CN116803340A - Noninvasive blood pressure detection method based on multi-source data fusion and graph neural network - Google Patents
Noninvasive blood pressure detection method based on multi-source data fusion and graph neural network Download PDFInfo
- Publication number
- CN116803340A CN116803340A CN202310885999.1A CN202310885999A CN116803340A CN 116803340 A CN116803340 A CN 116803340A CN 202310885999 A CN202310885999 A CN 202310885999A CN 116803340 A CN116803340 A CN 116803340A
- Authority
- CN
- China
- Prior art keywords
- layer
- data
- ppg
- blood pressure
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000036772 blood pressure Effects 0.000 title claims abstract description 74
- 238000001514 detection method Methods 0.000 title claims abstract description 52
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 37
- 230000004927 fusion Effects 0.000 title claims abstract description 23
- 230000033001 locomotion Effects 0.000 claims abstract description 41
- 230000008859 change Effects 0.000 claims abstract description 25
- 238000005259 measurement Methods 0.000 claims abstract description 16
- 230000001629 suppression Effects 0.000 claims abstract description 16
- 238000000547 structure data Methods 0.000 claims abstract description 15
- 238000012549 training Methods 0.000 claims abstract description 10
- 239000010410 layer Substances 0.000 claims description 88
- 238000000034 method Methods 0.000 claims description 21
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000011176 pooling Methods 0.000 claims description 15
- 210000001519 tissue Anatomy 0.000 claims description 14
- 238000000605 extraction Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 230000004913 activation Effects 0.000 claims description 6
- 210000004204 blood vessel Anatomy 0.000 claims description 6
- 230000005764 inhibitory process Effects 0.000 claims description 5
- 230000035515 penetration Effects 0.000 claims description 5
- 230000010349 pulsation Effects 0.000 claims description 4
- 239000003086 colorant Substances 0.000 claims description 3
- 239000008358 core component Substances 0.000 claims description 3
- 230000031700 light absorption Effects 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 210000003205 muscle Anatomy 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 239000002344 surface layer Substances 0.000 claims description 3
- 230000002792 vascular Effects 0.000 claims description 3
- 230000004931 aggregating effect Effects 0.000 claims description 2
- 230000003313 weakening effect Effects 0.000 claims description 2
- 230000007774 longterm Effects 0.000 abstract description 12
- 210000003491 skin Anatomy 0.000 description 30
- 238000013461 design Methods 0.000 description 5
- 238000013135 deep learning Methods 0.000 description 4
- 230000035487 diastolic blood pressure Effects 0.000 description 3
- 230000035488 systolic blood pressure Effects 0.000 description 3
- 230000017531 blood circulation Effects 0.000 description 2
- 210000004207 dermis Anatomy 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 210000000707 wrist Anatomy 0.000 description 2
- UXFQFBNBSPQBJW-UHFFFAOYSA-N 2-amino-2-methylpropane-1,3-diol Chemical compound OCC(N)(C)CO UXFQFBNBSPQBJW-UHFFFAOYSA-N 0.000 description 1
- 101150035093 AMPD gene Proteins 0.000 description 1
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000012503 blood component Substances 0.000 description 1
- 238000009530 blood pressure measurement Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000036996 cardiovascular health Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000000306 component Substances 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 210000002615 epidermis Anatomy 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000010247 heart contraction Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 206010033675 panniculitis Diseases 0.000 description 1
- 238000013186 photoplethysmography Methods 0.000 description 1
- 230000001766 physiological effect Effects 0.000 description 1
- 239000000049 pigment Substances 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 210000004304 subcutaneous tissue Anatomy 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/02141—Details of apparatus construction, e.g. pump units or housings therefor, cuff pressurising systems, arrangements of fluid conduits or circuits
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Cardiology (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Physics & Mathematics (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Physiology (AREA)
- Vascular Medicine (AREA)
- Signal Processing (AREA)
- Power Engineering (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention discloses a noninvasive blood pressure detection method based on multi-source data fusion and a graph neural network, which comprises the following steps: s1, establishing a physiological data acquisition module and acquiring human PPG data and ECG data; s2, performing physiological signal compensation based on motion pseudo-noise suppression of measurement points and skin color difference suppression of wave bands; s3, constructing graph structure data after extracting waveform characteristics, performing model training according to a graph convolution neural network, and finally realizing accurate estimation of blood pressure data. The noninvasive blood pressure detection method based on the multi-source data fusion and the graph neural network improves the estimation accuracy of the blood pressure change trend and the robustness of long-term detection, and supports the long-term detection of blood pressure.
Description
Technical Field
The invention relates to the technical field of medical engineering fusion, in particular to a noninvasive blood pressure detection method based on multisource data fusion and a graph neural network.
Background
Blood pressure is an important indicator of cardiovascular health, and by detecting blood pressure, many cardiovascular diseases can be discovered and prevented early.
Invasive and noninvasive blood pressure detection algorithms are two different methods for measuring human blood pressure. Invasive blood pressure detection algorithms directly measure blood pressure by inserting a sensor in the arterial or venous vessel of the patient. Are commonly used in intensive care units and operating rooms to obtain high accuracy blood pressure data. The algorithm uses a pressure sensor to measure the pressure in the blood vessel and transmits the results to a monitor or computer for analysis and display. While invasive blood pressure detection algorithms provide accurate and real-time blood pressure data, the use of this method requires insertion into a blood vessel, possibly with the risk of infection and other complications.
The noninvasive blood pressure detection technology is widely applied to the scenes such as wards, operating rooms, emergency rooms, home health care and the like, and provides a convenient and quick blood pressure detection means for medical system personnel and individual users. Traditional noninvasive blood pressure detection algorithms calculate systolic and diastolic pressures by analyzing blood pressure signal changes based on cuff inflation and deflation processes. The noninvasive blood pressure detection algorithm is convenient, safe and easy to use, but cannot be detected in real time and is inconvenient to measure. To overcome these limitations, sleeveless blood pressure detection techniques have evolved to measure blood pressure using advanced sensor techniques and algorithms without the need for a cuff. In general, a device such as a photoelectric sensor, a pressure sensor or an acoustic sensor is used to detect a weak change caused by blood flow and acquire blood pressure data.
Sleeveless blood pressure detection using PPG (photoplethysmogram) and ECG (electrocardiogram) is a representative of this technique. The PPG sensor detects a change in blood flow by measuring a change in reflected light intensity of the skin surface using a photoelectric measurement principle; the ECG sensor records the electrical activity of the heart, including the information of systole and diastole, and by detecting the PPG and ECG signals simultaneously and combining with a new generation of AI processing algorithm, the real-time analysis and interpretation of blood pressure data can be realized. From the aspect of algorithm classification, the method is mainly divided into two major categories of traditional signal processing algorithm and machine learning/deep learning algorithm, and equipment products combining the two major categories are also available. Taking a continuous noninvasive blood pressure measurement system (CN202110247131. X) based on freeRTOS, which takes a patent of Shanghai LiHetai medical science and technology Co., ltd as an example, after data acquisition, calculating the blood pressure of a person to be measured in real time according to an optimized traditional PWV calculation method and a regression equation generated by considering heart rate factors; in contrast, the patent of Beijing aviation aerospace university (CN 202211609290.0) only uses PPG data and combines with a new generation deep learning transducer algorithm, so that a part of estimation accuracy and the degree of association between various data are sacrificed in exchange for convenience of data acquisition.
The non-invasive blood pressure detection related equipment basically integrates the non-invasive blood pressure detection and has certain continuous detection capability. However, the problems of single input data, low detection precision and the like are not solved.
In summary, the following problems are presented in the conventional detection methods by understanding the blood pressure detection technology.
1. The current cuff-free blood pressure detection equipment is difficult to realize the design of use without sense or low sense in use;
2. on the basis of the blood pressure detection of the PPG and the ECG, the problems that the PPG acquisition is easily influenced by factors such as motion pseudo noise, skin color difference and the like and the ECG has insufficient data acquisition precision caused by insufficient input impedance of acquisition equipment exist;
3. the existing sleeveless blood pressure detection mostly adopts a curve fitting method based on pulse wave transmission time or an estimation method based on deep learning, and has the defects of poor detection precision, obvious individual difference, poor interpretability and the like;
4. in the long-term management of blood pressure, the current blood pressure detection equipment has the problems of inconvenient long-term data statistics, inaccurate blood pressure trend change, deep information of long-term blood pressure data to be mined and the like.
Disclosure of Invention
The invention aims to provide a noninvasive blood pressure detection method based on multi-source data fusion and a graph neural network, which improves the estimation accuracy of the blood pressure change trend and the robustness of long-term detection and supports the long-term detection of blood pressure.
In order to achieve the above purpose, the invention provides a noninvasive blood pressure detection method based on multi-source data fusion and a graph neural network, which comprises the following steps:
s1, establishing a physiological data acquisition module and acquiring human PPG data and ECG data;
s2, performing physiological signal compensation based on motion pseudo-noise suppression of measurement points and skin color difference suppression of wave bands;
s3, constructing graph structure data after extracting waveform characteristics, performing model training according to a graph convolution neural network, and finally realizing accurate estimation of blood pressure data.
Preferably, in step S1, the step of acquiring PPG data is as follows: firstly, an embedded multiband LED light source irradiates a tested part, each tissue of the tested part is absorbed to different degrees, the light absorption quantity changes along with the change of a substance, and a reflected transmitted light intensity signal is received by a plurality of photoelectric conversion sensors at points and converted into an electric signal, namely PPG data of different points at different wavebands is obtained;
the acquisition of ECG data requires the attachment of electrode pads to designated locations of the body.
Preferably, in step S2, the specific flow of the motion artifact suppression algorithm based on the measurement point location is as follows:
(1) For the same band 4-point (four-point hypothesis A, B, C, D) PPG data (PPG A 、PPG B 、PPG C 、PPG D ) The collecting area is smaller at this time, the difference of 7 human skin colors and the like is small in the measuring area, the voltage waveform of PPG data is ideally composed of reflected light, ambient light and instrument noise, and the amplitude of PPG is expressed by the following formula
V PPG =V Reflected light +V Ambient light +V Instrument noise (1)
Wherein V is Reflected light Voltage generated for reflecting light of corresponding wave band, V Ambient light Voltage generated for light of corresponding wave band existing in environment, V Instrument noise Voltage noise generated for the measuring device itself;
(2) When motion occurs, V Reflected light Can change due to local muscle change and loose wearing, and can also cause V due to the phenomena of light leakage and the like in the measurement area Ambient light Producing a change, PPG data relating to two types of motion artifacts;
(3) When there is no motion artifact, then suppose V Reflected light ,V Ambient light Are all substantially similar, at this point V PPG Derived from the following formula:
as can be seen from equation (2), when there is no motion artifact, the noise error of the instrument is reduced according to this manner.
Preferably, in step S2, the influence of two motion artifacts on PPG data is as follows:
(1) When motion artifact 1 occurs, in the above formula (1), the light intensity of the LED is weakened due to light leakage, and V Reflected light Reduced, while the enhancement of ambient light causes V Ambient light Raising, weakening the parameter with the smallest change before and after as the PPG acquisition value of the band;
(2) And when motion artifact 2 occurs, V at four points PPG V respectively A +ΔV A 、V B +ΔV B 、V C +ΔV C 、V D +ΔV D Wherein V is A 、V B 、V C 、V D V without motion artifacts, respectively PPG Due to DeltaV A <ΔV B 、ΔV C <ΔV D While the physical positions of a and B, C and D have a linear relationship, there is a functional relationship f between the waveform voltage change of the point O, PPG and the point between a and D, so:
V O =f((V B -V A ),(V C -V D ),V D ,V A ) (3)
virtual point V O And reducing motion artifacts for PPG data of the wave band through the mapping relation.
Preferably, in step S2, the band-based skin color difference suppression algorithm is based on the difference in the penetration capability of different band lights to different tissue layers of the skin:
wherein V of four wave bands Reflected light ,V green 、V red 、V ir_1 、V ir_2 Is represented by the following formula:
wherein V is green Green light, V red Is red light, V ir_1 And V ir_2 Representing voltages generated by infrared light in different wave bands, V when proper wearing and other measurement conditions are ideal Skin color Is constant, V Surface layer 、V Superficial layer in skin 、V Deep layer in skin 、V Deep layer of skin Consisting of the tissue constant of the layer and vascular pulsations, namely:
V layer(s) =V Layer structure +V Blood vessel
And obtaining light reflection voltages of different tissue layers through a data difference algorithm, so as to achieve the effect of skin color difference inhibition.
Preferably, in step S3, the graph convolution neural network blood pressure estimation algorithm mainly comprises an input layer, a GCN layer, and a connection layer, and performs feature extraction on physiological data when the input layer, and performs model training by using GCN after constructing graph structure data by using the features, and sequentially performs GCN, pooling layer, and full connection layer processing on the graph structure data, specifically as follows:
(1) GCN layer: updating the characteristic representation of the node by calculating the weighted average value of the node and the neighbor node thereof;
(2) Pooling layer: the method is used for reducing the number of nodes in the graph convolution neural network, and keeping important information, and a pooling operation is used for aggregating a group of nodes into one node, so that the scale of the graph is reduced;
(3) Full tie layer: the full-connection layer is used for carrying out final feature processing and output prediction in the graph roll neural network, each node in the full-connection layer is connected with all nodes of the previous layer, each connection has weight, the full-connection layer carries out further feature extraction and combination on the outputs of the graph roll layer and the pooling layer to generate a final prediction result, and the full-connection layer comprises a ReLU nonlinear activation function to increase the expression capacity and nonlinear modeling capacity of the network.
Preferably, in step S3, the feature representation of the node is updated by calculating a weighted average of the node and its neighboring nodes, and the specific steps are as follows:
(a) The GCN layer is a core component of the graph convolution neural network and is used for carrying out feature propagation and learning on graph structure data;
(b) Feature propagation is performed by using neighbor information of nodes, so that the features of each node are affected by neighboring nodes;
specifically, the output of the GCN layer is an updated node feature matrix, wherein the features of each node are updated to a new representation that comprehensively considers neighbor node information, wherein the formula of the GCN layer is:
wherein sigma is an activation function, H l Is the node characteristic matrix of the first layer,in the form of an adjacency matrix a plus a self-loop,is the form of degree matrix D plus self-loop, W l Is a layer i weight matrix.
Preferably, in step S3, feature extraction of physiological data mainly includes: and extracting characteristic points of PPG of different wave bands for PPG data, wherein the characteristic points comprise wave crests and wave troughs, the wave form rises by 0.1 times of the wave crests, and the ECG signals are used for identifying P waves, Q waves, R waves, S waves and T waves.
Preferably, the physiological data acquisition module is additionally provided with high input impedance in the ECG acquisition circuit, so that the robustness of the acquisition module is enhanced, and the accurate acquisition of ECG data is realized.
Therefore, the non-invasive blood pressure detection method based on multi-source data fusion and graph neural network has the following technical effects:
(1) The measuring module integrally adopts a wristband type structure, and is worn for a long time by a user; the acquisition part is designed to resist movement pseudo noise, is tightly connected with the wrist at a plurality of photoelectric conversion positions, can be suitable for wearable equipment such as a bracelet and the like, and has small interference to normal life.
(2) The invention adopts a technical route of simultaneously collecting 4-band PPG signals and single-lead ECG signals. According to the invention, data fusion is carried out according to the data of different points, so that motion artifact resistance is realized; according to the principle that light of different wave bands has different penetrability to skin, difference fusion is carried out on multi-wave band data, and inhibition of skin color influence is achieved. And a high input impedance design is added to a traditional ECG acquisition circuit, so that the ECG acquisition precision is improved.
(3) In the implementation of blood pressure estimation, the method of adopting the multi-modal data and the graph neural network algorithm is adopted, and compared with the single-channel PPG and single-lead ECG adopted in traditional sleeveless blood pressure detection for calculating PWTT and then fitting an empirical formula or an estimation method based on deep learning, the technical route of the invention can better dig possible dependency relationship among multiple physiological data and improve detection precision.
(4) According to the method, the information is transmitted to the cloud end while the blood pressure data is estimated, the data mining algorithm is deployed in the cloud end, and an experience playback mechanism is utilized, so that the accuracy of estimating the blood pressure change trend and the robustness of long-term detection can be improved, and the long-term detection of the blood pressure is supported.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a hardware diagram of a physiological data acquisition module;
FIG. 2 is a graph of a blood pressure estimation algorithm based on the neural network of the graph;
FIG. 3 is a top view of a physiological data acquisition module hardware acquisition architecture;
FIG. 4 is a side view of a physiological data acquisition module hardware acquisition architecture;
FIG. 5 is a technical route of a non-invasive blood pressure detection method based on multi-source data fusion and a graph neural network;
FIG. 6 is a motion artifact generation scenario;
FIG. 7 is a physiological signal staging and generation principle;
fig. 8 is diagram structural data.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art. Such other embodiments are also within the scope of the present invention.
It should also be understood that the above-mentioned embodiments are only for explaining the present invention, the protection scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the protection scope of the present invention by equally replacing or changing the technical scheme and the inventive concept thereof within the scope of the present invention.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be considered part of the specification where appropriate.
The disclosures of the prior art documents cited in the present specification are incorporated by reference in their entirety into the present invention and are therefore part of the present disclosure.
Example 1
As shown in the figure, the invention provides a noninvasive blood pressure detection method based on multi-source data fusion and a graph neural network, which comprises the following steps:
step one, establishing a physiological data acquisition module and acquiring physiological data
The step of collecting data of the physiological data collection module is shown in figure 1, the measured individual collects PPG and ECG physiological parameters, firstly, the embedded LED light source irradiates to the measured part (four points of the wrist) for collecting PPG data, each tissue of the measured part is absorbed to different degrees, the light absorption quantity changes along with the change of substances, and the reflected transmitted light intensity signals are received by the photoelectric conversion sensors of the four points and converted into electric signals, so that PPG data of different points in the same wave band can be obtained, and the MCU time-sharing drives the LEDs to generate light in the wave bands of 550nm, 660nm, 880nm and 960nm, so that PPG data of different wave bands and different points can be obtained; the acquisition of ECG data requires that electrode pads are connected to specified positions of a body, common ECG acquisition modes comprise 12, 5, 3 leads and the like, but the acquisition is complex, consumable materials exist in wet electrodes, and the ECG acquisition is not suitable for real-time detection; in addition, due to the reasons of low amplitude, high human body impedance and the like, the ECG acquisition is easily influenced by factors such as noise, low input impedance of an acquisition device and the like, and in order to solve the problems, the invention integrates an ECG acquisition circuit comprising an amplifying and holding circuit, a filter circuit, an AD conversion circuit and the like and simultaneously carries out high input impedance design, thereby enhancing the robustness of an acquisition module and realizing accurate acquisition of ECG data.
The hardware acquisition structure of the physiological data acquisition module is shown in the anti-motion artifact designs shown in fig. 3 and 4, wherein '1' and '2' respectively represent photoelectric conversion sensors and multiband LED lamps, 4 photoelectric conversion sensors are projected to be contacted with skin more stably, and 4 band LEDs are recessed to be designed, so that the radiation area of LED light is wider, the inhibition of motion artifact is realized, and the resistance of the device to the motion artifact is improved.
The physiological data acquisition module adopts a wristband type design, and when a user performs blood pressure detection, the physiological data acquisition can be realized by only wearing the device with one hand and touching the electrode plate on the device with the other hand.
The physiological data acquisition module supports the expansion of functions such as data storage and 4G communication, can store and transmit a series of data such as multi-mode physiological data and blood pressure estimated values in a cloud, and supports the subsequent long-term management of blood pressure and deep information mining.
The physiological data of the user is continuously acquired in real time, wherein the physiological data mainly comprises non-invasive measurement of multiple physiological parameters by photoplethysmography and single-lead ECG.
Collecting human PPG data and ECG data, wherein the main component of the PPG signal is pulse waveform caused by heart pulsation, and the conditions of heart contraction and relaxation can be reflected indirectly by analyzing the characteristics of pulse wave, such as the amplitude, rise time, fall time and the like of the pulse wave; the ECG records the electrical activity of the heart. By analyzing characteristics such as morphology, duration, and interval of the ECG signal, the rhythm and function of the heart can be assessed.
Step two, physiological signal compensation is carried out based on motion pseudo-noise suppression of measurement points and band skin color difference suppression
The PPG algorithm adopts an optical sensing principle, but in the acquisition process, waveform distortion of PPG is often caused by factors such as motion pseudo noise, sensor attachment, ambient light, skin color difference and the like, and the partial algorithm performs PPG data quality optimization based on multi-point multi-band PPG data, so that the accuracy and reliability of the PPG data are improved.
The physiological signal compensation algorithm consists of a motion artifact suppression algorithm based on measurement points and a band-based skin color difference suppression algorithm.
The motion pseudo noise suppression algorithm based on the measurement points comprises the following specific flow:
(1) For the same band 4-point (four-point hypothesis A, B, C, D) PPG data (PPG A 、PPG B 、PPG C 、PPG D ) The collecting area is smaller at this time, the difference of 7 human skin colors and the like is small in the measuring area, the voltage waveform of PPG data is ideally composed of reflected light, ambient light and instrument noise, and the amplitude of PPG is expressed by the following formula
V PPG =V Reflected light +V Ambient light +V Instrument noise (1)
Wherein V is Reflected light Voltage generated for reflecting light of corresponding wave band, V Ambient light Voltage generated for light of corresponding wave band existing in environment, V Instrument noise For measuring the voltage noise generated by the device itself.
(2) When motion occurs, V Reflected light Can change due to local muscle change and loose wearing, and can also cause V due to the phenomena of light leakage and the like in the measurement area Ambient light The changes are generated, and specific motion artifacts are shown in fig. 6:
(a) When motion artifact 1 occurs, in the above formula (1), the light intensity of the LED is weakened due to light leakage, and V Reflected light Reduced, while the enhancement of ambient light causes V Ambient light As shown in fig. 6, the two parameters of the D point have the smallest change, and the PPG data of the D point is selected as the PPG acquisition value of the band;
(b) And when the situation shown in the motion artifact 2 of fig. 6 occurs, V of four points PPG V respectively A +ΔV A 、V B +ΔV B 、V C +ΔV C 、V D +ΔV D Wherein V is A 、V B 、V C 、V D V without motion artifacts, respectively PPG . As can be seen from the positional relationship of FIG. 6, deltaV A <ΔV B 、ΔV C <ΔV D While there is a linear relationship between the physical locations of a and B, C and D, it is assumed that there is a functional relationship f between the waveform voltage change at point O, PPG and D and the point, so:
V O =f((V B -V A ),(V C -V D ),V D ,V A ) (2)
virtual point V O And reducing motion artifacts for PPG data of the wave band through the mapping relation.
(3) When there is no motion artifact, then suppose V Reflected light ,V Ambient light Are all substantially similar, at this point V PPG Derived from the following formula:
as can be seen from equation (3), when there is no motion artifact, the noise error of the instrument is reduced according to this manner.
The band-based skin color difference suppression algorithm is based on the fact that the penetration capacities of different band lights on different tissue layers of skin are different:
the penetration depth of green light (wavelength 495-570 nm) at the skin surface is relatively shallow, and it is mainly absorbed by blood vessels, hair and pigments at the skin surface;
the penetration depth of red light (about 620-700 nanometers) is relatively large, and can reach the middle and shallow layers of skin, so that the red light can penetrate the epidermis layer and the dermis layer, and has certain application value for evaluating skin tissues and blood components;
infrared light (about wavelength 700 nm to 1 mm): the skin can be penetrated through the dermis layer and a part of subcutaneous tissue, and infrared light has a certain effect on observation and evaluation of some deep tissues.
Wherein V of four wave bands Reflected light ,V green 、V red 、V ir_1 、V ir_2 Is represented by the following formula:
wherein V is green Green light, V red Is red light, V ir_1 And V ir_2 Representing voltages generated by infrared light in different wave bands, V when proper wearing and other measurement conditions are ideal Skin color Is constant, V Surface layer 、V Superficial layer in skin 、V Deep layer in skin 、V Deep layer of skin Consisting of the tissue constant of the layer and vascular pulsations, namely:
V layer(s) =V Layer structure +V Blood vessel
And obtaining light reflection voltages of different tissue layers through a data difference algorithm, so as to achieve the effect of skin color difference inhibition.
Thirdly, constructing graph structure data after extracting waveform characteristics, performing model training according to a graph convolution neural network, and finally realizing accurate estimation of blood pressure data
In order to realize deep mining of PPG and ECG data information and realize real-time and accurate detection of blood pressure indexes, the method comprises the steps of constructing graph structure data after waveform characteristics are extracted, performing model training according to a graph roll neural network (GCN), and finally realizing accurate estimation of blood pressure data.
The algorithm mainly comprises an input layer, a GCN layer and a connecting layer.
When the physiological data is input into the layer, firstly, feature extraction is carried out on the physiological data, and feature points of PPG of different wave bands shown in figure 7 are extracted mainly by adopting a Fourier curve fitting, an AMPD peak algorithm, a threshold method and the like on PPG data, wherein the features comprise wave crests, wave troughs, wave crests rising by 0.1 times and the like. The ECG signal mainly adopts wavelet transformation and self-adaptive sliding window algorithm to identify P wave, Q wave, R wave, S wave, T wave and the like.
The graph structure data is constructed using the above features, as shown in fig. 8. Wherein, the hollow node represents the characteristics of different wave bands PPG at different waveform characteristic points, and the black node is the ECG characteristic. The nodes from left to right are sequentially time sequence and amplitude parameters corresponding to different characteristic points, and the sides are corresponding relations of actual physiological activities of the heart.
After the graph structure data is established, model training is carried out by utilizing the GCN, and the graph structure data is sequentially processed by the GCN, the pooling layer and the full-connection layer, wherein the model training method comprises the following steps of:
1. GCN layer: the characteristic representation of the node is updated by calculating the weighted average value of the node and the neighbor node, and the specific steps are as follows:
(1) The GCN layer is a core component of the graph roll-up neural network for feature propagation and learning on graph structure data.
(2) Feature propagation is performed by using neighbor information of nodes, so that features of each node are affected by neighboring nodes.
Specifically, the output of the GCN layer is an updated node feature matrix, wherein the features of each node are updated to take into account new representations of neighbor node information. Wherein the formula of the GCN layer is:
wherein sigma is an activation function, H l Is the node characteristic matrix of the first layer,in the form of an adjacency matrix a plus a self-loop,is the form of degree matrix D plus self-loop, W l Is a matrix of weights of a first layer,
2. pooling layer: used in graph convolutional neural networks to reduce the number of nodes in the graph and to retain important information. The pooling operation aggregates a set of nodes into one node, thereby reducing the size of the graph. Common graph pooling methods include maximum pooling, which chooses the largest eigenvalue in the aggregate node, and average pooling, which computes the characteristic average of the aggregate node.
3. Full tie layer: the fully connected layer is used for final feature processing and output prediction in the graph roll-up neural network. Each node in the fully connected layer has a connection with all nodes of the previous layer, each connection having a weight. The fully connected layer performs further feature extraction and combination of the outputs of the graph roll layer and the pooling layer to generate a final prediction result. The fully connected layer includes ReLU (Rectified Linear Unit) nonlinear activation functions to increase the expressive power and nonlinear modeling power of the network.
Through training and verification of the self-built data set, the error of the blood pressure estimation of the module is as follows:
index (I) | Absolute error/mmHg | Relative error/% |
Systolic Blood Pressure (SBP) | ±9.1 | 7.8 |
Diastolic Blood Pressure (DBP) | ±5.5 | 7.09 |
Therefore, the noninvasive blood pressure detection method based on the multi-source data fusion and the graph neural network improves the estimation accuracy of the blood pressure change trend and the robustness of long-term detection, and supports the long-term detection of blood pressure.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (9)
1. A noninvasive blood pressure detection method based on multi-source data fusion and a graph neural network is characterized by comprising the following steps:
s1, establishing a physiological data acquisition module and acquiring human PPG data and ECG data;
s2, performing physiological signal compensation based on motion pseudo-noise suppression of measurement points and skin color difference suppression of wave bands;
s3, constructing graph structure data after extracting waveform characteristics, performing model training according to a graph convolution neural network, and finally realizing accurate estimation of blood pressure data.
2. A non-invasive blood pressure detection method based on multi-source data fusion and graph neural network according to claim 1, wherein in step S1, the step of collecting PPG data is as follows: firstly, an embedded multiband LED light source irradiates a tested part, each tissue of the tested part is absorbed to different degrees, the light absorption quantity changes along with the change of a substance, and a reflected transmitted light intensity signal is received by a plurality of photoelectric conversion sensors at points and converted into an electric signal, namely PPG data of different points at different wavebands is obtained;
the acquisition of ECG data requires the attachment of electrode pads to designated locations of the body.
3. The non-invasive blood pressure detection method based on multi-source data fusion and graph neural network according to claim 1, wherein in step S2, the specific flow of the motion artifact suppression algorithm based on measurement points is as follows:
(1) For the same wave band4-Point (four-Point hypothesis A, B, C, D) PPG data (PPG A 、PPG B 、PPG C 、PPG D ) The collecting area is smaller at this time, the difference of 7 human skin colors and the like is small in the measuring area, the voltage waveform of PPG data is ideally composed of reflected light, ambient light and instrument noise, and the amplitude of PPG is expressed by the following formula
V PPG =V Reflected light +V Ambient light +V Instrument noise (1)
Wherein V is Reflected light Voltage generated for reflecting light of corresponding wave band, V Ambient light Voltage generated for light of corresponding wave band existing in environment, V Instrument noise Voltage noise generated for the measuring device itself;
(2) When motion occurs, V Reflected light Can change due to local muscle change and loose wearing, and can also cause V due to the phenomena of light leakage and the like in the measurement area Ambient light Producing a change, PPG data relating to two types of motion artifacts;
(3) When there is no motion artifact, then suppose V Reflected light ,V Ambient light Are all substantially similar, at this point V PPG Derived from the following formula:
as can be seen from equation (2), when there is no motion artifact, the noise error of the instrument is reduced according to this manner.
4. A non-invasive blood pressure detection method based on multi-source data fusion and graph neural network according to claim 3, wherein in step S2, the influence of two kinds of motion artifacts on PPG data is as follows:
(1) When motion artifact 1 occurs, in the above formula (1), the light intensity of the LED is weakened due to light leakage, and V Reflected light Reduced, while the enhancement of ambient light causes V Environment (environment)Light source Raising, weakening the parameter with the smallest change before and after as the PPG acquisition value of the band;
(2) And when motion artifact 2 occurs, V at four points PPG V respectively A +ΔV A 、V B +ΔV B 、V C +ΔV C 、V D +ΔV D Wherein V is A 、V B 、V C 、V D V without motion artifacts, respectively PPG Due to DeltaV A <ΔV B 、ΔV C <ΔV D While the physical positions of a and B, C and D have a linear relationship, there is a functional relationship f between the waveform voltage change of the point O, PPG and the point between a and D, so:
virtual point V O And reducing motion artifacts for PPG data of the wave band through the mapping relation.
5. The method for non-invasive blood pressure detection based on multi-source data fusion and graph neural network according to claim 1, wherein in step S2, the band-based skin color difference suppression algorithm is based on different penetration capacities of different band lights to different tissue layers of skin:
wherein V of four wave bands Reflected light ,V green 、V red 、V ir_1 、V ir_2 Is represented by the following formula:
wherein V is green Green light, V red Is red light, V ir_1 And V ir_2 Representing voltages generated by infrared light in different wave bands, V when proper wearing and other measurement conditions are ideal Skin color Is constant, V Surface layer 、V Superficial layer in skin 、V In the skinDeep layer 、V Deep layer of skin Consisting of the tissue constant of the layer and vascular pulsations, namely:
V layer(s) =V Layer structure +V Blood vessel
And obtaining light reflection voltages of different tissue layers through a data difference algorithm, so as to achieve the effect of skin color difference inhibition.
6. The non-invasive blood pressure detection method based on multi-source data fusion and graph neural network according to claim 1, wherein in step S3, the graph convolution neural network blood pressure estimation algorithm mainly comprises an input layer, a GCN layer and a connection layer, the physiological data is subjected to feature extraction when the input layer is used, the graph structure data is constructed by the features, then model training is performed by using the GCN, and the graph structure data is sequentially subjected to GCN, pooling layer and full connection layer processing, specifically as follows:
(1) GCN layer: updating the characteristic representation of the node by calculating the weighted average value of the node and the neighbor node thereof;
(2) Pooling layer: the method is used for reducing the number of nodes in the graph convolution neural network, and keeping important information, and a pooling operation is used for aggregating a group of nodes into one node, so that the scale of the graph is reduced;
(3) Full tie layer: the full-connection layer is used for carrying out final feature processing and output prediction in the graph roll neural network, each node in the full-connection layer is connected with all nodes of the previous layer, each connection has weight, the full-connection layer carries out further feature extraction and combination on the outputs of the graph roll layer and the pooling layer to generate a final prediction result, and the full-connection layer comprises a ReLU nonlinear activation function to increase the expression capacity and nonlinear modeling capacity of the network.
7. The non-invasive blood pressure detection method based on multi-source data fusion and graph neural network according to claim 6, wherein in step S3, the characteristic representation of the node is updated by calculating the weighted average of the node and its neighboring nodes, and the specific steps are as follows:
(a) The GCN layer is a core component of the graph convolution neural network and is used for carrying out feature propagation and learning on graph structure data;
(b) Feature propagation is performed by using neighbor information of nodes, so that the features of each node are affected by neighboring nodes;
specifically, the output of the GCN layer is an updated node feature matrix, wherein the features of each node are updated to a new representation that comprehensively considers neighbor node information, wherein the formula of the GCN layer is:
wherein sigma is an activation function, H l Is the node characteristic matrix of the first layer,in the form of an adjacency matrix A plus self-loop, < >>Is the form of degree matrix D plus self-loop, W l Is a layer i weight matrix.
8. The non-invasive blood pressure detection method based on multi-source data fusion and graph neural network according to claim 1, wherein in step S3, feature extraction of physiological data mainly comprises: and extracting characteristic points of PPG of different wave bands for PPG data, wherein the characteristic points comprise wave crests and wave troughs, the wave form rises by 0.1 times of the wave crests, and the ECG signals are used for identifying P waves, Q waves, R waves, S waves and T waves.
9. The noninvasive blood pressure detection method based on multi-source data fusion and graph neural network according to claim 1, wherein the physiological data acquisition module is additionally provided with high input impedance in an ECG acquisition circuit, so that the robustness of the acquisition module is enhanced, and the accurate acquisition of ECG data is realized.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310885999.1A CN116803340B (en) | 2023-07-19 | 2023-07-19 | Noninvasive blood pressure detection method based on multi-source data fusion and graph neural network |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310885999.1A CN116803340B (en) | 2023-07-19 | 2023-07-19 | Noninvasive blood pressure detection method based on multi-source data fusion and graph neural network |
Publications (2)
Publication Number | Publication Date |
---|---|
CN116803340A true CN116803340A (en) | 2023-09-26 |
CN116803340B CN116803340B (en) | 2024-06-14 |
Family
ID=88079508
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310885999.1A Active CN116803340B (en) | 2023-07-19 | 2023-07-19 | Noninvasive blood pressure detection method based on multi-source data fusion and graph neural network |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116803340B (en) |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015187732A1 (en) * | 2014-06-03 | 2015-12-10 | The Texas A&M University System | Optical sensor for health monitoring |
CN112512413A (en) * | 2019-04-12 | 2021-03-16 | 北京顺源开华科技有限公司 | Blood pressure measurement |
WO2021208490A1 (en) * | 2020-04-13 | 2021-10-21 | 中国科学院深圳先进技术研究院 | Blood pressure measuring method and device based on deep neural network |
CN114145724A (en) * | 2021-12-08 | 2022-03-08 | 四川北易信息技术有限公司 | Method for dynamically monitoring blood pressure based on ECG (electrocardiogram) and PPG (photoplethysmography) multiple physiological characteristic parameters |
WO2023055862A1 (en) * | 2021-10-01 | 2023-04-06 | Softserve, Inc. | Systems and methods for platform-agnostic, real-time physiologic vital sign detection from video stream data |
CN115956889A (en) * | 2022-12-05 | 2023-04-14 | 出门问问信息科技有限公司 | Blood pressure monitoring method and device and electronic equipment |
-
2023
- 2023-07-19 CN CN202310885999.1A patent/CN116803340B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015187732A1 (en) * | 2014-06-03 | 2015-12-10 | The Texas A&M University System | Optical sensor for health monitoring |
CN112512413A (en) * | 2019-04-12 | 2021-03-16 | 北京顺源开华科技有限公司 | Blood pressure measurement |
WO2021208490A1 (en) * | 2020-04-13 | 2021-10-21 | 中国科学院深圳先进技术研究院 | Blood pressure measuring method and device based on deep neural network |
WO2023055862A1 (en) * | 2021-10-01 | 2023-04-06 | Softserve, Inc. | Systems and methods for platform-agnostic, real-time physiologic vital sign detection from video stream data |
CN114145724A (en) * | 2021-12-08 | 2022-03-08 | 四川北易信息技术有限公司 | Method for dynamically monitoring blood pressure based on ECG (electrocardiogram) and PPG (photoplethysmography) multiple physiological characteristic parameters |
CN115956889A (en) * | 2022-12-05 | 2023-04-14 | 出门问问信息科技有限公司 | Blood pressure monitoring method and device and electronic equipment |
Non-Patent Citations (1)
Title |
---|
汤宇飞: "基于脉搏波的糖尿病和高血压诊断算法研究", 《中国矿业大学硕士学位论文》, 1 May 2023 (2023-05-01), pages 37 * |
Also Published As
Publication number | Publication date |
---|---|
CN116803340B (en) | 2024-06-14 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109157202B (en) | Cardiovascular disease early warning system based on multi-physiological signal deep fusion | |
Miao et al. | Multi-sensor fusion approach for cuff-less blood pressure measurement | |
US12059271B2 (en) | Processing of electrophysiological signals | |
CA3097663A1 (en) | Methods to estimate the blood pressure and the arterial stiffness based on photoplethysmographic (ppg) signals | |
CN107233087A (en) | A kind of Woundless blood pressure measuring device based on photoplethysmographic feature | |
WO2019161609A1 (en) | Method for analyzing multi-parameter monitoring data and multi-parameter monitor | |
CN106413534A (en) | Blood-pressure continuous-measurement device, measurement model establishment method, and system | |
Zhou et al. | The noninvasive blood pressure measurement based on facial images processing | |
CN110236508A (en) | A kind of non-invasive blood pressure continuous monitoring method | |
US20210000351A1 (en) | Monitoring device for monitoring of vital signs | |
CN108478203A (en) | A kind of blood pressure measuring method monitoring radar based on single vital sign | |
CN111714088B (en) | Human body characteristic index detection method and system based on traditional Chinese medicine principle | |
CN110974172A (en) | Real-time physiological parameter measuring system | |
CN110881967A (en) | Non-invasive multi-segment peripheral arterial vessel elastic function detection method and instrument thereof | |
CN115299899A (en) | Activity recognition and beat-to-beat blood pressure monitoring, analyzing and early warning system based on multiple sensors | |
CN107582040B (en) | Method and device for monitoring heart rhythm | |
CN106618532A (en) | Dressing device for collecting characteristic parameters of electrocardio and blood pressure and pulse | |
CN116869499A (en) | Continuous blood pressure measurement method based on PPG and multi-order differential signals thereof | |
Wang et al. | A novel approach to estimate blood pressure of blood loss continuously based on stacked auto-encoder neural networks | |
Lu et al. | Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review | |
CN114587307B (en) | Non-contact blood pressure detector and method based on capacitive coupling electrode | |
CN116803340B (en) | Noninvasive blood pressure detection method based on multi-source data fusion and graph neural network | |
Kew et al. | Wearable patch-type ECG using ubiquitous wireless sensor network for healthcare monitoring application | |
Alqudah et al. | Multiple time and spectral analysis techniques for comparing the PhotoPlethysmography to PiezoelectricPlethysmography with electrocardiography | |
Yılmaz et al. | Comparison of Electrode Configurations for Impedance Plethysmography Based Heart Rate Estimation at the Forearm |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |