CN206792400U - HRV detection means - Google Patents
HRV detection means Download PDFInfo
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- CN206792400U CN206792400U CN201720050167.8U CN201720050167U CN206792400U CN 206792400 U CN206792400 U CN 206792400U CN 201720050167 U CN201720050167 U CN 201720050167U CN 206792400 U CN206792400 U CN 206792400U
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Abstract
It the utility model is related to HRV detection means.The HRV detection means includes：Data acquisition module, gather the original electrocardiographicdigital diagram data of testee；R ripple detection modules, it is connected to data acquisition module, original electrocardiographicdigital diagram data is received from data acquisition module, R ripples are detected from original electrocardiographicdigital diagram data, according to R ripples obtain RR between phase and NN the phase time series signal, the phase is the time interval between two adjacent R ripples between RR, phase between whole sinus property heartbeat RR phase between NN；HRV data acquisition module, be connected to R ripple detection modules, from R ripples detection module receive time series signal, according to time series signal sequentially in time or heartbeat order arrangement obtain HRV data.According to HRV detection means of the present utility model, using original electrocardiographicdigital figure, accurately comprehensively analysis obtains the time domain specification and frequency domain characteristic of HRV data, so as to help to analyze the body stress or disease process of testee.
Description
Technical field
Health examination field is the utility model is related to, more particularly to a kind of HRV detection means.
Background technology
HRV (HRV) refers to the phenomenon of sinus rate periodically-varied within a certain period of time, is that reaction is sympathetic-secondary
The important indicator of sympathetic tone and its balance, autonomic nerves system activity and qualitative assessment cardiac sympathetic nerve can be reflected
With vagal tone and its balance.If being capable of the active detection means of quantitative response autonomic nerve, to evaluating the heart
The change of autonomic nerve has very important clinical value in vascular diseases and neuroendocrine disorder process.The autonomous god of measurement
Activity through system, reflect its sympathetic and parasympathetic balanced capacity, draw the related multiple parameters of heart rate variability, and then
Assess and the body stress of analysis testee or disease process are to avoid sudden cardiac death and arrhythmia cordis sexual behavior part.
Utility model content
The purpose of this utility model is to provide a kind of test dress for obtaining the multiple parameters related to HRV
Put, the test device can carry out quantitative analysis to the parameter of acquisition, so as to which testing staff can be to the body spirit of testee
Pressure or disease process are inferred and predicted.
According to one side of the present utility model, there is provided a kind of HRV detection means, the HRV detection
Device includes：Data acquisition module, gather the original electrocardiographicdigital diagram data of testee；R ripple detection modules, are connected to data acquisition
Module, original electrocardiographicdigital diagram data is received from data acquisition module, detect R ripples from original electrocardiographicdigital diagram data, and RR is obtained according to R ripples
Between between phase and NN the phase time series signal, wherein, the phase is the time interval between two adjacent R ripples between RR, the phase between NN
The phase between whole sinus property heartbeat RR；HRV data acquisition module, R ripple detection modules are connected to, are connect from R ripple detection modules
Receive time series signal, according to time series signal sequentially in time or heartbeat order arrangement obtain HRV data.
The HRV detection means also includes：Time-domain analysis module, it is connected to HRV data acquisition mould
Block, HRV data are received from HRV data acquisition module, are carried out time-domain analysis to HRV data, are obtained
It is at least one into SDNN, RMSSD, NN50 and PNN50, wherein, the standard deviation of SDNN phases between whole sinus property heartbeat RR,
The root-mean-square value of the difference of phase, the difference of NN50 phases between interim adjacent NN between whole NN are more than 50 between RMSSD is whole adjacent NN
Millisecond heart rate, PNN50 between NN50 divided by total NN phase number multiplied by with 100 obtained values.
HRV is presented using Poincare (Poincare) scatter diagram in the time-domain analysis module.
The HRV detection means may also include：Frequency-domain analysis module, it is connected to HRV data acquisition
Module, HRV data are received from HRV data acquisition module, and frequency-domain analysis is carried out to HRV data,
Obtain at least one in general power, very low frequencies power, low frequency power and the high frequency power of photoplethysmographic.
The HRV detection means may also include：Display module, be connected to HRV data acquisition module,
It is at least one in time-domain analysis module and frequency-domain analysis module, and show HRV data, the output of time-domain analysis module
Time-domain analysis result and frequency-domain analysis module output frequency-domain analysis result in it is at least one.
The data acquisition module is the measuring electrode for contacting the cardia of testee.
Compared with prior art, according to HRV detection means of the present utility model original electrocardiographicdigital figure can be utilized accurate
Really, comprehensively analysis obtains the time domain specification and frequency domain characteristic of HRV data, so as to help to analyze testee's
Body stress or disease process.
Brief description of the drawings
Fig. 1 is the structured flowchart according to the HRV detection means of embodiment of the present utility model.
Fig. 2 is the diagram of the time series signal of phase between the collection RR according to embodiment of the present utility model.
Fig. 3 is the diagram according to the HRV of embodiment of the present utility model.
Fig. 4 is the Poincare scatter diagrams according to embodiment of the present utility model.
Fig. 5 is the diagram for carrying out spectrum analysis to HRV using FFT methods according to embodiment of the present utility model.
Fig. 6 is the distribution schematic diagram relative to frequency according to the power of embodiment of the present utility model.
Embodiment
To enable above-mentioned purpose of the present utility model, feature and advantage more obvious understandable, below in conjunction with the accompanying drawings and tool
Body embodiment is described in further detail to the utility model.
Fig. 1 is the structured flowchart according to the HRV detection means of embodiment of the present utility model.
Data acquisition module 101, R ripples detection module 102 are included according to HRV detection means of the present utility model
With HRV data acquisition module 103.Data acquisition module 101 is adopted to the original electrocardiographicdigital diagram data of testee
Collection, and original electrocardiographicdigital diagram data is sent to R ripples detection module 102.R ripples detection module 102 is connected to data acquisition module
101, original electrocardiographicdigital diagram data is received from data acquisition module 101, R ripple detections are carried out to original electrocardiographicdigital diagram data, and according to R ripples
Obtain RR between phase and NN the phase time series signal.Time interval between RR between the adjacent R ripples of phase two, between NN the phase be
Phase between whole sinus property heartbeat RR.HRV data acquisition module 103 is connected to R ripples detection module 102, and mould is detected from R ripples
Block 102 receives the time series signal, and according to the time series signal sequentially in time or heartbeat order arranges
To HRV data.In one embodiment, the data acquisition module 101 can be the heart portion for contacting testee
The measuring electrode of position.
Time-domain analysis module 104, time-domain analysis mould may also include according to HRV detection means of the present utility model
Block 104 is connected to HRV data acquisition module 103, and heart rate variability is received from HRV data acquisition module 103
Property data, to HRV data carry out time-domain analysis, obtain at least one in SDNN, RMSSD, NN50 and PNN50.
SDNN is the standard deviation of whole normal sinus IBIs, and the root-mean-square value of the difference of phase, NN50 are between RMSSD is whole adjacent NN
The difference of phase is more than the heart rate of 50 milliseconds (ms), PNN50 phases between NN50 divided by total NN between interim adjacent NN between whole NN
Number is multiplied by the value obtained using 100 (unit is %).
In addition, the time-domain analysis module 104 can describe time-domain analysis HRV using Poincare scatter diagrams
Measurement.
The HRV detection means may also include frequency-domain analysis module 105, and the frequency-domain analysis module 105 connects
To HRV data acquisition module 103.Frequency-domain analysis module 105 receives the heart from HRV data acquisition module 103
Rate variability data, frequency-domain analysis is carried out to HRV data, so as to obtain the general power of photoplethysmographic, extremely low
It is at least one in frequency power, low frequency power and high frequency power.
The HRV detection means may also include display module 106, and display module 106 may be connected to heart rate variability
It is at least one in property data acquisition module 103, time-domain analysis module 104 and frequency-domain analysis module 105, with to HRV
In the frequency-domain analysis result that the time-domain analysis result and frequency-domain analysis module 105 that data, time-domain analysis module 104 export export
It is at least one shown, so that testing staff intuitively observes testing result, so as to analyze the heart rate variability of testee
Property.
Fig. 2 is the diagram of the time series signal of phase between the collection RR according to embodiment of the present utility model.Such as Fig. 2 institutes
Show, by carrying out R ripple detections to original electrocardiographicdigital diagram data, obtain the time series signal of phase between RR.
Fig. 3 is the diagram according to the HRV of embodiment of the present utility model.By the time sequence of phase between the RR obtained in Fig. 2
Column signal is arranged with time sequencing or heartbeat order, can obtain the HRV data shown in Fig. 3.By in Fig. 3
HRV data carry out time-domain analysis, can obtain SDNN, RMSSD, NN50 and PNN50.In addition, it can be calculated according to time domain waveform
Go out root mean square of pulse frequency, standard deviation, continuity difference etc., and can by pulse frequency, standard deviation, continuity difference root mean square
Etc. being embodied in heart rate variability form.
Fig. 4 is the Poincare scatter diagrams according to embodiment of the present utility model.Based on issue between the RR obtained from Fig. 3
According to Poincare scatter diagrams can be drawn.The coronary artery of testee can be judged by the morphological analysis to Poincare scatter diagrams
Lesion degree, for example, the scatter diagram form of normal person is comet formation, the scatter diagram of coronary artery Single vessel disease patient is in torpedo-shaped or short
Bar-shaped, the scatter diagram of coronary artery multi-vessel lesion patient is in round point shape or thick bar-shaped.
Fig. 5 is the diagram for carrying out spectrum analysis to HRV using FFT methods according to embodiment of the present utility model.Such as Fig. 5 institutes
Show, time-domain signal is converted into very low frequencies signal (VLF), low frequency signal (LF) and high-frequency signal (HF).
Fig. 6 is the distribution schematic diagram relative to frequency according to the power of embodiment of the present utility model.Can be according to frequency domain work(
Rate composes the general power (ms that photoplethysmographic is calculated2), very low frequencies power (ms2), high frequency power (ms2) and low frequency work(
Rate (ms2), calculate low-frequency standard value so as to the general power according to calculating, very low frequencies power, high frequency power and low frequency power
(n.u.) and high-frequency standard value (n.u.), and above result of calculation can be embodied in heart rate variability form.
Compared with prior art, according to HRV detection means of the present utility model original electrocardiographicdigital figure can be utilized accurate
Really, comprehensively analysis obtains the time domain specification and frequency domain characteristic of HRV data, so as to help to analyze testee's
Body stress or disease process.
Embodiment in above-described embodiment can be further combined or replace, and embodiment is only new to this practicality
The preferred embodiment of type is described, and not spirit and scope of the present utility model are defined, new not departing from this practicality
On the premise of type design philosophy, various change that professional and technical personnel in the art make to the technical solution of the utility model and
Improve, belong to the scope of protection of the utility model.
Claims (6)
1. a kind of HRV detection means, it is characterised in that the HRV detection means includes：
Data acquisition module, gather the original electrocardiographicdigital diagram data of testee；
R ripple detection modules, are connected to data acquisition module, original electrocardiographicdigital diagram data are received from data acquisition module, from the original heart
Electromyographic data detect R ripples, and according to R ripples obtain RR between phase and NN the phase time series signal, wherein, between RR the phase be two
Time interval between adjacent R ripples, phase between whole sinus property heartbeat RR phase between NN；
HRV data acquisition module, R ripple detection modules are connected to, time series signal, root are received from R ripples detection module
According to time series signal sequentially in time or heartbeat order arrangement obtain HRV data.
2. HRV detection means as claimed in claim 1, it is characterised in that the HRV detection means is also
Including：Time-domain analysis module, HRV data acquisition module is connected to, the heart is received from HRV data acquisition module
Rate variability data, time-domain analysis is carried out to HRV data, obtained in SDNN, RMSSD, NN50 and PNN50 extremely
It is few one,
Wherein, the standard deviation of SDNN phases between whole sinus property heartbeat RR, the root mean square of the difference of phase between RMSSD is whole adjacent NN
Value, the difference of NN50 phases between interim adjacent NN between whole NN are more than 50 milliseconds of heart rate, and PNN50 is NN50 divided by total NN
Between phase number multiplied by with 100 obtained values.
3. HRV detection means as claimed in claim 2, it is characterised in that time-domain analysis module is dissipated using Poincare
HRV is presented in point diagram.
4. HRV detection means as claimed in claim 2, it is characterised in that the HRV detection means is also
Including：Frequency-domain analysis module, HRV data acquisition module is connected to, the heart is received from HRV data acquisition module
Rate variability data, frequency-domain analysis is carried out to HRV data, obtains general power, the very low frequencies work(of photoplethysmographic
It is at least one in rate, low frequency power and high frequency power.
5. HRV detection means as claimed in claim 4, it is characterised in that the HRV detection means is also
Including：Display module, it is connected in HRV data acquisition module, time-domain analysis module and frequency-domain analysis module at least
One, and show HRV data, the time-domain analysis result of time-domain analysis module output and the output of frequency-domain analysis module
Frequency-domain analysis result in it is at least one.
6. HRV detection means as claimed in claim 1, it is characterised in that data acquisition module is that contact is tested
The measuring electrode of the cardia of person.
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| CN201720050167.8U CN206792400U (en) | 2017-01-16 | 2017-01-16 | HRV detection means |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109157211A (en) * | 2018-08-14 | 2019-01-08 | 江苏师范大学 | A kind of portable cardiac on-line intelligence monitoring diagnosis system design method |
| CN109350020A (en) * | 2018-11-21 | 2019-02-19 | 新绎健康科技有限公司 | Psychosomatic health analytical equipment and method |
| CN110141205A (en) * | 2019-05-27 | 2019-08-20 | 深圳市是源医学科技有限公司 | Resistance to compression data, the test method of fatigue data and device based on HRV technology |
| CN110946573A (en) * | 2019-11-01 | 2020-04-03 | 东软集团股份有限公司 | Cardiac arrest detection device, detection model training device, method and equipment |
| CN111374647A (en) * | 2018-12-29 | 2020-07-07 | 中兴通讯股份有限公司 | Method and device for detecting pulse wave and electronic equipment |
| CN111920399A (en) * | 2020-09-04 | 2020-11-13 | 迪姆软件(北京)有限公司 | Analysis method and device for heart rate variability |
| CN112237421A (en) * | 2020-09-23 | 2021-01-19 | 浙江大学山东工业技术研究院 | Video-based dynamic heart rate variability analysis model |
| CN113712570A (en) * | 2020-05-12 | 2021-11-30 | 深圳市科瑞康实业有限公司 | Long-interval electrocardiosignal data early warning method |
| CN114403889A (en) * | 2022-03-14 | 2022-04-29 | 武汉中旗生物医疗电子有限公司 | NN interval signal acquisition device, system and storage medium |
| WO2022089289A1 (en) * | 2020-10-30 | 2022-05-05 | 华为技术有限公司 | Signal processing method and apparatus |
| CN114451879A (en) * | 2022-03-15 | 2022-05-10 | 武汉中旗生物医疗电子有限公司 | Intelligent heart rate variability analysis system |
| CN114847906A (en) * | 2022-04-12 | 2022-08-05 | 河北大学 | Electrocardiosignal heart rate variability feature extraction method |
| CN117918811A (en) * | 2024-03-21 | 2024-04-26 | 佛山需要智能机器人有限公司 | Heart rate variability pressure detection method and related assembly integrating attention mechanism |
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2017
- 2017-01-16 CN CN201720050167.8U patent/CN206792400U/en active Active
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109157211A (en) * | 2018-08-14 | 2019-01-08 | 江苏师范大学 | A kind of portable cardiac on-line intelligence monitoring diagnosis system design method |
| CN109350020A (en) * | 2018-11-21 | 2019-02-19 | 新绎健康科技有限公司 | Psychosomatic health analytical equipment and method |
| CN111374647A (en) * | 2018-12-29 | 2020-07-07 | 中兴通讯股份有限公司 | Method and device for detecting pulse wave and electronic equipment |
| CN110141205B (en) * | 2019-05-27 | 2022-03-11 | 深圳市是源医学科技有限公司 | HRV technology-based compression data and fatigue data testing method and device |
| CN110141205A (en) * | 2019-05-27 | 2019-08-20 | 深圳市是源医学科技有限公司 | Resistance to compression data, the test method of fatigue data and device based on HRV technology |
| CN110946573A (en) * | 2019-11-01 | 2020-04-03 | 东软集团股份有限公司 | Cardiac arrest detection device, detection model training device, method and equipment |
| CN113712570B (en) * | 2020-05-12 | 2024-03-08 | 深圳市科瑞康实业有限公司 | Long intermittent electrocardiosignal data early warning method |
| CN113712570A (en) * | 2020-05-12 | 2021-11-30 | 深圳市科瑞康实业有限公司 | Long-interval electrocardiosignal data early warning method |
| CN111920399A (en) * | 2020-09-04 | 2020-11-13 | 迪姆软件(北京)有限公司 | Analysis method and device for heart rate variability |
| CN112237421A (en) * | 2020-09-23 | 2021-01-19 | 浙江大学山东工业技术研究院 | Video-based dynamic heart rate variability analysis model |
| WO2022089289A1 (en) * | 2020-10-30 | 2022-05-05 | 华为技术有限公司 | Signal processing method and apparatus |
| CN114403889A (en) * | 2022-03-14 | 2022-04-29 | 武汉中旗生物医疗电子有限公司 | NN interval signal acquisition device, system and storage medium |
| CN114403889B (en) * | 2022-03-14 | 2024-07-05 | 武汉中旗生物医疗电子有限公司 | NN interval signal acquisition device, NN interval signal acquisition system and storage medium |
| CN114451879A (en) * | 2022-03-15 | 2022-05-10 | 武汉中旗生物医疗电子有限公司 | Intelligent heart rate variability analysis system |
| CN114451879B (en) * | 2022-03-15 | 2024-04-09 | 武汉中旗生物医疗电子有限公司 | Intelligent heart rate variability analysis system |
| CN114847906A (en) * | 2022-04-12 | 2022-08-05 | 河北大学 | Electrocardiosignal heart rate variability feature extraction method |
| CN117918811A (en) * | 2024-03-21 | 2024-04-26 | 佛山需要智能机器人有限公司 | Heart rate variability pressure detection method and related assembly integrating attention mechanism |
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