CN115633943A - Lower limb venous thrombosis early warning evaluation system based on multi-mode continuous monitoring signals - Google Patents
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Abstract
The invention provides a lower limb venous thrombosis early warning evaluation system based on multi-mode continuous monitoring signals, and relates to the technical field of wearable medical health monitoring. The system acquires temperature signals through a lower limb area signal acquisition unit and acquires blood flow signals of a lower limb area in two ways of dynamic measurement and static measurement; performing adaptive wavelet denoising processing and characteristic passband component extraction on the blood flow signals of the lower limb region through a signal decomposition processing unit; evaluating the lower limb thrombus risk state and positioning the abnormal position by an early DVT risk evaluation unit; the lower limb thrombus risk state evaluation is realized by using a characteristic frequency division method or an SVM machine learning method based on a characteristic frequency band frame; and if the DVT risk exists after evaluation, the processed blood flow signals and the acquired temperature signals are respectively positioned and divided by using a longitudinal positioning method and a transverse positioning method, and the division results are further subjected to fusion analysis, so that the abnormal position of the lower limb where the DVT possibly exists is accurately positioned.
Description
Technical Field
The invention relates to the technical field of wearable medical health monitoring, in particular to a lower limb venous thrombosis early warning evaluation system based on multi-mode continuous monitoring signals.
Background
The formation of Deep Venous Thrombosis (DVT) is a Venous reflux disorder caused by abnormal clotting of blood in the Deep veins, which often occurs in the lower extremities. The detection methods currently used clinically can be mainly classified into invasive and non-invasive methods, wherein the non-invasive method is usually dual-function ultrasonic detection, but the method is not suitable for primary health care and requires highly skilled physicians. Plethysmography and D-dimer methods are commonly used for preliminary screening to avoid the need to operate more complex duplex ultrasound exams. Although plethysmography is a low cost and relatively fast method, it requires the patient to perform a certain activity program during the measurement process, but for example, patients with open wounds are not suitable for such procedures, and the above method cannot be used for early DVT diagnosis.
The patents on the diagnosis of venous thrombosis of lower limbs available at present can be mainly divided into the following two categories: (1) Examples of acquisition methods and post-processing methods based on magnetic resonance imaging. From the signal acquisition perspective, there are examples of blood flow signal suppression by using radio frequency pulses of different parameters to distinguish vessel wall, blood flow, thrombus signal (patent No. CN 201711366717.8); there are also examples proposed using a new dielectric material pad placed between the radio-frequency coil and the tissue to be measured, in order to improve the homogeneity of the magnetic resonance signal (CN 201611233829.1). From the perspective of a signal post-processing method, a thrombus detection method using a YOLOv3 network based on a deep learning theory is proposed in an example (patent number: CN 202010387879.5), and an image segmentation algorithm based on the deep learning theory is proposed in an example to detect and locate thrombus on an image (patent number: CN 201811365954.7); image-based methods, however, are not sensitive to early thrombus formation, the acquisition equipment itself is bulky and requires specialized personnel, which limits their application in early DVT detection. (2) An example of DVT detection based on the principle of light transmission discloses a device based on a light transmission and detection system for providing an indication of the presence or absence of DVT and diabetic peripheral neuropathy, but there is room for improvement in using the difference of signals of both legs as a method for judging the presence or absence of DVT, because the difference of signals of both legs may also be caused by physiological reasons of human body, and the difference cannot be used as a sufficient condition for deep venous thrombosis (patent No. CN 200580035023.5).
In the research on evaluating DVT by plethysmography, the basic principle is that the blood volume of the limb changes by the limb movement, and the size and volume of the limb change, but only the whole limb blood volume regulation function can be evaluated, and the blood flow condition of the limb can be evaluated.
The photoplethysmography developed on the basis of the plethysmography uses a PPG sensor to monitor changes in microcirculation blood flow, i.e., local blood volume changes, instead of measuring the entire limb, and further evaluates the condition of the lower limb blood flow. However, the early detection of DVT by PPG method has several disadvantages: 1) The method usually requires the patient to perform a certain course of action, on one hand, the patient with open wound cannot perform such operation, on the other hand, the relative displacement of the sensors due to the movement of the limbs can cause the artifacts; 2) Plethysmography and related methods are traditionally used to detect lower extremity arterial related diseases, but DVT is a venous disease that does not achieve good results; 3) Various lower limb circulation diseases can cause the change of the whole blood volume of limbs, so that the blood flow of regional microcirculation is changed, the whole condition cannot be completely reflected only by measuring a single local position, and a suspicious focus region cannot be positioned.
Disclosure of Invention
The invention aims to solve the technical problem of providing a lower limb venous thrombosis early warning evaluation system based on a multi-mode continuous monitoring signal to realize early warning evaluation of lower limb venous thrombosis.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:the lower limb venous thrombosis early warning evaluation system based on the multi-mode continuous monitoring signals comprises a lower limb area signal acquisition and transmission unit, a signal decomposition processing unit and an early DVT risk evaluation unit; the lower limb area signal acquisition unit is used for measuring blood flow signals and temperature signals of a lower limb area; the signal decomposition processing unit carries out N on the blood flow signals of the lower limb area m Performing order wavelet denoising processing and characteristic passband component extraction; the early DVT risk assessment unit has a lower limb thrombus risk state assessment function and an abnormal position positioning function; on the basis of the processing result of the signal decomposition processing unit, aiming at blood flow signals acquired by two measuring modes of dynamic measurement and static measurement, respectively using a feature scoring method based on a feature frequency band frame and an SVM machine learning method to evaluate the possibility that the lower limb suffers from DVT; and if DVT risk exists after evaluation, positioning and dividing the processed blood flow signals and the acquired temperature signals by using a longitudinal positioning method and a transverse positioning method respectively, and further performing fusion analysis on division results to position abnormal positions of the lower limbs where DVT possibly exists.
Preferably, the lower limb area signal acquisition and transmission unit comprises a multi-mode signal acquisition probe with a fixing device and a main control box; the multi-mode signal acquisition probes comprise a PPG probe and a temperature probe, and the number of the probes is adjusted according to measurement requirements; the PPG probe is used for acquiring blood flow signals of a local area of the lower limb; the temperature probe is used for monitoring a temperature signal of a local area of the lower limb; the main control box is a cuboid box with a plurality of signal input interfaces, a main control device is arranged in the main control box, and the main control box is provided with an air charging and discharging interface, a multi-mode signal acquisition probe input interface and a memory card socket; the inflation and deflation interface is connected with the cuff with the air bag and used for dynamically measuring the lower limb area; the main control device is installed in the main control box, so that data collected by the multi-mode signal collecting probe can be temporarily stored locally and transmitted to the early DVT risk assessment unit in a wired or wireless mode.
Preferably, the lower limb area signal acquisition and transmission unit controls inflation and deflation of the valve with the airbag cuff through a motor to realize lower limb pressurization so as to perform dynamic measurement of PPG signals under different pressurization; the static measurement is realized by continuously monitoring the selected area for a long time under the condition of no external pressure of the lower limb.
Preferably, the signal decomposition processing unit performs N on each local region blood flow signal of the lower limb acquired by the limb region signal acquisition and transmission unit through a characteristic frequency band frame m The method comprises the following steps of (1) order wavelet denoising processing and characteristic passband component extraction, and specifically comprises the following steps:
(1) Collecting a ppg signal as a reference signal through a fingertip to obtain a characteristic frequency band frame; the characteristic frequency band frame is composed of three characteristic pass bands, and the center frequencies of the three characteristic pass bands are respectively f P1 、f P2 、f P3 Wherein f is P1 [0.8Hz,1.5Hz ] as reference signals]Frequency, f, corresponding to peak points in a frequency interval P2 Is [2 x f P1 -0.5Hz,2*f P1 +0.5Hz]Frequency, f, corresponding to peak points in a frequency interval P3 Is [3 x f P1 -0.5Hz,3*f P1 +0.5Hz]The frequency corresponding to the peak point in the frequency interval; from each center frequency f P1 、f P2 、f P3 The lower limit cut-off frequency of the three characteristic pass bands is respectively determined according to the 3db bandwidth principle in the adjacent frequency domain
And upper cut-off frequency
Wherein m is a pass band ordinal number, and m =1, 2 and 3, to obtain three characteristic pass bands;
(2) Based on the characteristic frequency band frame, based on the upper limit cut-off frequency of the first pass band
Self-adaptive determination of wavelet denoising order N m And carrying out N on blood flow signals of each local area of the lower limb m Denoising with order wavelet;
(3) Obtaining a pass N based on the characteristic frequency band framework m Each characteristic passband component corresponding to the signal after the wavelet de-noising processing of the order
(4) Based on each characteristic passband component
And obtaining a time domain denoising signal of each local region of the lower limb.
Preferably, the cutoff frequency is based on the upper limit of the first pass band
Adaptive determination of wavelet noise reduction order N m The specific method comprises the following steps:
setting the energy of the first characteristic pass band not to be influenced by noise, extracting the characteristic pass band components of the blood flow signals of each local region of the lower limb to obtain the upper limit cut-off frequency of the first pass band
Preferably, the order of the blood flow signal of each local area of the lower limb is N by using a tightly-supported set orthogonal wavelet layer Wavelet decomposition of (2), when the lower bound f of the band affected by the wavelet decomposition bw And the upper limit cut-off frequency of the first pass band
The following conditions are satisfied:
in this case the order N layer Maximum value N that can be obtained m I.e. the selected order of wavelet de-noising, wherein Nm is influenced by the signal sampling frequency Fs, and the selection of Fs is at least 1000Hz.
Preferably, the early DVT risk assessment unit is used for assessing the possibility of the lower limb suffering from DVT by the following specific method:
and selecting different methods according to the measurement mode to evaluate the state of the lower limb.
In a dynamic measurement mode, calculating the proportional response scores of each local area of the lower limb by using the characteristic pass band components based on the characteristic frequency band frame, comparing each proportional response score with a threshold value determined through experiments, and if the proportional response scores of more than two areas exceed the threshold value, judging that the user is suspected to suffer from early DVT; the calculation of the proportional response score for each local region of the lower limb is shown in the following formula:
wherein the content of the first and second substances,
denotes the proportional response fraction, c 1 、c 2 、c 3 Correlation coefficients of the characteristic passband components of the denoised blood flow signals of each local area of the lower limb and the characteristic passband components of the reference signal are respectively obtained;
under a static measurement mode, firstly, a sliding window is adopted to divide a time domain de-noising signal obtained by processing of a signal decomposition processing unit to obtain a plurality of groups of signals; classifying the single group of signals by an SVM method based on Riemann manifold theory optimization, namely judging whether the signals suffer from early DVT; then, the classification results of the multiple groups of signals are counted to obtain the average probability P of each classification result dvt The calculation formula is as follows:
wherein N is the total number of sliding window divisions, N dvt The number of cases with early DVT was judged.
Preferably, the early DVT risk assessment unit locates an abnormal position of the lower limb where DVT may exist by analyzing and processing the blood flow signal and the temperature signal, specifically:
firstly, dividing a lower limb area, and dividing the lower limb into six areas in advance; selecting a maximum possible abnormal region of the lower limb from the six regions according to the fusion analysis of the longitudinal positioning result and the transverse positioning result;
judging whether the abnormal position is in a proximal region or a distal region in six regions of the lower limb according to the time-domain blood flow signals processed by the signal decomposition processing unit, namely longitudinally positioning;
according to the temperature signals, transverse comparison analysis is carried out on the abnormal positions in six areas of the lower limb, and the area with the highest temperature is the transverse maximum possible abnormal area, namely transverse positioning;
and performing fusion analysis on the judgment results of the lower limbs in the longitudinal direction and the transverse direction, and taking the region which accords with the longitudinal positioning judgment result and the transverse judgment result from the six regions as the maximum possible abnormal region of the lower limbs, thereby realizing positioning.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: according to the lower limb venous thrombosis early warning evaluation system based on the multi-mode continuous monitoring signals, the early DVT of the lower limb without obvious visual symptoms is detected by a non-invasive blood flow monitoring method, and a static measurement mode can be used for continuously monitoring the lower limb for a long time so as to evaluate the blood flow condition of the lower limb; the dynamic measurement mode is used for testing the lower limb blood flow response characteristic under the action of external pressure so as to evaluate the state of the lower limb; based on the multi-modal signals, the abnormal part is located.
Drawings
Fig. 1 is a block diagram of a lower limb venous thrombosis early warning and evaluating system based on a multi-modal continuous monitoring signal according to an embodiment of the present invention;
fig. 2 is a flowchart of noise reduction processing and characteristic passband component extraction on a blood flow signal based on a characteristic frequency band frame according to an embodiment of the present invention;
FIG. 3 is a flowchart of wavelet order threshold denoising based on characteristic pass bands according to an embodiment of the present invention;
fig. 4 is a flowchart of an early DVT risk assessment method based on dynamic measurement data according to an embodiment of the present invention;
fig. 5 is a flowchart of an early DVT risk assessment method based on static measurement data according to an embodiment of the present invention;
FIG. 6 is a flowchart of a method for locating a region based on multi-modal signals according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a PPG probe and a temperature sensor placement area provided in an embodiment of the present invention, where (a) is the PPG probe placement area, and (b) is the temperature sensor placement area;
fig. 8 is a waveform diagram of a local blood flow signal of a lower limb and a blood flow signal processed by a wavelet denoising method selected by an order threshold according to an embodiment of the present invention, where (a) is a local blood flow signal waveform of a lower limb, and (b) is a blood flow signal waveform processed by a wavelet denoising method selected by an order threshold;
fig. 9 is a schematic diagram illustrating the division of the main part of the lower leg according to the embodiment of the present invention.
In the figure, 7101-7103 are three acquisition positions of PPG signals of three local areas of the lower limb, and 7201-7203 are three acquisition areas of temperature signals; 9301-9306 are six regions divided by the main part of the lower leg.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In this embodiment, the lower limb venous thrombosis early warning and evaluation system based on the multi-modal continuous monitoring signal, as shown in fig. 1, includes a lower limb area signal acquisition unit, a signal decomposition processing unit, and an early DVT risk assessment unit; the lower limb area signal acquisition unit is used for measuring blood flow signals and temperature signals of a lower limb area; the signal decomposition processing unit carries out N on the blood flow signals of the lower limb area m Performing order wavelet denoising processing and characteristic passband component extraction; the early DVT risk assessment unit has a function of assessing the thrombus risk state of the lower limbs and a function of locating an abnormal position (suspected lesion area); processing result base in signal decomposition processing unitOn the basis, aiming at two measurement modes of dynamic measurement and static measurement of blood flow signals, a feature scoring method based on a feature frequency band frame and an SVM machine learning method are respectively used for evaluating the possibility that the lower limbs suffer from DVT; the blood flow condition of the lower limbs is judged by dynamically measuring the recovery time of the partial region blood recharging curve during deflation; if the risk of DVT is evaluated, the abnormal position of the lower limb where DVT possibly exists is further located by respectively using a longitudinal locating method and a transverse locating method through the blood flow signal and the temperature signal which are processed by the signal decomposition processing unit.
In this embodiment, the lower limb area signal acquisition and transmission unit comprises a multi-mode signal acquisition probe with a fixing device and a main control box; the multi-mode signal acquisition probes comprise a PPG probe and a temperature probe, and the number of the probes is adjusted according to measurement requirements; the PPG probe is used for acquiring blood flow signals of a local area of the lower limb and is fixed by adopting buckling belts, fixing cards and other modes; the temperature probe is used for monitoring a temperature signal of a local area of the lower limb; the main control box is a cuboid box with a plurality of signal input interfaces, a main control device is arranged in the main control box, and the main control box is provided with an air charging and discharging interface, a multi-mode signal acquisition probe input interface and a storage card socket; the inflation and deflation interface is connected with the cuff with the air bag through the leather hose and is used for realizing dynamic measurement of the lower limb area; the main control device is arranged in the main control box, so that data acquired by the multi-mode signal acquisition probe are locally temporarily stored and transmitted to the early DVT risk assessment unit in a wired or wireless mode; meanwhile, the lower limb area signal acquisition and transmission unit realizes the dynamic acquisition of blood flow signals by pressurizing the lower limbs through the inflation and deflation of the air bag sleeve belt by the motor.
In this embodiment, the signal decomposition processing unit performs N on the blood flow signals of each local region of the lower limb acquired by the lower limb region signal acquisition and transmission unit through the characteristic frequency band frame m Step wavelet denoising processing and characteristic passband component extraction, as shown in fig. 2, the specific method is as follows:
(1) Acquiring a fingertip ppg signal as a reference signal to obtain a characteristic frequency band frame; the characteristic frequency band frame is composed of three characteristic pass bandsThe center frequencies of the three characteristic pass bands are respectively f P1 、f P2 、f P3 Wherein f is P1 As reference signal, [0.8Hz,1.5Hz]Frequency, f, corresponding to peak points in a frequency interval P2 Is [2 x f P1 -0.5Hz,2*f P1 +0.5Hz]Frequency, f, corresponding to peak points in a frequency interval P3 Is [3 x f P1 -0.5Hz,3*f P1 +0.5Hz]The frequency corresponding to the peak point in the frequency interval; from each center frequency f P1 、f P2 、f P3 The lower limit cut-off frequency of the three characteristic pass bands is respectively determined according to the 3db bandwidth principle in the adjacent frequency domain
And upper cut-off frequency
Wherein m is the number of pass bands, and m =1, 2 and 3, to obtain three characteristic pass bands;
(2) Based on the characteristic frequency band frame, based on the upper limit cut-off frequency of the first pass band
Adaptive determination of wavelet de-noising order N based on, but not limited to, tight-set orthogonal wavelets m And carrying out N on blood flow signals of each local area of the lower limb m Denoising by using an order wavelet;
(3) Based on the characteristic frequency band frame, obtaining a pass N m Each characteristic passband component corresponding to the signal after the wavelet de-noising processing of the order
(4) Based on each characteristic passband component
And obtaining a time domain denoising signal of each local region of the lower limb.
In this exampleBased on the upper cut-off frequency of the first pass band
Adaptive determination of wavelet noise reduction order N m As shown in fig. 3, the specific method is as follows:
setting the energy of the first characteristic pass band not to be influenced by noise, extracting the characteristic pass band components of the blood flow signals of each local area of the lower limb to obtain the upper limit cut-off frequency of the first pass band
Preferably, the order of the blood flow signal of each local area of the lower limb is N by using a tightly-supported set orthogonal wavelet layer Wavelet decomposition of (2), when the lower bound f of the band affected by the wavelet decomposition bw And the upper limit cut-off frequency of the first pass band
The following conditions are satisfied:
at this time, the order N layer Maximum value N that can be obtained m The order selected for wavelet denoising is obtained;
in this embodiment, a specific method for evaluating the possibility that the lower limb suffers from DVT by the early DVT risk evaluation unit is as follows:
selecting different methods according to the measurement mode to evaluate the state of the lower limbs;
in the dynamic measurement mode, as shown in fig. 4, the proportional response scores of the respective local regions of the lower limb are calculated using the characteristic pass band components based on the characteristic band frame, and the respective proportional response scores are compared with the threshold value determined by the experiment, wherein the threshold value is obtained by the average distribution of the experimentally acquired data. If the proportional response scores of more than two areas exceed the threshold value, judging that the user is suspected to have the risk of early DVT; the calculation of the proportional response score for each local region of the lower limb is shown in the following formula:
wherein the content of the first and second substances,
denotes the proportional response fraction, c 1 、c 2 、c 3 Respectively obtaining correlation coefficients of characteristic passband components of the blood flow signals of each local region of the lower limb after noise reduction and characteristic passband components of the reference signals;
in the static measurement mode, as shown in fig. 5, firstly, a sliding window is adopted to segment the time domain denoising signal obtained by processing of the signal decomposition processing unit to obtain a plurality of groups of signals; classifying the single group of signals by an SVM method based on Riemann manifold theory optimization, namely judging whether the signals suffer from early DVT; then, the classification results of the multiple groups of signals are counted to obtain the average probability P of each classification result dvt The calculation formula is as follows:
wherein N is the total number of sliding window divisions, N dvt The number of times of having early DVT is judged;
in this embodiment, the early DVT risk assessment unit locates an abnormal position where DVT may exist in the lower limb by using the blood flow signal and the temperature signal, as shown in fig. 6, specifically:
first, the lower limb region is divided, and the main part of the lower leg is divided into six regions in advance. And selecting the maximum possible abnormal region of the lower limb from the six regions according to the fusion analysis of the longitudinal positioning result and the transverse positioning result.
Judging whether the abnormal position is in a proximal region or a distal region in six regions of the lower limb according to the time-domain blood flow signals processed by the signal decomposition processing unit, namely longitudinally positioning;
according to the temperature signals, transverse comparison analysis is carried out on the abnormal positions in six areas of the lower limb, and the area with the highest temperature is the transverse maximum possible abnormal area, namely transverse positioning;
and performing fusion analysis on the judgment results of the lower limbs in the longitudinal direction and the transverse direction, and taking the region which accords with the longitudinal positioning judgment result and the transverse judgment result from the six regions as the maximum possible abnormal region of the lower limbs, thereby realizing positioning.
In this embodiment, three positions where the PPG probes are placed on the lower limb are shown in fig. 7 (a), a placement region where the temperature sensor is placed on the lower limb is shown in fig. 7 (b), and three local region blood flow signals sig on the lower limb are acquired by the three PPG probes 1 (t)、sig 2 (t)、sig 3 (t) another PPG probe is used for acquiring fingertip signals to obtain a reference signal sig 4 (t); obtaining a temperature signal tm by using 12 temperature sensors 1 (t)～tm 12 (t) where tm 1 (t)～tm 4 (t) measurement of region I, tm in FIG. 7 (b) 5 (t)～tm 8 (t) measurement of region II, tm in FIG. 7 (b) 9 (t)～tm 12 (t) region III in FIG. 7 (b) was measured. In this embodiment, a blood flow signal of a local region of a lower limb acquired by a PPG probe and a blood flow signal waveform processed by a wavelet denoising method selected by an order threshold are shown in fig. 8. When signal acquisition is carried out on the lower limbs on one side, if dynamic acquisition is carried out, the inflation control function on the main control box is needed, the cuff is controlled to actively pressurize the legs, and 50mmHg pressure or 100mmHg pressure is respectively applied according to a selected high-pressure mode or low-pressure mode; in the pressurizing process, the cuff pressure change curve and other data acquisition probes are synchronously acquired. The main control box plays a role in storing and transmitting data and controlling the cuff inflation mode.
In the present embodiment, the band components are based on the respective characteristics
Obtaining time domain de-noising signals of three local areas of the lower limb as
Then the proportional response fraction of the three local positions is calculated and determined through experimentsThreshold value TH of PRS1 、TH PRS2 、TH PRS3 Comparing, and if more than two parts exceed the threshold value, judging that the disease is suspected to be ill;
in this embodiment, the most likely abnormal area of the lower limb is located on the basis of the determination of the disease. First, on the basis of three regions divided in the lateral direction during temperature measurement, division based on the proximal and distal regions in the longitudinal direction is added, and the main part of the lower leg is divided into six regions in total, as shown in fig. 9. And selecting the maximum possible abnormal area from the six areas of the lower limb according to the fusion analysis of the longitudinal positioning result and the transverse positioning result.
In this embodiment, a specific method for performing longitudinal positioning analysis on the lower limb abnormal position in six regions according to the three measured local blood flow signals of the lower limb local area includes:
noise-reduced signal of lower limb inner side position
Get
The waveform is adjacent to the peak point interval by a block and will be within the block
Normalization is carried out to obtain Normal 1 (t)、Normal 3 (t), then obtaining the average similarity of two paths of synchronous signals
If the two signals are similar, the potential lesion may be present in the proximal region, i.e., the 9301-9303 region as shown in FIG. 9. If the difference between the signals is large, the potential lesion may be located in the distal region, i.e., the 9304-9306 regions as shown in FIG. 9. Based on the above method, the longitudinal maximum possible abnormal area is determined. Normal mail 1 (t)、Normal 3 (t) and
the specific calculation of (a) is as follows:
wherein, { t n Is at the signal
Time coordinate of peak point, N, extracted in order peak To signal within the analysis range
The number of peak points included in (1).
In this embodiment, a specific method for performing lateral positioning analysis on the abnormal position of the lower limb according to the temperature signal is as follows:
taking the average temperature TM of four temperature sensors in each temperature signal acquisition area I, II and III 1 、TM 2 、TM 3 As the temperature of each zone, the zone with the highest temperature is the maximum possible abnormal zone in the lateral direction. As shown in fig. 9, TM 1 Maximum time means that the 9301 and 9304 regions are selected as the transverse most probable abnormal regions, TM 2 Maximum time means that the lateral most probable abnormal region (TM) of 9302 and 9305 regions is selected 3 When the maximum value is reached, the 9303 and 9306 regions are selected to have the maximum possible abnormal region; TM 1 、TM 2 、TM 3 Is calculated as follows:
TM 1 ＝average(tm 1 (t)+tm 2 (t)+tm 3 (t)+tm 4 (t))
TM 2 ＝average(tm 5 (t)+tm 6 (t)+tm 7 (t)+tm 8 (t))
TM 3 ＝average(tm 9 (t)+tm 10 (t)+tm 11 (t)+tm 12 (t))
and performing fusion analysis on the judgment results of the lower limbs in the longitudinal direction and the transverse direction, and taking a region which accords with the longitudinal positioning judgment result and the transverse judgment result, wherein the region is the most possible abnormal region of the lower limbs, so that the positioning is realized.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.
Claims (7)
1. The utility model provides a lower limbs vein thrombosis early warning evaluation system based on multimode continuous monitoring signal which characterized in that: the lower limb area signal analysis and transmission system comprises a lower limb area signal acquisition and transmission unit, a signal decomposition processing unit and an early DVT risk assessment unit; the lower limb area signal acquisition unit is used for measuring blood flow signals and temperature signals of a lower limb area; the signal decomposition processing unit carries out N on the blood flow signals of the lower limb area m Performing order wavelet denoising processing and characteristic passband component extraction; the early DVT risk assessment unit has a lower limb thrombus risk state assessment function and an abnormal position positioning function; on the basis of the processing result of the signal decomposition processing unit, aiming at blood flow signals acquired by two measuring modes of dynamic measurement and static measurement, respectively using a feature scoring method based on a feature frequency band frame and an SVM machine learning method to evaluate the possibility that the lower limb suffers from DVT; if DVT risk exists after evaluation, the processed blood flow signals and the acquired temperature signals are respectively positioned and divided by a longitudinal positioning method and a transverse positioning method, and the division results are further subjected to fusion analysis, so that abnormal positions of DVT possibly existing in the lower limbs are further subjected toAnd (6) positioning.
2. The lower limb venous thrombosis early warning and evaluating system based on the multi-modal continuous monitoring signal as claimed in claim 1, is characterized in that: the lower limb area signal acquisition and transmission unit comprises a multi-mode signal acquisition probe with a fixing device and a main control box; the multi-mode signal acquisition probes comprise a PPG probe and a temperature probe, and the number of the probes is adjusted according to measurement requirements; the PPG probe is used for acquiring blood flow signals of a local area of the lower limb; the temperature probe is used for monitoring a temperature signal of a local area of the lower limb; the main control box is a cuboid box with a plurality of signal input interfaces, a main control device is arranged in the main control box, and the main control box is provided with an air charging and discharging interface, a multi-mode signal acquisition probe input interface and a storage card socket; the inflation and deflation interface is connected with the cuff with the air bag and used for realizing dynamic measurement of the lower limb area; the main control device is installed in the main control box, so that data collected by the multi-mode signal collecting probe can be temporarily stored locally and transmitted to the early DVT risk assessment unit in a wired or wireless mode.
3. The lower limb venous thrombosis early warning and evaluating system based on the multi-modal continuous monitoring signal as claimed in claim 2, is characterized in that: the lower limb area signal acquisition and transmission unit is used for pressurizing the lower limbs by controlling the inflation and deflation of the valve to the air bag sleeve belt through the motor so as to dynamically measure PPG signals under different pressures; the static measurement is realized by continuously monitoring the selected area for a long time under the condition of no external pressure of the lower limb.
4. The lower limb venous thrombosis early warning and evaluating system based on the multi-modal continuous monitoring signal is characterized in that: the signal decomposition processing unit carries out N on each local area blood flow signal of the lower limb acquired by the limb area signal acquisition and transmission unit through a characteristic frequency band frame m The method comprises the following steps of (1) order wavelet denoising processing and characteristic passband component extraction, and specifically comprises the following steps:
(1) Collecting a ppg signal as a reference signal through a fingertip to obtain a characteristic frequency band frame;the characteristic frequency band frame is composed of three characteristic pass bands, and the center frequencies of the three characteristic pass bands are respectively f P1 、f P2 、f P3 Wherein f is P1 As reference signal, [0.8Hz,1.5Hz]Frequency, f, corresponding to peak points in a frequency interval P2 Is [2 x f P1 -0.5Hz,2*f P1 +0.5Hz]Frequency, f, corresponding to peak points in a frequency interval P3 Is [3 x f P1 -0.5Hz,3*f P1 +0.5Hz]The frequency corresponding to the peak point in the frequency interval; from each center frequency f P1 、f P2 、f P3 The lower limit cut-off frequency of the three characteristic pass bands is respectively determined according to the 3db bandwidth principle in the adjacent frequency domain
And upper cut-off frequency
Wherein m is a pass band ordinal number, and m =1, 2 and 3, to obtain three characteristic pass bands;
(2) Based on the characteristic frequency band frame, based on the upper limit cut-off frequency of the first pass band
Adaptive determination of wavelet noise reduction order N m And carrying out N on blood flow signals of each local area of the lower limb m Denoising with order wavelet;
(3) Obtaining a pass N based on the characteristic frequency band framework m Each characteristic passband component corresponding to the signal after the wavelet de-noising processing of order
(4) Based on each characteristic passband component
Obtaining each local area of the lower limbThe time domain de-noised signal of (1).
5. The system for early warning and evaluating the lower limb venous thrombosis based on the multi-modal continuous monitoring signal according to claim 4, characterized in that: the cutoff frequency is based on the upper limit of the first pass band
Adaptive determination of wavelet noise reduction order N m The specific method comprises the following steps:
setting the energy of the first characteristic pass band not to be influenced by noise, extracting the characteristic pass band components of the blood flow signals of each local region of the lower limb to obtain the upper limit cut-off frequency of the first pass band
Preferably, the order of the blood flow signal of each local area of the lower limb is N by using a tightly-supported set orthogonal wavelet layer Wavelet decomposition of (2), when the lower bound f of the band affected by the wavelet decomposition bw And the upper limit cut-off frequency of the first pass band
The following conditions are satisfied:
at this time, the order N layer Maximum value N that can be obtained m I.e. the selected order of wavelet de-noising, wherein Nm is influenced by the signal sampling frequency Fs, and the selection of Fs is at least 1000Hz.
6. The system for early warning and evaluating the lower limb venous thrombosis based on the multi-modal continuous monitoring signal according to claim 5, characterized in that: the specific method for evaluating the possibility of the lower limb suffering from DVT by the early DVT risk evaluation unit comprises the following steps:
selecting different methods according to the measurement mode to evaluate the state of the lower limbs;
in a dynamic measurement mode, calculating the proportional response scores of each local area of the lower limb by using the characteristic pass band components based on the characteristic frequency band frame, comparing each proportional response score with a threshold value determined through experiments, and if the proportional response scores of more than two areas exceed the threshold value, judging that the user is suspected to suffer from early DVT; the calculation of the proportional response score for each local region of the lower limb is shown in the following formula:
wherein the content of the first and second substances,
denotes the proportional response fraction, c 1 、c 2 、c 3 Respectively obtaining correlation coefficients of characteristic passband components of the blood flow signals of each local region of the lower limb after noise reduction and characteristic passband components of the reference signals;
under a static measurement mode, firstly, a sliding window is adopted to divide a time domain de-noising signal obtained by processing of a signal decomposition processing unit to obtain a plurality of groups of signals; classifying the single group of signals by an SVM (support vector machine) method based on Riemann manifold theory optimization, namely judging whether the early DVT (dynamic virtual tool) exists; then, the classification results of the multiple groups of signals are counted to obtain the average probability P of each classification result dvt The calculation formula is as follows:
wherein N is the total number of sliding window segmentation, N dvt The number of cases with early DVT was judged.
7. The lower limb venous thrombosis early warning and evaluating system based on the multi-modal continuous monitoring signal as claimed in claim 6, is characterized in that: the early DVT risk assessment unit is used for positioning the abnormal position of the lower limb where DVT possibly exists by analyzing and processing blood flow signals and temperature signals, and specifically comprises the following steps:
firstly, dividing a lower limb area, and dividing the lower limb into six areas in advance; selecting a region with the maximum possible abnormality of the lower limbs from the six regions according to the fusion analysis of the longitudinal positioning result and the transverse positioning result;
judging whether the abnormal position is in a proximal region or a distal region in six regions of the lower limb according to the time-domain blood flow signals processed by the signal decomposition processing unit, namely longitudinally positioning;
according to the temperature signals, transverse comparison analysis is carried out on the abnormal positions in six areas of the lower limb, and the area with the highest temperature is the transverse maximum possible abnormal area, namely transverse positioning;
and performing fusion analysis on the judgment results of the lower limbs in the longitudinal direction and the transverse direction, and taking the region which accords with the longitudinal positioning judgment result and the transverse judgment result from the six regions as the maximum possible abnormal region of the lower limbs, thereby realizing accurate positioning.
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