CN112450899A - Self-calibration continuous blood pressure measuring device and method - Google Patents
Self-calibration continuous blood pressure measuring device and method Download PDFInfo
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Abstract
The invention discloses a self-calibration continuous blood pressure measuring device and a method, comprising the following steps of S101: collecting single-channel electrocardiosignals; s102: removing power frequency interference in the single-channel electrocardiosignal; s103: respiratory and body movement interference in the single-channel electrocardiosignal is removed; s104: acquiring an electrocardiographic waveform R wave; s105: synchronously collecting photoplethysmography signals; s106: removing power frequency interference in the pulse wave signal; s107: removing respiratory and body movement interference in the pulse wave signal; s108: searching Peak characteristic points and Onset characteristic points; s109: calculating a real-time heart rate HR; s1010: calculating pulse wave arrival time PAT; s1011: calculating a pulse intensity ratio PIR; s1012: calibrating periodically; s1013: measuring blood pressure; s1014: and (6) returning data. The method can realize automatic calibration and compensation of blood pressure detection, inhibit data drift and track the blood pressure change information of the user.
Description
Technical Field
The invention relates to the field of blood pressure measurement, in particular to a self-calibration continuous blood pressure measurement device and a self-calibration continuous blood pressure measurement method.
Background
Cardiovascular and cerebrovascular diseases have very high morbidity and mortality and great harm to human beings. Blood pressure is an important indicator of human health, and hypertension is generally considered as a major contributing factor to cardiovascular and cerebrovascular diseases.
The existing cuff blood pressure measuring method can only provide transient blood pressure data and cannot capture continuous changes of the blood pressure of a human body. The continuous blood pressure estimation method based on the pulse wave arrival time (PAT) or the pulse wave transmission time (PTT) which is of great interest can track the continuous change of the blood pressure, but the initial calibration of the method is not fine and effective enough, the change of the body position of the user can cause the change of the whole body hemodynamic parameters, the drift is easy to occur in the long-term measurement process, and the method lacks regular calibration and compensation.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a self-calibration continuous blood pressure measuring device and a self-calibration continuous blood pressure measuring method, which can realize automatic calibration and compensation of blood pressure detection, effectively inhibit data drift in a long-term measuring process and continuously track blood pressure change information of a user.
In order to achieve the above object, a self-calibration continuous blood pressure measuring method according to the present invention includes the steps of, S101: collecting single-channel electrocardiosignals; s102: removing power frequency interference in the single-channel electrocardiosignal; s103: respiratory and body movement interference in the single-channel electrocardiosignal is removed; s104: acquiring an electrocardio waveform R wave by a Pan-Tompkins algorithm; s105: synchronously collecting photoplethysmography signals; s106: removing power frequency interference in the pulse wave signals; s107: removing respiratory and body movement interference in the pulse wave signal; s108: searching Peak characteristic points and Onset characteristic points of the pulse wave waveform backwards; s109: calculating a real-time heart rate HR according to the RR interval of the electrocardiographic waveform; s1010: calculating the arrival time PAT of the pulse wave according to the characteristic points of the electrocardio signal R wave and the pulse wave signal Onset; s1011: calculating a pulse intensity ratio PIR according to Peak characteristic points and Onset characteristic points of the pulse wave waveform; s1012: the standard medical blood pressure module regularly calibrates the measurement system; s1013: measuring blood pressure through a training model; s1014: and displaying the measurement result and returning the data.
Further, in step S102 and step S106, a 5-point moving average filter is used to remove power frequency interference.
Further, in step S103 and step S107, an IIR filter or a digital band-pass FIR filter with a cutoff frequency of 0.3 to 50Hz is used to remove respiratory and body motion interference.
Further, in step S108, a local minimum point of the pulse wave signal in a 120ms-220ms time window after the electrocardiographic waveform R wave is extracted as an Onset feature point, and sampling position information and amplitude information I of the Onset feature point of the feature point are recordedLExtracting a local maximum value point of a pulse wave signal in a 220ms-320ms time window after an electrocardio waveform R wave as a Peak characteristic point, and recording amplitude information I of the Peak characteristic point of the characteristic pointH。
Further, in step S1010, the pulse wave arrival time PAT is a difference between an R wave of an electrocardiographic waveform and a characteristic point Onset sampling position of the pulse wave waveform.
Further, in the step S1011, the pulse intensity ratio PIR is a pulse wave waveformPeak feature point amplitude information IIH and Onset feature point amplitude information ILThe ratio of.
Further, in step S1013, a blood pressure estimation model is proposed, and the obtained training model formula is expressed as:
wherein SBP and DBP represent blood pressure estimation value and DBP0，PP0，PAT0，PIR0，HR0As the initial model value, PAT, PIR, and HR are model parameters, PAT is the pulse wave arrival time calculated in step S1010, PIR is the pulse intensity ratio calculated in step S1011, and HR is the real-time heart rate calculated in step S109.
Further, in step S1014, the continuous blood pressure measurement result obtained in step S1013 is displayed in real time through a display screen, and the measurement result is transmitted back to the intelligent client in real time, and finally transmitted back to the back-end big data platform by the intelligent client for storage and analysis.
The measuring device comprises a sensing unit, an air bag cuff, a microprocessor, a blood pressure calibration module, a wireless communication module, a display module and a power module, wherein the air bag cuff and the blood pressure calibration module are in pneumatic connection, the sensing unit, the microprocessor, the blood pressure calibration module, the wireless communication module and the display module are sequentially in electric connection, and the power module, the sensing unit, the microprocessor, the blood pressure calibration module, the wireless communication module and the display module are all in electric connection.
Further, the sensing unit comprises an electrocardio electrode and a pulse wave probe.
Has the advantages that: 1. the self-calibration continuous blood pressure measuring device can realize automatic calibration and compensation of blood pressure measurement in the using process of a user, and effectively inhibit data drift in the long-term measuring process;
2. the self-calibration continuous blood pressure measuring device can accurately track the continuous change of the blood pressure parameter of a user in real time;
3. the self-calibration continuous blood pressure measuring method provided by the invention is characterized in that a blood pressure estimation model is constructed according to the detected waveform characteristics, and specifically, a blood pressure value is estimated by detecting the pulse wave arrival time PAT, the pulse intensity ratio PIR, the real-time heart rate HR and other characteristic parameters and substituting the characteristic parameters into the blood pressure estimation model.
4. The intelligent client serves as transfer equipment to realize reliable data transmission;
5. and long-term tracking is carried out on the data by combining a back-end big data platform.
Drawings
The present invention will be further described and illustrated with reference to the following drawings.
Fig. 1 is a schematic structural diagram of a self-calibrating continuous blood pressure measuring device in accordance with a preferred embodiment of the present invention.
FIG. 2 is a schematic structural diagram of a sensing unit according to a preferred embodiment of the present invention;
FIG. 3 is a flow chart of the steps of a method of self-calibrating continuous blood pressure measurement;
fig. 4 is a diagram of pulse wave arrival time PAT, pulse intensity ratio PIR and heart rate HR signature definitions.
[ reference numerals ]
101-a power supply module; 102-a sensing unit; 103-a microprocessor; 104-a wireless communication module; 105-an airbag cuff; 106-a blood pressure calibration module; 107-display module; 108-a smart client; 1020-electrocardio-electrodes; 1021-pulse wave probe;
Detailed Description
The technical solution of the present invention will be more clearly and completely explained by the description of the preferred embodiments of the present invention with reference to the accompanying drawings.
As shown in fig. 1, the self-calibrating continuous blood pressure measuring device according to the preferred embodiment of the present invention includes a sensing unit 102, an airbag cuff 105, a microprocessor 103, a blood pressure calibrating module 106, a wireless communication module 104, a display module 107, and a power module 101.
The sensing unit 102 includes an electrocardiograph electrode 1020 and a pulse wave probe 1021. The airbag cuff 105 and the blood pressure calibration module 106 are in pneumatic connection. The sensing unit 102, the microprocessor 103, the blood pressure calibration module 106, the wireless communication module 104 and the display module 107 are electrically connected in sequence. The power module 101 is electrically connected to the sensing unit 102, the microprocessor 103, the blood pressure calibration module 106, the wireless communication module 104, and the display module 107.
The principle of the measuring device is as follows:
the electrocardio-electrode 1020 and the pulse wave probe 1021 of the sensing unit 102 are used for synchronously sensing the electrocardio-signal (ECG signal) and the pulse wave signal (PPG signal) of the human body.
The electrocardio-electrode 1020 is attached to the chest of a human body or is arranged between two hands to realize the acquisition of electrocardiosignals and is used for measuring the single-lead electrocardiogram of the human body, and the electrocardio-electrode 1020 can be an Ag/AgCI wet electrode, an AgCI dry electrode and the like. In the embodiment, an Ag/AgCI wet electrode is adopted to collect the electrocardiogram signals of the chest leads of the human body.
The pulse wave probe 1021 realizes the collection of pulse wave signals by being clamped on the finger of the tested person or being placed on the wrist, the pulse wave probe 1021 can be a transmission type or reflection type pulse wave probe, and the transmission type pulse wave probe is adopted in the embodiment to collect the pulse wave signals of the finger part of the human body.
The processor 103 is used for acquiring real-time data of the acquired electrocardiosignals (ECG signals) and pulse wave signals (PPG signals), processing data, analyzing physiological parameters of blood pressure, and the like. The microprocessor 103 may be an ARM series, DSP series, or FPGA series chip, and in this embodiment, a CC2640R2F microprocessor chip of TI corporation is used.
The wireless communication module 104 mainly realizes data exchange between the device and the intelligent client 108, including instruction transmission, blood pressure measurement result transmission, electrocardio and pulse wave signal transmission, and finally transmits all effective information of the user back to the back-end big data platform 109.
The wireless communication module 104 may be embedded in SOC chips such as the CC2640R2F and the nRF52840, and the wireless communication module 104 may also adopt an external integrated communication module, for example, a bluetooth module in the HC series and a WIFI module in the ESP8266 series, and in this embodiment, a BLE4.2 wireless communication protocol stack is integrated in the CC2640R2F SOC chip, and no additional wireless communication module is needed.
The intelligent client 108 is used for controlling the device to measure, when a regular calibration instruction of the intelligent client 108 is sent, the device starts the blood pressure calibration module 106 to obtain a reference blood pressure value of the human body at the moment, the reference blood pressure value calibrates the blood pressure model of the device, and the device starts a continuous blood pressure measurement mode and transmits a continuous blood pressure measurement result, an electrocardiosignal and a pulse wave signal to the intelligent client 108.
The power module 101 provides a stable and reliable power for the operation of the sensing unit 102, the microprocessor 103, the blood pressure calibration module 106, the wireless communication module 104, and the display module 107. The power module 101 may be a switching power supply (DC/DC), a linear regulator (LDO), or the like, and in this embodiment, a switching power supply (DC/DC) chip is used to supply power to all the electrical connection modules of the apparatus.
The cuff 105 is a repeatable inflatable cuff, and the cuff can be used with the blood pressure calibration module 106 to measure a reference blood pressure, which is used to calibrate the blood pressure measuring device. The airbag cuff 105 may be an arm cuff, a wrist cuff, or a finger cuff, and an arm cuff is used in the present embodiment.
The blood pressure calibration module 106 is a standard medical blood pressure measuring device, and is used in cooperation with the air bag cuff 105 to measure a reference blood pressure value, and the reference blood pressure value measured by the blood pressure calibration module 106 is used for calibrating the blood pressure measuring device. The blood pressure calibration module 106 may be a blood pressure module registered by a medical device, and in this embodiment, a noninvasive blood pressure OEM module of cistai corporation, usa is used.
The display module 107 realizes real-time display of blood pressure measurement results, and may display an electrocardiogram waveform, a pulse wave signal waveform, a heart rate HR, a pulse wave transit time PTT, blood pressure measurement results, and the like. The display module 107 may be a display screen such as an LCD, an OLED, or a TFT, and in this embodiment, an OLED liquid crystal display screen is used to display the measurement result.
The invention also provides a self-calibration continuous blood pressure measuring method, which is used for the self-calibration continuous blood pressure measuring device and specifically comprises the following steps:
as shown in fig. 3, step S101: single channel cardiac electrical signals (ECG signals) are acquired. The single-channel electrocardiosignal is obtained through the electrocardioelectrode 1020 of the sensing unit 102, and then is amplified by taking an ultra-low power consumption analog front end AFE4900 chip as an amplification unit of a weak bioelectricity signal, and the content of an original signal is not influenced by the amplification of the electrocardiosignal. The single-channel electrocardiosignal comprises power frequency interference, human respiration, body movement and other noises.
Step S102: and (4) removing power frequency interference in the single-channel electrocardiosignal in the step (S101) by adopting a moving average filter. The sampling frequency of the electrocardiosignals collected in the embodiment of the invention is 250Hz, and the power frequency interference frequency is 50Hz, so that the power frequency interference in the electrocardiosignals is removed by adopting a 5-point moving average filter in the embodiment. The electrocardiosignal is accessed to the analog front end and the microprocessor system through a signal wire, and is output to a signal after interference is removed through a 5-point moving average filter.
Step S103: the respiratory and body movement interference in the single-channel electrocardiosignal is removed. Because the frequency spectrum range of the respiration and body motion interference is below 0.3Hz, and the frequency spectrum of the electrocardiosignal is within 50Hz, the signal obtained in the step S102 is passed through an IIR band-pass filter with the cut-off frequency of 0.3-50Hz to remove the above two interferences, and the filter can be a digital band-pass FIR filter, a wavelet filter, an empirical mode decomposition EMD filter, etc.
Step S104: the Pan-Tompkins algorithm acquires the R wave of the electrocardiographic waveform. The electrocardiograph signal comprises a P wave, a QRS complex, a T wave and the like, wherein the amplitude of the QRS complex is more obvious compared with the amplitudes of other waves, as shown in the first waveform of fig. 4, and the Pan-Tompkins algorithm is adopted to extract the sampling position information of the R wave in the embodiment of the invention. The QRS complex reflects changes in left and right ventricular depolarization potential and time, with the first downward wave being the Q wave, the upward wave being the R wave, and the next downward wave being the S wave. The sampling point position of the corresponding electrocardiographic waveform at the peak point of the R wave (the position of the vertical dotted line corresponding to the first wave band in fig. 4) is obtained by a Pan-Tompkins algorithm.
Step S105: the photoplethysmography signal (PPG signal) is acquired synchronously. The pulse wave signal is obtained by the pulse wave probe 1021 of the sensing unit 102, and is acquired synchronously with the single-channel electrocardiographic signal in step S101. The pulse wave probe 1021 adopts the photoelectric principle, and the arterial pulsation generated by the pulse wave signal is photo-electrically converted into a current signal. The pulse wave signal (PPG signal) in this embodiment is a transmission photocurrent measured at the finger area by the self-calibration continuous blood pressure measurement device, and may also be a reflection photocurrent at the wrist.
Step S106: and removing power frequency interference in the pulse wave signals. In the same manner as in step S102, the power frequency interference in the electrocardiographic signal is removed by using the 5-point moving average filter.
Step S107: and removing respiratory and body motion interference in the pulse wave signals. In the same manner as in step S103, respiratory and body motion disturbances in the shake signal are removed by an IIR filter with a cutoff frequency of 0.3-50 Hz.
Step S108: and searching a Peak characteristic point (Peak) and an Onset characteristic point (trough) of the pulse wave waveform backwards. Specifically, a local minimum value point of a PPG signal in a time window of 120ms-220ms after an R point of an electrocardiographic waveform is extracted as an Onset characteristic point, and sampling position information and amplitude information I of the Onset characteristic point are recordedL. Extracting a local maximum point of the PPG signal in a time window of 220ms-320ms after the R point of the electrocardiographic waveform as a Peak characteristic point, and recording the amplitude information I of the Peak point of the characteristic pointH。
Step S109: and calculating the real-time heart rate HR according to the interval of the electrocardio waveform RR, wherein the specific calculation formula is that HR is 60/RR.
RR is the time difference between two points.
Step S1010: and calculating the arrival time PAT of the pulse wave according to the R wave of the electrocardiographic wave and the characteristic point Onset of the pulse wave waveform. In particular to a sampling position difference value (the difference value is between 150ms and 220 ms) of an Onset characteristic point of an R wave of an ECG signal and a PPG pulse wave signal.
Step S1011: calculating a pulse intensity ratio PIR according to the Peak characteristic point and the Onset characteristic point of the pulse wave waveform, wherein the pulse intensity ratio PIR is defined as the pulse wave Peak amplitude IHAmplitude of trough ILThe ratio of.
And step S1012, periodically calibrating the measuring system according to the standard medical blood pressure module. Specifically, the standard medical blood pressure module regularly measures a reference blood pressure value, and the reference blood pressure value is used for regularly calibrating the training model in real time. The calibration period may be a time period of 15 minutes, 30 minutes, etc., and the calibration period may be set by the mobile phone client in a manner of wirelessly transmitting a calibration instruction.
Step S1013: blood pressure is measured according to the training model. Model parameters PAT, PIR, HR and the like are obtained by synchronously acquiring electrocardiosignals and pulse wave signals, and the model parameters are substituted into the training model to estimate the blood pressure value. The model parameters are not limited to the above parameters, and may be time domain characteristics and frequency domain characteristics derived from the pulse wave signal.
In the embodiment of the present invention, the formula of the training model is as follows:
wherein SBP and DBP represent blood pressure estimation value and DBP0，PP0，PAT0，PIR0，HR0As the initial model value, PAT, PIR, and HR are model parameters, PAT is the pulse wave arrival time calculated in step S1010, PIR is the pulse intensity ratio calculated in step S1011, and HR is the real-time heart rate calculated in step S109. The training model is not limited to the above model, and the training model may be an intelligent algorithm model such as Support Vector Regression (SVR), Least Squares (LSTM), Recurrent Neural Network (RNN), deep neural network, or the like.
In step S1014, the measurement result is displayed and the data is returned. And displaying the continuous blood pressure measurement result obtained by analyzing in real time through a display screen, transmitting the measurement result back to the intelligent client in real time, and finally transmitting the measurement result back to a back-end big data platform for storage and analysis.
The specific operation flow is as follows: in this embodiment, PAT, PIR, and HR are independent variables, and SBP and DBP are dependent variables. The standard medical blood pressure module provides blood pressure calibration value SBP0，DBP0，PP0Using the reference blood pressure value pairTraining the model to perform regular self calibration, and measuring the initial value PAT of the model parameter in the calibration process0，PIR0，HR0After self-calibration is completed, the features extracted based on the ECG waveform and the PPG waveform are substituted into the training model to estimate the blood pressure value, specifically, in this embodiment, model parameters (proposed features) PAT, PIR, and HR obtained from the ECG signal and the pulse wave signal that are synchronously acquired are substituted into the training model to estimate the blood pressure value.
The invention has the advantages that:
1. the self-calibration continuous blood pressure measuring device can realize automatic calibration and compensation of blood pressure measurement in the using process of a user, and effectively inhibit data drift in the long-term measuring process;
2. the self-calibration continuous blood pressure measuring device can accurately track the continuous change of the blood pressure parameter of a user in real time;
3. the self-calibration continuous blood pressure measuring method provided by the invention is characterized in that a blood pressure estimation model is constructed according to the detected waveform characteristics, and specifically, a blood pressure value is estimated by detecting the pulse wave arrival time PAT, the pulse intensity ratio PIR, the real-time heart rate HR and other characteristic parameters and substituting the characteristic parameters into the blood pressure estimation model.
4. The intelligent client serves as transfer equipment to realize reliable data transmission;
5. and long-term tracking is carried out on the data by combining a back-end big data platform.
The above detailed description merely describes preferred embodiments of the present invention and does not limit the scope of the invention. Without departing from the spirit and scope of the present invention, it should be understood that various changes, substitutions and alterations can be made herein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents. The scope of the invention is defined by the claims.
Claims (10)
1. A self-calibrating continuous blood pressure measurement method is characterized by comprising the following steps,
s101: collecting single-channel electrocardiosignals;
s102: removing power frequency interference in the single-channel electrocardiosignal;
s103: respiratory and body movement interference in the single-channel electrocardiosignal is removed;
s104: acquiring an electrocardio waveform R wave by a Pan-Tompkins algorithm;
s105: synchronously collecting photoplethysmography signals;
s106: removing power frequency interference in the pulse wave signals;
s107: removing respiratory and body movement interference in the pulse wave signal;
s108: searching Peak characteristic points and Onset characteristic points of the pulse wave waveform backwards;
s109: calculating a real-time heart rate HR according to the RR interval of the electrocardiographic waveform;
s1010: calculating the arrival time PAT of the pulse wave according to the characteristic points of the electrocardio signal R wave and the pulse wave signal Onset;
s1011: calculating a pulse intensity ratio PIR according to Peak characteristic points and Onset characteristic points of the pulse wave waveform;
s1012: the standard medical blood pressure module regularly calibrates the measurement system;
s1013: measuring blood pressure through a training model;
s1014: and displaying the measurement result and returning the data.
2. The self-calibrating continuous blood pressure measuring method according to claim 1, wherein in step S102 and step S106, a 5-point moving average filter is used to remove power frequency interference.
3. The self-calibration continuous blood pressure measuring method of claim 1, wherein in step S103 and step S107, an IIR filter or a digital band-pass FIR filter with a cut-off frequency of 0.3-50Hz is used to remove respiratory and body motion disturbances.
4. The self-calibration continuous blood pressure measuring method according to claim 1, wherein in step S108, the local minimum point of the pulse wave signal in the 120ms-220ms time window after the electrocardiographic waveform R wave is extracted as the Onset feature point, the sampling position information and the amplitude information IL of the Onset feature point of the feature point are recorded, the local maximum point of the pulse wave signal in the 220ms-320ms time window after the electrocardiographic waveform R wave is extracted as the Peak feature point, and the amplitude information IH of the Peak feature point of the feature point is recorded.
5. The self-calibrating continuous blood pressure measuring method according to claim 1, wherein in step S1010, the pulse wave arrival time PAT is a difference between an R-wave of an electrocardiographic waveform and a characteristic point Onset sampling position of a pulse wave waveform.
6. The self-calibration continuous blood pressure measuring method of claim 4, wherein in step S1011, the pulse intensity ratio PIR is the ratio of the pulse wave waveform Peak characteristic point amplitude information IIH and Onset characteristic point amplitude information IL.
7. The self-calibrating continuous blood pressure measuring method according to claim 1, wherein in step S1013, a blood pressure estimation model is proposed, and the obtained training model is formulated as:
wherein SBP and DBP represent blood pressure estimation value and DBP0，PP0，PAT0，PIR0，HR0As the initial model value, PAT, PIR, and HR are model parameters, PAT is the pulse wave arrival time calculated in step S1010, PIR is the pulse intensity ratio calculated in step S1011, and HR is the real-time heart rate calculated in step S109.
8. The self-calibration continuous blood pressure measuring method according to claim 1, wherein in step S1014, the continuous blood pressure measurement result obtained in step S1013 is displayed in real time through a display screen, and the measurement result is transmitted back to the smart client in real time, and finally transmitted back to the back-end big data platform for storage and analysis by the smart client.
9. The self-calibration continuous blood pressure measuring device designed by the self-calibration continuous blood pressure measuring method according to any one of claims 1 to 10, wherein the measuring device comprises a sensing unit, an air bag cuff, a microprocessor, a blood pressure calibration module, a wireless communication module, a display module and a power supply module, the air bag cuff and the blood pressure calibration module are in pneumatic connection, the sensing unit, the microprocessor, the blood pressure calibration module, the wireless communication module and the display module are sequentially in electrical connection, and the power supply module is electrically connected with the sensing unit, the microprocessor, the blood pressure calibration module, the wireless communication module and the display module.
10. The self-calibrating continuous blood pressure measuring device of claim 9, wherein said sensing unit comprises an electrocardio-electrode and a pulse wave probe.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113303813A (en) * | 2021-05-12 | 2021-08-27 | 西安电子科技大学 | Lightweight electrocardiosignal noise removal and QRS waveform detection method |
| CN114027810A (en) * | 2021-03-31 | 2022-02-11 | 北京超思电子技术有限责任公司 | Method for generating blood pressure calculation model containing arteriosclerosis classification and blood pressure measuring system |
| 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 |
| CN114617536A (en) * | 2022-01-14 | 2022-06-14 | 深圳技术大学 | Blood pressure prediction method and device and computer readable storage medium |
| CN115137316A (en) * | 2022-06-10 | 2022-10-04 | 佳禾智能科技股份有限公司 | Multi-physiological-parameter monitoring watch and monitoring method thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170202459A1 (en) * | 2016-01-19 | 2017-07-20 | Zansors Llc | Wireless monitoring system |
| CN107865647A (en) * | 2016-09-28 | 2018-04-03 | 京东方科技集团股份有限公司 | The bearing calibration of blood pressure detector and blood pressure detector |
| CN108720821A (en) * | 2018-05-28 | 2018-11-02 | 京东方科技集团股份有限公司 | A kind of blood pressure estimation device and blood pressure estimating system |
| CN111481185A (en) * | 2020-03-24 | 2020-08-04 | 南京润楠医疗电子研究院有限公司 | Continuous blood pressure estimation device and method based on pre-ejection period |
-
2020
- 2020-12-04 CN CN202011408698.2A patent/CN112450899A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170202459A1 (en) * | 2016-01-19 | 2017-07-20 | Zansors Llc | Wireless monitoring system |
| CN107865647A (en) * | 2016-09-28 | 2018-04-03 | 京东方科技集团股份有限公司 | The bearing calibration of blood pressure detector and blood pressure detector |
| CN108720821A (en) * | 2018-05-28 | 2018-11-02 | 京东方科技集团股份有限公司 | A kind of blood pressure estimation device and blood pressure estimating system |
| CN111481185A (en) * | 2020-03-24 | 2020-08-04 | 南京润楠医疗电子研究院有限公司 | Continuous blood pressure estimation device and method based on pre-ejection period |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114027810A (en) * | 2021-03-31 | 2022-02-11 | 北京超思电子技术有限责任公司 | Method for generating blood pressure calculation model containing arteriosclerosis classification and blood pressure measuring system |
| CN114027810B (en) * | 2021-03-31 | 2024-03-26 | 北京超思电子技术有限责任公司 | Blood pressure calculation model generation method containing arteriosclerosis classification and blood pressure measurement system |
| CN113303813A (en) * | 2021-05-12 | 2021-08-27 | 西安电子科技大学 | Lightweight electrocardiosignal noise removal and QRS waveform detection method |
| 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 |
| CN114617536A (en) * | 2022-01-14 | 2022-06-14 | 深圳技术大学 | Blood pressure prediction method and device and computer readable storage medium |
| CN115137316A (en) * | 2022-06-10 | 2022-10-04 | 佳禾智能科技股份有限公司 | Multi-physiological-parameter monitoring watch and monitoring method thereof |
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