;

[0072] a filtering module configured to filter the difference sequence according to the slope variation point to determine a selectable heavy cut point;

[0073] a first determination module configured to determine a skewness weight corresponding to each time point in the heavy detection interval based on a current skewness;

[0074] a second determination module configured to map the selectable heavy cut point and the skewness weight based on a time point of the selectable heavy cut point to determine a target heavy cut point.

[0075] According to the third aspect, the embodiments of the present application provide an electronic device, comprising a memory and a processor, which are communicatively connected with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the heavy cut point detection method in the first aspect or any one of the embodiments of the first aspect.

[0076] According to the fourth aspect, the embodiments of the present application provide a computer readable storage medium, which stores computer instructions for causing the computer to perform the heavy cut point detection method in the first aspect or any one of the embodiments of the first aspect.

[0077] It should be noted that the corresponding beneficial effects of the heavy cut detection device, the electronic device and the computer readable storage medium provided by the embodiments of the present application are described above in the description of the detection method of the heavy cut, and will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0079] Figure 1 is a schematic diagram of a pulse signal;

[0080] Figure 2 is a flowchart of the detection method of the heavy cut according to the embodiments of the present application;

[0081] Figure 3 is a flowchart of the detection method of the heavy cut according to the embodiments of the present application;

[0082] Figure 4a is a schematic diagram of a pulse signal;

[0083] Figure 4b is Figure 4a the differential sequence corresponding to the pulse signal shown in FIG. 8;

[0084] Figure 4c is Figure 4a the skew state weight corresponding to the pulse signal shown in FIG. 9;

[0085] Figure 5a is a schematic diagram of a pulse signal;

[0086] Figure 5b is Figure 5a the differential sequence corresponding to the pulse signal shown in FIG. 10;

[0087] Figure 5c is Figure 5a the skew state weight corresponding to the pulse signal shown in FIG. 11;

[0088] Figure 6 is a flowchart of the detection method of the heavy cut according to the embodiments of the present application;

[0089] Figure 7 is a detection result schematic diagram of the heavy cut point corresponding to the pulse signal according to the embodiments of the present application;

[0090] Figure 8is a detection result schematic diagram of the corresponding rebo incisure point of the pulse signal according to the embodiment of the present application;

[0091] Figures 9a-9c is a flow chart of the detection method of the rebo incisure according to the embodiment of the present application;

[0092] Figure 10 is a schematic diagram of the rebo incisure detection according to the embodiment of the present application;

[0093] Figure 11 is a structural block diagram of the detection device of the rebo incisure according to the embodiment of the present application;

[0094] Figure 12 is a hardware structure schematic diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0095] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0096] The detection method of the rebo incisure provided by the embodiments of the present application can detect the rebo incisure point of the pulse signal in real time, that is, the pulse signal is collected and the rebo incisure point is detected at the same time. Or, the pulse signal is collected first, and then the rebo incisure point of the pulse signal is detected within a preset time. The preset time is set in advance, or can be determined according to user demand, etc. The pulse signal includes but is not limited to blood pressure signal or pulse wave signal, and is not limited here. The specific application scenario can be determined according to actual use.

[0097] The medical device described in the embodiments of the present application can be a monitor, a central station or other devices, etc. The specific application scenario is not limited here.

[0098] The detection of the rebo incisure point provided by the embodiments of the present application is to extract the difference sequence in the rebo detection interval first, and then determine the slope variation point of the extracted difference sequence to determine the optional rebo incisure point. Then, the optional rebo incisure point is mapped with the skew state weight based on the time point to determine the weight corresponding to each optional rebo incisure point, and finally the target rebo incisure point is determined.

[0099] Further, the medical device also provides a human-computer interaction function. The medical device displays the pulse signal and the detected incisura jugularis point on the interface of the medical device by performing the detection method of the incisura jugularis provided in the embodiments of the present application. The user can adjust the automatically detected incisura jugularis point on the interface according to the requirement to obtain an adjusted incisura jugularis point. The medical device reversely calculates the skewness based on the adjusted incisura jugularis point, and detects the incisura jugularis point of the subsequent pulse signal based on the skewness.

[0100] Further, the interface also displays a prompt information to remind the user that the current detection of the incisura jugularis is based on the requirement of the user to avoid the influence of the detection accuracy of the incisura jugularis point due to the misoperation of the user.

[0101] According to the embodiments of the present application, a detection method of the incisura jugularis is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0102] In the present embodiment, a detection method of the incisura jugularis is provided, which can be used in the medical device described above, such as a monitor, etc. Figure 2 is a flowchart of the detection method of the incisura jugularis according to the embodiments of the present application, as shown in Figure 2 The flowchart includes the following steps:

[0103] S11, obtaining a pulse signal and determining an incisura jugularis detection interval in the pulse signal.

[0104] As described above, the pulse signal can be a real-time monitored pulse signal or an offline pulse signal. The pulse signal is a signal of time and pulse amplitude. For example, the medical device monitors the pulse signal of a target patient in real time and detects the incisura jugularis point; or other devices monitor the pulse signal of the target patient in real time and send it to the medical device, and accordingly, the medical device can obtain the pulse signal and detect the incisura jugularis point; or the medical device has stored the pulse signal in advance, etc. The source of the pulse signal is not limited here and can be set according to the actual requirement.

[0105] After obtaining the pulse signal, the medical device determines the positions of the wave peaks and wave troughs in the pulse signal by detecting the wave peaks and wave troughs to determine the incisura jugularis detection interval. The detection of the wave peaks and wave troughs can be performed by using the differential threshold method or other methods.

[0106] After detecting the wave peak and the wave valley, the features of the heavy beat point are combined, for example, the heavy beat feature does not appear in a period of time after the wave peak, and the like. Therefore, the medical device removes the waveforms in a period of time after the wave peak and in a period of time before the wave valley to prevent some abnormal interference. The heavy beat detection interval Intv can be expressed in the following manner:

[0107] Intv = {[V(i+1)-P(i)]xC1 [V(i+1)-P(i)]xC2}, i = 1, 2, 3…

[0108] wherein P(i) is the time corresponding to the wave peak of the ith wave, V(i+1) is the time corresponding to the wave valley of the (i+1)th wave, C1 is a constant greater than zero and close to zero, and C2 is a constant greater than zero and less than 1 and close to 1. For example, C1 is 0.1, and C2 is 0.9. Wherein, Figure 5a and Figure 4a The schematic of the heavy beat detection interval is shown.

[0109] S12, a difference sequence corresponding to the heavy beat detection interval is extracted based on the detection step.

[0110] The detection step can be a fixed value or determined for different heavy beat detection intervals, which is not limited herein. The difference sequence includes a plurality of collection points, and each collection point is the difference between the signal amplitude corresponding to the current time point and the signal amplitude corresponding to the time point after the detection step. For example, the difference sequence VSD is expressed as follows:

[0111] VSD = S(k)-S(k+step), k = 1, 2, 3…

[0112] wherein S is the signal amplitude corresponding to the heavy beat detection interval, and step is the detection step.

[0113] The specific process of this step will be described in detail below.

[0114] S13, the minimum value analysis is performed on each point in the difference sequence to determine the slope variation point.

[0115] As described above, the difference sequence includes a plurality of collection points, and the minimum value analysis is performed on every three collection points to determine the minimum value point in the difference sequence to determine the slope variation point. The slope variation point can also be the heavy beat notch point, and the heavy beat feature at the slope variation point can not be very obvious. Therefore, by detecting the slope variation point first and taking it as the optional heavy beat notch point, the accuracy of the target heavy beat notch point determined finally can be improved.

[0116] The specific process of this step will be described in detail below.

[0117] S14, filtering the differential sequence according to the slope variation point to determine a selectable heavy cut point.

[0118] After determining the slope variation point, it can be determined as a selectable heavy cut point. Alternatively, the medical device can also perform an extreme value pair analysis on the differential sequence to determine the extreme value pair in the differential sequence, which is also a selectable heavy cut point. Thus, the selectable heavy cut point includes the slope variation point and / or the extreme value pair.

[0119] This step will be described in detail below.

[0120] S15, determining the skew state weight corresponding to each time point in the heavy detection interval based on the current skew degree.

[0121] The current skew degree is used to represent the skew direction and degree of the pulse signal distribution. The current skew degree used for each heavy detection interval can be the same or different. When the current skew degrees corresponding to all heavy detection intervals are the same, an initial fixed value can be set as the current skew degree. The initial fixed value can be set according to an empirical value, or determined according to the physiological parameters of the target patient, etc.

[0122] When the current skew degrees corresponding to the heavy detection intervals are different, the current skew degree is updated in real time, and the historical skew degree is combined in the updating process.

[0123] After the medical device determines the current skew degree, it can determine the skew state weight corresponding to each time point in the heavy detection interval based on the current skew degree. The time span of the heavy detection interval is the period of the skew state weight, that is, the time points of the heavy detection interval correspond to the time points of the skew state weight. It should be noted that the skew state weight corresponding to each time point can be represented by a waveform or a discrete point.

[0124] This step will be described in detail below.

[0125] S16, mapping the selectable heavy cut point to the skew state weight based on the time point of the selectable heavy cut point to determine the target heavy cut point.

[0126] After the medical device determines the selectable heavy cut point, it can determine the position of the selectable heavy cut point in the pulse signal, and then determine the time point corresponding to the selectable heavy cut point. Then, the selectable heavy cut point is mapped to the skew state weight based on the time point to determine the weight corresponding to each selectable heavy cut point. Based on the weight corresponding to each selectable heavy cut point, the target heavy cut point can be determined. For example, the selectable heavy cut point with the largest weight is determined as the target heavy cut point.

[0127] The method for detecting heavy-tailed notch provided in the embodiment extracts a difference sequence through a detection step, avoids processing of a complex algorithm, analyzes a slope variation point on this basis, can detect a point with an unclear wave peak and wave trough change, takes the point as a selectable heavy-tailed notch point, thereby effectively and accurately identifying a signal with an unclear or weak heavy-tailed feature, and finally introduces a current skewness to determine a target heavy-tailed notch point, which can ensure the accuracy of the determined target heavy-tailed notch point.

[0128] In the embodiment, a method for detecting a heavy-tailed notch is provided, which can be used in the medical device such as the monitor. Figure 3 is a flowchart of the method for detecting a heavy-tailed notch according to the embodiment of the application, as shown in the figure, the flowchart includes the following steps: Figure 3

[0129] S21, acquiring a pulse signal and determining a heavy-tailed detection interval in the pulse signal.

[0130] For details, refer to S11 of the embodiment shown in Figure 2 , which will not be described here again.

[0131] S22, extracting a difference sequence corresponding to the heavy-tailed detection interval based on a detection step.

[0132] Specifically, the above S22 includes:

[0133] S221, determining the detection step by using the length of the heavy-tailed detection interval.

[0134] After the medical device determines the heavy-tailed detection interval, the length thereof is also determined. The medical device can store a corresponding relationship between the length and the step, for example, the step is a function of the length of the heavy-tailed detection interval, and the function is an increasing function, that is, the larger the length of the heavy-tailed detection interval, the larger the corresponding step. Alternatively, a plurality of preset length ranges can be set, and each preset length range corresponds to a step.

[0135] In some optional embodiments of the present embodiment, the above S221 can include:

[0136] (1) comparing the length of the heavy-tailed detection interval with a preset length range to determine a target preset length range.

[0137] (2) determining the step corresponding to the target preset length range as the detection step.

[0138] The medical device compares the length of the heavy-tailed detection interval with each preset length range to determine the corresponding target length range. Then, the step corresponding to the target length range is determined as the detection step. For example, the relationship between the step and the length is as follows: ​

[0139]

[0140] wherein, step is a step length, and len is a length.

[0141] Corresponding step lengths are set for different preset length ranges, and the detection step length can be determined through comparison, thereby improving the efficiency of detection step length determination.

[0142] It should be noted that the division of the preset length range is not limited to the above description, and other ways can be used for division, which is not limited herein, and can be set according to actual needs.

[0143] S222, extracting a difference sequence corresponding to the heavy detection interval based on the detection step length.

[0144] After the detection step length is determined, the detection step length can be used to extract the difference sequence in the heavy detection interval. The specific process can be referred to S12 of the above-mentioned embodiment, which will not be described here.

[0145] For example, Figure 4b It is shown that Figure 4a The variable step difference sequence corresponding to the pulse signal shown in Figure 5b It is shown that Figure 5a The variable step difference sequence corresponding to the pulse signal shown in

[0146] S23, performing minimum value analysis on each point in the difference sequence to determine a slope variation point.

[0147] Specifically, the above-mentioned S23 includes:

[0148] S231, querying the amplitude of each point in the difference sequence to determine a minimum value point with an amplitude greater than zero.

[0149] S232, determining a target minimum value point with the smallest amplitude in each minimum value point.

[0150] The medical device finds the minimum value point with an amplitude greater than zero in the difference sequence, and then screens the target minimum value point with the smallest amplitude from the minimum value points.

[0151] S233, screening each minimum value point based on the amplitude of the target minimum value point to determine a slope variation point.

[0152] Since the minimum value point can also be caused by signal fluctuation, the minimum amplitude is used to screen each minimum value point, thereby finally determining the slope variation point.

[0153] In some optional embodiments of the present embodiment, the above-mentioned S233 can include:

[0154] (1) Query the minimum points among all the minimum points whose amplitude is greater than a preset multiple of the minimum amplitude.

[0155] (2) Delete the local minimum points found in each local minimum point to determine the slope variation point.

[0156] Specifically, considering the changes in the signal slope, minimum points greater than N times the minimum amplitude need to be excluded. The medical device first calculates the value of a preset multiple of the minimum amplitude, and then compares the amplitude of each minimum point with the calculation result to filter out the minimum points that need to be deleted, thereby determining the slope variation points.

[0157] Taking into account the changes in the signal slope, a preset multiple is set to delete the minimum points, simplifying the data processing process.

[0158] S24, filter the difference sequence based on slope variation points to determine the optional repetition notch points.

[0159] Specifically, S24 includes:

[0160] S241, perform extremum pair analysis on the difference sequence to determine the extremum pairs.

[0161] S242 identifies slope variation points and extreme value pairs in the difference sequence as optional repetition notches.

[0162] The extreme value pair is defined as [extreme valley, extreme peak]. Medical devices detect the extreme valley near the zero-crossing point from positive to negative in the differential sequence, and then detect the extreme peak near the zero-crossing point from negative to positive in the VSD. The point corresponding to the extreme value pair may also be the repetition notch point; therefore, the repetition notch point includes slope variation points and extreme value pairs.

[0163] Since extreme value pairs are points with relatively obvious repetitive characteristics, determining extreme value pairs can improve the comprehensiveness and accuracy of selecting repetitive notch points.

[0164] S25, determine the skew state weights corresponding to each time point in the repetitive detection interval based on the current skewness.

[0165] Please see details Figure 2 S15 of the illustrated embodiment will not be described again here.

[0166] S26. Based on the time point of the optional repetition notch, the optional repetition notch is mapped to the skew state weight to determine the target repetition notch.

[0167] Please see details Figure 2 S16 of the illustrated embodiment will not be described again here.

[0168] The detection method of the notch of the heavy beat provided in the embodiment is based on the length of the heavy beat detection interval to determine the detection step, that is, the detection step changes with the length of the heavy beat detection interval, so as to avoid too few data points in the differential sequence caused by using a longer detection step for a shorter heavy beat detection interval, or too many data points in the differential sequence caused by using a shorter detection step for a longer heavy beat detection interval, and improve the detection efficiency. Since the minimum value point may also be caused by the fluctuation of the signal, the minimum value point is screened based on the amplitude of the target minimum value point, so as to remove the minimum value point caused by the fluctuation, which can reduce the data processing amount and improve the accuracy of the detection.

[0169] In the embodiment, a detection method of the notch of the heavy beat is provided, which can be used for the medical equipment such as the monitor and the like. Figure 6 The flowchart of the detection method of the notch of the heavy beat according to the embodiment of the present application is shown in FIG. 1, which includes the following steps: Figure 6

[0170] S31, obtaining the pulse signal and determining the heavy beat detection interval in the pulse signal.

[0171] For details, refer to S11 of the embodiment shown in FIG. 1, which will not be repeated here. Figure 2

[0172] S32, extracting the differential sequence corresponding to the heavy beat detection interval based on the detection step.

[0173] For details, refer to S22 of the embodiment shown in FIG. 2, which will not be repeated here. Figure 3

[0174] S33, performing minimum value analysis on each point in the differential sequence to determine the slope variation point.

[0175] For details, refer to S23 of the embodiment shown in FIG. 3, which will not be repeated here. Figure 3

[0176] S34, filtering the differential sequence according to the slope variation point to determine the optional heavy beat notch point.

[0177] For details, refer to S24 of the embodiment shown in FIG. 4, which will not be repeated here. Figure 3

[0178] S35, determining the skew state weight corresponding to each time point in the heavy beat detection interval based on the current skew degree.

[0179] Specifically, the above S35 includes:

[0180] S351, determining the current skew time based on the current skew degree and the length of the heavy beat detection interval. ​​​​​

[0181] The current skewness is an initial fixed value, and using the initial fixed value as the current skewness can simplify the detection process.

[0182] Skewness S d is expressed as: S d = T skew / (T-T skew )

[0183] T skew is the skewness time, T is the skewness integral period, the 0 time of the skewness state function is the starting point of the detection interval, and the waveform end point is the T time.

[0184] From the above formula, after the skewness and the skewness integral period are determined, the current skewness time can be determined accordingly, and the skewness weight of each time point can be determined based on the current skewness time. The current skewness is an initial fixed value, which can be set according to actual needs.

[0185] In some optional embodiments of the present embodiment, the skewness is updated in real time, that is, the determination method of the current skewness includes:

[0186] (1) Obtain the last skewness and the historical skewness before the last skewness.

[0187] The last skewness is the skewness used when detecting the beat point in the last beat interval, and the historical skewness can be the statistical analysis result of all historical skewnesses, or the skewness corresponding to the pulse waveform with good pulse waveform quality can be calculated. Based on this, the determination method of the historical skewness includes:

[0188] 1.1) Perform signal quality analysis on each historical pulse signal, and obtain the skewness of the historical pulse signal with signal quality meeting the requirements.

[0189] 1.2) Perform statistical analysis on the obtained skewness to determine the historical skewness.

[0190] A signal quality analysis module is provided in the medical device, which is used to analyze the quality of each historical pulse signal to obtain a quality score. The quality score is compared with a preset condition to screen out historical pulse signals with good signal quality, and then the skewness of these historical pulse signals with good signal quality is obtained.

[0191] The medical device can perform mean calculation on the obtained skewness, or weighted mean calculation, etc., to determine the historical skewness. Based on this, the historical skewness can also be called the historical stable skewness.

[0192] The skewness corresponding to the historical pulse signal with high signal quality is used to determine the historical skewness, so that the calculation of the historical skewness is based on reliable historical pulse signals, and the accuracy of the historical skewness can be ensured.

[0193] (2) The current skewness is determined based on the last skewness and the weighted historical skewness.

[0194] The skewness updating formula can be expressed as:

[0195] Next_S d =a×Cur_S d +(1-a)×His_S d

[0196] Next_S d is the skewness of the next re-Bochmann notch detection, Cur_S d is the current skewness, His_S d is the historical stable skewness, and a is a coefficient greater than zero and less than 1. The specific value of a can be set according to actual conditions. For example, a = 0.2 or a = 0.3.

[0197] In the calculation process of the current skewness, the historical skewness is combined to correct the influence of the physiological state of the blood vessel on the Bochmann notch and improve the accuracy of the re-Bochmann notch detection.

[0198] In some other optional embodiments of the present embodiment, the determination method of the initial fixed value includes:

[0199] (1) Obtain the physiological parameters of the target patient, including at least one of gender, age, weight, and height.

[0200] (2) Determine the initial fixed value based on the physiological parameters.

[0201] The physiological parameters include but are not limited to gender Gender, age Age, weight Weight, height Height, etc. For example, the typical time of the systolic and diastolic transition of a healthy adult male and a healthy adult female is different. Therefore, by inputting the physiological parameters in advance, the best skewness can be obtained by using the physiological parameters at the initial stage of the re-Bochmann notch detection, thereby improving the accuracy of the re-Bochmann notch detection. That is, the initial fixed value can be expressed as:

[0202] S d =f(Prior_Para)

[0203] {Gender,Age,Weight,Height,...}∈Prior_Para

[0204] Wherein, f is an empirical formula obtained by fusing analysis on the physiological parameter.

[0205] Since the physiological parameter is used to determine the initial optimal skewness, the physiological parameter can also be called a priori parameter.

[0206] Since the typical time corresponding to the heavy-Bo incisal point of the target patient corresponding to different physiological parameters is different, determining the initial fixed value based on the physiological parameter can ensure the reliability of the initial fixed value.

[0207] S352, based on the length of the current skew time and the heavy-Bo detection interval, determine the skew state weight corresponding to each time point in the heavy-Bo detection interval.

[0208] The skew state function can be expressed as follows:

[0209]

[0210] Wherein, A is a proportional coefficient, when A=1, the skew state weight between the range 0-1 can be generated, t is the time point. Based on the above formula, the corresponding calculation formula can be determined based on the size relationship between each time point in the heavy-Bo detection interval and the current skew time and T, and then the skew state weight corresponding to each time point in the heavy-Bo detection interval is determined.

[0211] For example, Figure 4c It is shown that Figure 4a The corresponding skew state weight, Figure 5c It is shown that Figure 5a The corresponding skew state weight. By Figure 4c And Figure 5c It can be seen that the skew state weight corresponds to the time of the pulse waveform to be detected, so it can be mapped based on time.

[0212] S36, based on the time point of the optional heavy-Bo incisal point, map the optional heavy-Bo incisal point and the skew state weight to determine the target heavy-Bo incisal point.

[0213] Specifically, the above S36 includes:

[0214] S361, based on the time point of the optional heavy-Bo incisal point, determine the weight corresponding to the time point in the skew state weight to determine the weight of each optional heavy-Bo incisal point.

[0215] S362, determine the optional heavy-Bo incisal point with the largest weight as the target heavy-Bo incisal point.

[0216] The medical device calculates the weight of each optional heavy-Bo incisal point mapped based on the time point, and the point with the largest weight is the target heavy-Bo incisal point.

[0217] For example, Figure 7and Figure 8 The target heavy cut point detected by the heavy cut point detection method provided by the embodiment of the application is shown.

[0218] The heavy cut point detection method provided by the embodiment guarantees the accuracy of the determined target heavy cut point by taking the optional heavy cut point with the largest weight as the target heavy cut point.

[0219] In one specific embodiment of the embodiment, Figure 9a The schematic diagram of the heavy cut point detection method is shown, and the method comprises:

[0220] S101, preprocessing the pulse signal; the preprocessing can be filtering the signal and the like.

[0221] S102, confirming a heavy cut detection interval;

[0222] S103, detecting a slope variation point and / or an extreme value point;

[0223] S104, generating a skewness weight;

[0224] S105, mapping the point with the largest weight as a heavy cut point.

[0225] In another specific embodiment of the embodiment, Figure 9b The schematic diagram of the heavy cut point detection method is shown. Different from Figure 9a In the method, the skewness is updated in real time, and based on this, the method can Figure 9a On the basis of the embodiment shown, S106, i.e., skewness updating, is added.

[0226] In another specific embodiment of the embodiment, Figure 9c The schematic diagram of the heavy cut point detection method is shown. Different from Figure 9a and Figure 9b In the method, the initial fixed value is determined based on the physiological parameter of the target patient, and based on this, the skewness can be updated in real time, i.e., S107, i.e., prior parameter input, is added.

[0227] In some optional embodiments of the embodiment, the pulse signal is represented in the form of a pulse waveform, and the medical device further provides a human-computer interaction interface to correct the target heavy cut point detected automatically. Based on this, the heavy cut point detection method further comprises:

[0228] (1) superimposing and displaying the target heavy cut point on the pulse waveform.

[0229] The number of pulse waveforms displayed on the interface can be set according to actual needs, for example, 5 pulse waveforms are selected on the central station to simultaneously adjust the target inflection points of the pulse waveforms.

[0230] As shown in Figure 10 , 10 pulse waveforms are displayed on the interface, and the target inflection points are automatically determined by the detection method of the inflection point of the pulse wave.

[0231] (2) In response to the adjustment operation on the target inflection point, the adjusted inflection point is determined.

[0232] The user adjusts the target inflection point according to his own experience, and the medical device determines the adjusted inflection point in response to the user's adjustment operation. The adjustment operation can be selected by a mouse, or the user can set the cursor as shown in Figure 10 .

[0233] (3) Based on the position of the adjusted inflection point in the pulse waveform, the adjusted skewness is determined.

[0234] After obtaining the adjusted inflection point, the medical device can determine the position of the adjusted inflection point in the pulse waveform, and can inversely deduce the adjusted skewness.

[0235] (4) Based on the adjusted skewness, the detection of the inflection point of the current pulse waveform is performed.

[0236] (5) The detection result of the inflection point of the current pulse waveform is displayed.

[0237] The detection result of the inflection point of the current pulse waveform is displayed on the interface.

[0238] By adjusting the automatically determined inflection point through human-computer interaction to modify the skewness, the accuracy of the subsequent determined detection result can be ensured.

[0239] As an optional implementation manner of the embodiment, after adjusting the target inflection point, the method further includes: displaying prompt information, the prompt information being used to prompt that the detection of the inflection point is performed based on the adjusted skewness.

[0240] The prompt information can be represented by text or by a countdown. Optionally, the prompt information includes a transition time. Based on this, the step of displaying the prompt information includes:

[0241] (1) In response to a setting operation of the transition time, the transition time is determined.

[0242] (2) display the change of the transition time.

[0243] The medical device is provided with a setting interface for setting the transition time, and the user sets the transition time on the setting interface. The transition time, i.e. the transition time for performing the rebo point detection by using the modified skewness, is displayed as a countdown on the interface, or the real-time length of the transition time is displayed.

[0244] In the embodiment, a rebo notch detection device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described herein. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0245] The embodiment provides a rebo notch detection device, as shown in the drawings, comprising: Figure 11

[0246] The acquisition module 41 is configured to acquire a pulse signal and determine a rebo detection interval in the pulse signal.

[0247] The extraction module 42 is configured to extract a difference sequence corresponding to the rebo detection interval based on a detection step.

[0248] The analysis module 43 is configured to perform minimum value analysis on each point in the difference sequence to determine a slope variation point.

[0249] The filtering module 44 is configured to filter the difference sequence according to the slope variation point to determine a selectable rebo notch point.

[0250] The first determination module 45 is configured to determine a skewness weight corresponding to each time point in the rebo detection interval based on a current skewness.

[0251] The second determination module 46 is configured to map the selectable rebo notch point and the skewness weight based on the time point of the selectable rebo notch point to determine a target rebo notch point.

[0252] The rebo notch detection device in the embodiment is presented in the form of a functional unit, and the unit herein refers to an ASIC circuit, a processor and a memory executing one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0253] Further function descriptions of the above-mentioned modules are the same as those of the corresponding embodiments, and will not be described herein.

[0254] The embodiment of the application also provides a medical device having the above-mentioned Figure 11 ​The illustrated detection device of the heavy Bo incision.

[0255] Please refer to Figure 12 , Figure 12 is a schematic structural diagram of a medical device provided by an optional embodiment of the application. As Figure 12 shown, the medical device can include at least one processor 601, such as a CPU (Central Processing Unit), at least one communication interface 603, a memory 604, and at least one communication bus 602. The communication bus 602 is used to realize the connection and communication between the components. The communication interface 603 can include a display, a keyboard, and the optional communication interface 603 can also include a standard wired interface and a wireless interface. The memory 604 can be a high-speed RAM memory (Random Access Memory) or a non-volatile memory, such as at least one disk memory. The memory 604 can also be at least one storage device located away from the aforementioned processor 601. The processor 601 can be combined with the device described in Figure 11 The memory 604 stores application programs, and the processor 601 calls the program code stored in the memory 604 to execute any of the above method steps.

[0256] The communication bus 602 can be a PCI bus or an EISA bus, etc. The communication bus 602 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 12 only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0257] The memory 604 can include volatile memory, such as random access memory (RAM); the memory can also include non-volatile memory, such as flash memory, a hard disk (HDD) or a solid-state disk (SSD); the memory 604 can also include a combination of the above types of memory.

[0258] The processor 601 can be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.

[0259] The processor 601 can further include a hardware chip. The hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0260] Optionally, the memory 604 is further configured to store program instructions. The processor 601 can invoke the program instructions to implement the detection method of the resection of the rebo.

[0261] The embodiments of the present application also provide a non-transitory computer storage medium, which stores computer executable instructions. The computer executable instructions can execute the detection method of the resection of the rebo in any method embodiment. The storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), a solid-state drive (SSD), or the like. The storage medium can also include a combination of the above-mentioned storage media.

[0262] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes are also within the scope defined by the appended claims.

Claims

1. A method of detecting a Schwalbe's Deletion, characterized by, The method comprises the following steps: acquiring a pulse signal and determining a heavy beat detection interval in the pulse signal; extracting a difference sequence corresponding to the heavy beat detection interval based on a detection step; performing minimum value analysis on each point in the difference sequence to determine a slope variation point; filtering the difference sequence according to the slope variation point to determine a selectable heavy beat notch point, wherein the selectable heavy beat notch point comprises a slope variation point and / or an extreme value pair, and the extreme value pair comprises an extreme value valley and an extreme value peak; determining a skew state weight corresponding to each time point in the heavy beat detection interval based on a current skew degree; mapping the selectable heavy beat notch point and the skew state weight based on a time point of the selectable heavy beat notch point to determine a target heavy beat notch point; wherein the step of performing minimum value analysis on each point in the difference sequence to determine a slope variation point comprises: inquiring the amplitude of each point in the difference sequence to determine a minimum value point with an amplitude greater than zero; determining a target minimum value point with the minimum amplitude in each minimum value point; screening each minimum value point based on the amplitude of the target minimum value point to determine the slope variation point.

2. The method of claim 1, wherein, The step of screening each minimum value point based on the amplitude of the target minimum value point to determine the slope variation point comprises: inquiring a minimum value point with an amplitude greater than a preset multiple of the minimum amplitude in each minimum value point; deleting the inquired minimum value point from each minimum value point to determine the slope variation point.

3. The method of claim 1, wherein, The step of extracting the difference sequence corresponding to the heavy beat detection interval based on the detection step comprises: determining the detection step by using the length of the heavy beat detection interval; extracting the difference sequence corresponding to the heavy beat detection interval based on the detection step.

4. The method of claim 3, wherein, The step of determining the detection step by using the length of the heavy beat detection interval comprises: comparing the length of the heavy beat detection interval with a preset length range to determine a target preset length range; determining the detection step as a step length corresponding to the target preset length range.

5. The method of claim 1, wherein, The step of filtering the difference sequence according to the slope variation point to determine a selectable heavy beat notch point comprises: performing extreme value pair analysis on the difference sequence to determine an extreme value pair; determining the slope variation point and the extreme value pair in the difference sequence as the selectable heavy beat notch point.

6. The method of claim 1, wherein, The step of determining a skew state weight corresponding to each time point in the heavy beat detection interval based on a current skew degree comprises: determining a current skew time based on the current skew degree and the length of the heavy beat detection interval; determining the skew state weight corresponding to each time point in the heavy beat detection interval based on the current skew time and the length of the heavy beat detection interval.

7. The method of claim 6, wherein, The current skew degree is an initial fixed value.

8. The method of claim 7, wherein, The determination method of the current skew degree comprises: acquiring a previous skew degree and a historical skew degree before the previous skew degree; determining the current skew degree based on the weighted previous skew degree and the historical skew degree.

9. The method of claim 8, wherein, The determination method of the historical skew degree comprises: performing signal quality analysis on each historical pulse signal and acquiring a skew degree of a historical pulse signal with a signal quality meeting a requirement; Statistical analysis is performed on the obtained skewness to determine the historical skewness.

10. The method according to claim 7 or 8, characterized in that, The initial fixed value is determined in the following manner: Obtain physiological parameters of a target patient, the physiological parameters including at least one of gender, age, weight, and height; Determine the initial fixed value based on the physiological parameters.

11. The method of claim 1, wherein, The target re-Boisot inflection point is determined by mapping the optional re-Boisot inflection points to the skewness weight based on the time points of the optional re-Boisot inflection points, including: Determine the weight corresponding to the time point in the skewness weight based on the time point of the optional re-Boisot inflection point, to determine the weight of each optional re-Boisot inflection point; Determine the target re-Boisot inflection point as the optional re-Boisot inflection point with the largest weight.

12. The method of claim 1, wherein, The pulse signal is represented by a pulse waveform, and the method further includes: Superimpose display of the target re-Boisot inflection point on the pulse waveform; Determine an adjusted re-Boisot inflection point in response to an adjustment operation on the target re-Boisot inflection point; Determine an adjusted skewness based on the position of the adjusted re-Boisot inflection point in the pulse waveform; Detect the re-Boisot inflection point of the current pulse waveform based on the adjusted skewness; Display the detection result of the re-Boisot inflection point of the current pulse waveform.

13. The method of claim 12, wherein, After adjusting the target re-Boisot inflection point, the method further includes: Display prompt information, the prompt information prompting that the detection of the re-Boisot inflection point is based on the adjusted skewness.

14. The method of claim 13, wherein, The prompt information includes a transition time, and the display of the prompt information includes: Determine the transition time in response to a setting operation of the transition time; Display the change of the transition time.

15. An apparatus for detecting a Schwalbe's dehiscence, comprising: Comprise: An acquisition module is configured to acquire a pulse signal and determine a re-Boisot detection interval in the pulse signal; An extraction module is configured to extract a difference sequence corresponding to the re-Boisot detection interval based on a detection step; An analysis module is configured to perform minimum value analysis on each point in the difference sequence to determine a slope variation point; A filtering module is configured to filter the difference sequence according to the slope variation point to determine optional re-Boisot inflection points, the optional re-Boisot inflection points including slope variation points and / or extreme value pairs, the extreme value pairs including extreme value valleys and extreme value peaks; A first determination module is configured to determine a skewness weight corresponding to each time point in the re-Boisot detection interval based on a current skewness; A second determination module is configured to determine a target re-Boisot inflection point by mapping the optional re-Boisot inflection points to the skewness weight based on the time points of the optional re-Boisot inflection points; The minimum value analysis on each point in the difference sequence to determine a slope variation point includes: Query the amplitudes of the points in the difference sequence to determine minimum value points with amplitudes greater than zero; Determine a target minimum value point with the smallest amplitude in each minimum value point; Filter each minimum value point based on the amplitude of the target minimum value point to determine the slope variation point.

16. A medical device, characterized by Comprise: A memory and a processor, which are connected in communication with each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for detecting a rebo cutting notch according to any one of claims 1-14.

17. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to perform the method for detecting a rebo cutting notch according to any one of claims 1-14.

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