CN110448289A - A kind of heart rate variability (HRV) analysis method, device, storage medium and equipment - Google Patents
A kind of heart rate variability (HRV) analysis method, device, storage medium and equipment Download PDFInfo
- Publication number
- CN110448289A CN110448289A CN201910648951.2A CN201910648951A CN110448289A CN 110448289 A CN110448289 A CN 110448289A CN 201910648951 A CN201910648951 A CN 201910648951A CN 110448289 A CN110448289 A CN 110448289A
- Authority
- CN
- China
- Prior art keywords
- hrv
- ecg data
- index
- evaluation
- obtains
- 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
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
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02405—Determining heart rate variability
-
- 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
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- 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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Pathology (AREA)
- Medical Informatics (AREA)
- Veterinary Medicine (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Physiology (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Cardiology (AREA)
- Power Engineering (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The present invention relates to medical signals processing technology field more particularly to a kind of heart rate variability (HRV) analysis method, device, storage medium and equipment.The described method includes: obtaining ECG data；According to ECG data selection analysis section, short distance ECG data is obtained；It is denoised according to the short distance ECG data, obtains ECG data to be analyzed；It is calculated according to the ECG data to be analyzed, obtains HRV time domain index, HRV frequency-domain index, HRV triangle index index；Obtain the clinical settings data of user corresponding with the ECG data；It is calculated according to the HRV time domain index, the HRV frequency-domain index, the HRV triangle index index, obtains HRV evaluation and test integral；It is calculated according to the clinical settings data, obtains clinical settings integral；It is calculated according to HRV evaluation and test integral, clinical settings integral, obtains HRV scoring.Therefore, operation of the present invention is easy, diagnostic accuracy is high, automatically analyzes short distance HRV.
Description
Technical field
The present invention relates to medical signals processing technology field more particularly to a kind of heart rate variability (HRV) analysis methods, dress
It sets, storage medium and equipment.
Background technique
Heart rate variability (HRV) is the index of function when reflecting heart disease, and reflection heart is to autonomic nerve (sympathetic secondary friendship
Sense) controlled ability.The analysis Major Clinical application of HRV: the assessment of heart failure, malignant arrhythmia cardiac sudden death.Clinically
HRV mainly has 3 kinds of forms: 24 hours long-range HRV, short distance HRV, at times HRV, is all in dynamic ECG or static electrocardiogram
On the basis of inspection, the further analytical calculation of source data is obtained by proprietary software.For HRV normal value health/patient it
Between there are problems that obvious overlapping intersect, difficult diagnosis, therefore it provides it is a kind of it is easy to operate, diagnose accurately multifactor short distance
HRV automatic analysis method is particularly important.
Summary of the invention
Based on this, it is necessary in view of the above-mentioned problems, proposing a kind of heart rate variability (HRV) analysis method, device, storage
Medium and equipment.
In a first aspect, the present invention provides a kind of heart rate variability (HRV) analysis methods, which comprises
Obtain ECG data；
According to ECG data selection analysis section, short distance ECG data is obtained；
It is denoised according to the short distance ECG data, obtains ECG data to be analyzed；
It is calculated according to the ECG data to be analyzed, obtains HRV time domain index, HRV frequency-domain index, HRV tri-
Angle index index；
Obtain the clinical settings data of user corresponding with the ECG data；
It is calculated, is obtained according to the HRV time domain index, the HRV frequency-domain index, the HRV triangle index index
HRV evaluation and test integral；
It is calculated according to the clinical settings data, obtains clinical settings integral；
It is calculated according to HRV evaluation and test integral, clinical settings integral, obtains HRV scoring.
Second aspect, the present invention also provides a kind of heart rate variability analysis device, described device includes:
Electrocardiogram management module, for recording ECG data；
Selection analysis section module, according to ECG data selection analysis section, is obtained for obtaining ECG data
To short distance ECG data；
It denoises module and obtains ECG data to be analyzed for being denoised according to the short distance ECG data；
Clinical settings obtain module, for obtaining the clinical settings data of user corresponding with the ECG data；
HRV grading module, for being calculated according to the ECG data to be analyzed, obtain HRV time domain index,
HRV frequency-domain index, HRV triangle index index refer to according to the HRV time domain index, the HRV frequency-domain index, the HRV triangle
Number index is calculated, and is obtained HRV evaluation and test integral, is calculated according to the clinical settings data, obtains clinical settings integral,
It is calculated according to HRV evaluation and test integral, clinical settings integral, obtains HRV scoring.
The third aspect, the present invention also provides a kind of storage mediums, are stored with computer program of instructions, and the computer refers to
When program being enabled to be executed by processor, so that the step of processor executes any one of first aspect the method.
Fourth aspect, the present invention also provides a kind of computer equipments, including at least one processor, at least one processing
Device, the memory is stored with computer program of instructions, when the computer program of instructions is executed by the processor, so that institute
State the step of processor executes any one of first aspect the method.
In conclusion a kind of heart rate variability (HRV) analysis method of the invention is according to ECG data selection point
Section is analysed, short distance ECG data is obtained；It carries out HRV time domain is calculated after denoising the short distance ECG data and refer to
Mark, HRV frequency-domain index, HRV triangle index index, according to the HRV time domain index, the HRV frequency-domain index, the HRV tri-
Angle index index, the clinical settings data carry out that HRV scoring is calculated；Pass through the index combination clinical settings data of HRV
HRV scoring is obtained, the accuracy of HRV scoring is improved, to improve the accuracy of diagnosis；The method passes through selection analysis
Section, denoising calculate HRV index, calculate HRV scoring, easy to operate, realize and automatically analyze.Therefore, operation of the present invention letter
Just, diagnostic accuracy is high, automatically analyzes short distance HRV.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Wherein:
Fig. 1 is a kind of flow chart of heart rate variability (HRV) analysis method in one embodiment；
Fig. 2 is that HRV evaluation and test integral flow chart is calculated in one embodiment；
Fig. 3 is that HRV evaluation and test integral flow chart is calculated in one embodiment；
Fig. 4 is to calculate clinical settings in one embodiment to integrate flow chart；
Fig. 5 is that ECG data denoises flow chart in one embodiment；
Fig. 6 is a kind of structural block diagram of heart rate variability (HRV) analytical equipment in one embodiment；
Fig. 7 is the structural block diagram of computer equipment in one embodiment.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Heart rate variability (HRV) refers to the situation of change of gradually heart beat cycle difference, it contains Neurohormonal factor to the heart
The information that vascular system is adjusted, to judge its state of an illness and prevention to diseases such as angiocarpy, it may be possible to predict sudden cardiac death
With a valuable index of arrhythmia cordis sexual behavior part.
As described in Figure 1, in one embodiment, a kind of heart rate variability (HRV) analysis method is improved, the method is real
Existing following steps:
S102, ECG data is obtained；
Electrocardiogram refer to heart in each cardiac cycle, it is in succession excited by pacemaker, atrium, ventricle, along with biology
The variation of electricity, the figure of the potential change of diversified forms is drawn by electrocardiograph from body surface, and electrocardio measuring technique has been sent out
It opens up to 18 leads.The inspection meaning of electrocardiogram is: for various arrhythmia cordis, ventricular atrial hypertrophy, myocardial infarction, the heart
The illnesss inspection such as myocardial ischemia.
S104, according to ECG data selection analysis section, obtain short distance ECG data；
Specifically, selecting one of lead according to the ECG data, part duration is intercepted from the lead
ECG data is as short distance ECG data.
The short distance ECG data is the ECG data that part duration is intercepted in a lead of electrocardiogram, part
The ECG data of duration refers to that duration is the ECG data not less than 5 minutes and not higher than 4 hours, for example, II lead
Wherein 5 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, citing is not especially limited herein.
S106, it is denoised according to the short distance ECG data, obtains ECG data to be analyzed；
Specifically, removing noise according to the short distance ECG data obtains ECG data to be analyzed.To mention
The high accuracy of this method.
Denoising is to remove the wave that non-electrocardio issues in the short distance ECG data, and the wave that non-electrocardio issues is some other
Interference and artifact caused by the noise of the generation of reason can not accurately calculate HRV scoring if do not removed interference and artifact.
S108, calculated according to the ECG data to be analyzed, obtain HRV time domain index, HRV frequency-domain index,
HRV triangle index index；
The variable of the HRV time domain index include SDNN (unit: ms, the standard deviation of all NN interphases), SDANN (unit:
Ms, all average NN interphase standard deviation in all 5 minute periods in record), (unit: the difference of ms, adjacent NN interphase are flat by RMSSD
The square root of side and mean value), SDNN index (unit: ms, all in record in all 5 minute periods NN interphase standard deviation it is equal
Value), SDSD (unit: ms, the standard deviation of the difference of adjacent NN interphase), NN50 count (unit: ms, all in record between adjacent NN
The difference of phase greater than 50ms at logarithm or count NN interphase below it is long at logarithm), PNN50 (unit: %, NN50 count divided by
The sum of NN interphase).The calculating of the variable of the HRV time domain index can be selected from the prior art, and therefore not to repeat here.
The HRV frequency-domain index is that each RR interphase change degree numerical value is carried out Fast Fourier Transform (FFT) (FFT), after processing
Power spectrum arranged from low to high according to frequency, 0~0.15Hz is defined as low frequency LF, is height by the power definition of > 0.15Hz
Frequently, LF/HF ratio is calculated.The calculating of the HRV frequency-domain index can be selected from the prior art, and therefore not to repeat here.
The HRV triangle index index, which refers to, is grouped RR interphase from length is short to, and organizes spacing 7.8125ms, identical width
The RR interphase of degree is same to organize superposition, triangle index=height/low width.The calculating of the HRV triangle index index can be from existing
The choice of technology, therefore not to repeat here.
S110, the clinical settings data for obtaining user corresponding with the ECG data；
The clinical settings data include diabetic history, chronic heart failure, ventricular hypertrophy, coronary heart disease myocardial infarction, cardiomyopathy
Or other organic heart diseasies.
S112, it is calculated according to the HRV time domain index, the HRV frequency-domain index, the HRV triangle index index,
Obtain HRV evaluation and test integral；
The HRV evaluation and test integral is calculated according to ECG data.
S114, it is calculated according to the clinical settings data, obtains clinical settings integral；
It is calculated according to the medical history of the clinical settings data, obtains clinical settings integral.
The medical history includes diabetic history, chronic heart failure, ventricular hypertrophy, coronary heart disease myocardial infarction, cardiomyopathy or other devices
Matter heart disease.
S116, it is calculated according to HRV evaluation and test integral, clinical settings integral, obtains HRV scoring.
Specifically, HRV evaluation and test integral is added to obtain HRV scoring with clinical settings integral.
The HRV scoring is for assisting the assessment of heart failure, the assessment of malignant arrhythmia cardiac sudden death.
The method obtains HRV scoring by the index combination clinical settings data of HRV, improves the accurate of HRV scoring
Property, to improve the accuracy of diagnosis；The method is commented by selection analysis section, denoising, calculating HRV index, calculating HRV
Point, it is easy to operate, it realizes and automatically analyzes.Therefore, operation of the present invention is easy, diagnostic accuracy is high, automatically analyzes short distance HRV.
As described in Figure 2, in one embodiment, it is described according to the HRV time domain index, it is the HRV frequency-domain index, described
HRV triangle index index is calculated, and is obtained HRV evaluation and test integral, is specifically included:
S202, short distance ECG data corresponding actually detected total time is obtained；
The actually detected total time is the time span for the short distance ECG data selected from electrocardiogram.
S204, it is calculated according to the short distance ECG data corresponding actually detected total time, obtains detection time
Coefficient；
Specifically, setting Kt as detection time coefficient, ts is actually detected total time (unit: minute)
Kt=1+ (1-ts/ (5*288))
S206, it is calculated according to the HRV time domain index, the detection time coefficient, obtains the evaluation and test of HRV time domain index
Value；
Specifically, SDNN evaluation and test value, SDANN is calculated according to the HRV time domain index, the detection time coefficient
Evaluation and test value, RMSSD evaluation and test value, SDSD index evaluation and test value, SDNN index evaluation and test value, PNN50 evaluation and test value.
If evaluation and test value is A, actual measured value B, evaluation and test value calculation is as follows:
A=B*Kt
Wherein, when A is SDNN evaluation and test value, B is SDNN actual measured value；When A is SDANN evaluation and test value, B is SDANN practical
Measured value；When A is RMSSD evaluation and test value, B is RMSSD actual measured value；When A is SDSD evaluation and test value, B is SDSD actual measured value；
When A is SDNN index evaluation and test value, B is SDNN index actual measured value；When A is SDANN evaluation and test value, B is SDANN actual measurement
Value；
PNN50 evaluation and test value is equal to PNN50 actual measured value；
S208, it is calculated according to the HRV frequency-domain index, obtains LF/HF evaluation and test value；
Specifically, each RR interphase change degree numerical value is subjected to Fast Fourier Transform (FFT) (FFT), treated power spectrum
It is arranged from low to high according to frequency, 0~0.15Hz is defined as low frequency LF, be high frequency by the power definition of > 0.15Hz, calculate
LF/HF ratio, the LF/HF ratio are LF/HF actual measured value, and LF/HF evaluation and test value is equal to LF/HF actual measured value.
S210, it is calculated according to the HRV triangle index index, obtains triangle index evaluation and test value；
Specifically, RR interphase is grouped from length is short to, spacing 7.8125ms, same group of the RR interphase of same widths are organized
Superposition, triangle index actual measured value=height/low width, triangle index evaluation and test value are equal to triangle index actual measured value.
S212, it is carried out according to the HRV time domain index evaluation and test value, the LF/HF evaluation and test value, the triangle index evaluation and test value
It calculates, obtains the HRV evaluation and test integral.
Specifically, being commented according to SDNN evaluation and test value, SDANN evaluation and test value, RMSSD evaluation and test value, SDSD evaluation and test value, SDNN index
The HRV evaluation and test integral is calculated in measured value, PNN50 evaluation and test value, the LF/HF evaluation and test value, the triangle index evaluation and test value.
As described in Figure 3, in one embodiment, described to be evaluated and tested according to the HRV time domain index evaluation and test value, the LF/HF
Value, the triangle index evaluation and test value are calculated, and are obtained the HRV evaluation and test integral, are specifically included:
S302, abnormal judgement is carried out according to the HRV time domain index evaluation and test value, obtains the anomaly classification of HRV time domain variable；
Specifically, anomaly classification is normal when SDNN evaluation and test value is greater than 100；SDNN evaluation and test value is more than or equal to 70 and small
Anomaly classification is suspicious when being equal to 100；Anomaly classification is abnormal when SDNN evaluation and test value is less than 70；
Anomaly classification is normal when SDANN evaluation and test value is greater than 100；SDANN evaluation and test value is more than or equal to 70 and is less than or equal to
Anomaly classification is suspicious when 100；Anomaly classification is abnormal when SDANN evaluation and test value is less than 70；
Anomaly classification is normal when RMSSD evaluation and test value is greater than 30；RMSSD evaluation and test value is more than or equal to 15 and is less than or equal to 30
When anomaly classification be it is suspicious；Anomaly classification is abnormal when RMSSD evaluation and test value is less than 15；
Anomaly classification is normal when SDSD evaluation and test value is greater than 30；When SDSD evaluation and test value is more than or equal to 15 and is less than or equal to 30
Anomaly classification is suspicious；Anomaly classification is abnormal when SDSD evaluation and test value is less than 15；
Anomaly classification is normal when SDNN index evaluation and test value is greater than 80；SDNN index evaluation and test value is more than or equal to 50 and is less than
Anomaly classification is suspicious when equal to 80；Anomaly classification is abnormal when SDNN index evaluation and test value is less than 50；
Anomaly classification is normal when PNN50 evaluation and test value is greater than 8%；PNN50 evaluation and test value is more than or equal to 2% and is less than or equal to
Anomaly classification is suspicious when 8%；Anomaly classification is abnormal when PNN50 evaluation and test value is less than 2%；
S304, abnormal judgement is carried out according to the LF/HF evaluation and test value, obtains the anomaly classification of LF/HF；
Specifically, anomaly classification is normal when LF/HF evaluation and test value is greater than 4；LF/HF evaluation and test value is more than or equal to 2 and small
Anomaly classification is suspicious when being equal to 4；Anomaly classification is abnormal when LF/HF evaluation and test value is less than 2；
S306, abnormal judgement is carried out according to the triangle index evaluation and test value, obtains the anomaly classification of HRV triangle index；
Specifically, anomaly classification is normal when triangle index evaluation and test value is greater than 30；Triangle index evaluation and test value is more than or equal to
15 and be less than or equal to 30 when anomaly classification be it is suspicious；Anomaly classification is abnormal when triangle index evaluation and test value is less than 15；
S308, referred to according to the anomaly classification of the HRV time domain variable, the anomaly classification of the LF/HF, the HRV triangle
Several anomaly classifications are calculated, and the HRV evaluation and test integral is obtained.
Specifically, the anomaly classification of the HRV time domain variable, the anomaly classification of the LF/HF, the HRV triangle
The anomaly classification of index carries out classification scoring, and all classification scoring is added to obtain HRV evaluation and test integral.Classification scoring
Include: anomaly classification be then be normally 0 point, it is 0.5 point that anomaly classification, which is suspicious, and it is then 1 point that anomaly classification, which is abnormal,.
In one embodiment, described to be calculated according to the clinical settings data, clinical settings integral is obtained, specifically
Include: to be calculated according to the disease condition in the clinical settings data, obtains the clinical settings integral.The clinical back
Diabetic history, chronic heart failure, ventricular hypertrophy (heart is super to confirm that anatomical structure is abnormal) in scape data, coronary heart disease myocardial infarction, the heart
Myopathy or other organic heart diseasies, citing is not especially limited herein.
As described in Figure 4, in one embodiment, the disease condition according in the clinical settings data is counted
It calculates, obtains the clinical settings integral, specifically include:
S402, the diabetic history according in the clinical settings data, chronic heart failure, ventricular hypertrophy, coronary heart disease cardiac muscle stalk
Extremely, cardiomyopathy or other organic heart diseasies carry out medical history and judge extremely, obtain the anomaly classification, described slow of the diabetic history
The property anomaly classification of heart failure, the anomaly classification of the ventricular hypertrophy, the coronary heart disease myocardial infarction anomaly classification, the cardiac muscle
The anomaly classification of sick or other organic heart diseasies；
Specifically, the anomaly classification of medical history includes: unknown with and without, background.
S404, according to the anomaly classification of the diabetic history, the anomaly classification of the chronic heart failure, the ventricular hypertrophy
Anomaly classification, the anomaly classification of the coronary heart disease myocardial infarction, the cardiomyopathy or other organic heart diseasies anomaly classification
It is calculated, obtains the clinical settings integral.
Specifically, according to the anomaly classification of the diabetic history, the anomaly classification of the chronic heart failure, ventricle fertilizer
The exception of the anomaly classification of big anomaly classification, the coronary heart disease myocardial infarction, the cardiomyopathy or other organic heart diseasies
Classification carries out medical history classification scoring, and all classification scoring is added to obtain clinical settings integral.Medical history classification scoring
Include: to have, for 1 point, without be then 0 point, background it is unknown, be 0.5 point.
In one embodiment, short distance ECG data refers to the selection preset time from II lead of 12 lead electrocardiogram
Data.In 12 lead electrocardiogram, II lead baseline is steady, and R/T ratio is big with respect to other leads, and II lead is selected to be conducive to
Improve the accuracy of this method.
It is in one embodiment, described that short distance ECG data is obtained according to ECG data selection analysis section,
It specifically includes: obtaining analysis duration parameters；Select baseline steady and R/ according to the analysis duration parameters, the ECG data
The big section of T ratio is used for the analysis of heart rate variance analyzing method as short distance ECG data, the short distance ECG data
Basis.
The analysis duration parameters are from the time span of electrocardiogram data intercept, and the analysis duration parameters are not less than 5
Minute and be not higher than 4 hours, for example, 5 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, illustrate do not make to have herein
Body limits.
The R/T ratio refers to that greatly the height of T wave versus baseline is less than the 30% of the height of R wave versus baseline.
The R wave refers to the waveform normally having on electrocardiogram.
The T wave is that a wave amplitude after QRS complex is lower and the longer electric wave of wave width, pole again after reflection ventricle is excited
Change process.The sequence of ventricular repolarization is with process of depolarization on the contrary, it goes to pole in outer layer slowly from outer layers towards inner layers progress
Change the positive potential when negative potential of part is restored to tranquillization first, outer layer made to be positive, internal layer is negative, therefore with vector when depolarising
Direction it is essentially identical.
As described in Figure 5, in one embodiment, described to be denoised according to the short distance ECG data, it obtains wait divide
The ECG data of analysis, specifically includes:
S502, data of being fought according to telepathy under short distance ECG data rejecting interference, artifact, non-sinus property ventricle, obtain
Short distance ECG data to be marked；
The interference and artifact are not the waves that electrocardio issues, and are that some noises of some other reason generate, for example, ring
The interference that improper noise generates in border.Artifact refers to electrocardiogram electrostatic interference caused by instrument reason itself.Interference and puppet
The recognition methods of difference can select from the prior art, and therefore not to repeat here.
It is common heart disease, such as systolia ectopica or the peculiar smell rhythm of the heart or ectopic beat that telepathy, which is fought, under non-sinus property ventricle, is known
Other method can select from the prior art, and therefore not to repeat here.
S504, identification mark is carried out according to the short distance ECG data to be marked, obtains short distance ECG data
Heartbeat attribute；
Specifically, sinus property heartbeat is labeled as N, supraventricular systolia ectopica is labeled as S, and property systolia ectopica in room is labeled as V, rises
Heartbeat of fighting is labeled as P, and auricular fibrillation heartbeat is labeled as Af, and auricular flutter heartbeat is labeled as AF.Heartbeat attribute labeling method can be with
It selects from the prior art, therefore not to repeat here.
S506, heartbeat interphase is obtained according to the heartbeat attribute of the short distance ECG data；
Specifically, two heartbeat attributes adjacent on electrocardiogram are formed a heartbeat interphase, for example, N-N interphase is (again
NN interphase can be), N-V interphase (can be NV interphase not only), V-V interval (can be VV interphase not only), N-S interphase (but also can name between NS
Phase).
S508, ECG data to be analyzed is obtained according to the heartbeat interphase.
Specifically, N-N interphase be effective heartbeat, analysis heart rate divided by the total heart rate of analystal section result be greater than etc.
In 0.7, then the data of N-N interphase are as ECG data to be analyzed；Heart rate is analyzed divided by the total heart rate of analystal section
As a result less than 0.7, then the analysis to the short distance ECG data is abandoned；Other heartbeat interval are invalid heartbeat, the part
ECG data does not participate in analysis, for example, N-V interphase, V-V interval, N-S interphase.It is to be analyzed to make to obtain after denoising
ECG data is effective heartbeat.
The analysis heart rate refers to the heartbeat sum of all N-N interphases in short distance ECG data.
The total heart rate of analystal section refers to the heartbeat sum of short distance ECG data.
In one embodiment, it is calculated, is obtained according to HRV evaluation and test integral, clinical settings integral described
To after HRV scoring, further includes: when clinical settings integral≤0.5, medical diagnosis on disease result is that HRV detects no abnormality seen；
When HRV evaluation and test integral≤0.5, medical diagnosis on disease result is that HRV detects no abnormality seen；When the clinical settings integrate >
0.5, when the HRV evaluation and test integral > 0.5, HRV scoring >=3, medical diagnosis on disease result is HRV abnormal；When the clinical back
When scape integrates HRV scoring < 3 described in > 0.5, the HRV evaluation and test integral > 0.5,1 <, medical diagnosis on disease result is that HRV is suspicious different
Often；When the clinical settings integrate > 0.5, the HRV evaluation and test integral > 0.5, HRV scoring≤1, medical diagnosis on disease result
It is without exception.
As described in Figure 6, in one embodiment, a kind of heart rate variability analysis device is provided, described device includes:
Electrocardiogram management module 601, for recording ECG data；
Selection analysis section module 603, for obtaining ECG data, according to ECG data selection analysis area
Between, obtain short distance ECG data；
It denoises module 604 and obtains electrocardiogram number to be analyzed for being denoised according to the short distance ECG data
According to；
Clinical settings obtain module 602, for obtaining the clinical settings data of user corresponding with the ECG data；
HRV grading module 605, for according to the ECG data to be analyzed be calculated HRV time domain index,
HRV frequency-domain index, HRV triangle index index refer to according to the HRV time domain index, the HRV frequency-domain index, the HRV triangle
Number index carries out that HRV evaluation and test integral is calculated, and is carried out that clinical settings integral, root is calculated according to the clinical settings data
According to HRV evaluation and test integral, clinical settings integral carry out that HRV scoring is calculated.
Described device obtains HRV scoring by the index combination clinical settings data of HRV, improves the accurate of HRV scoring
Property, to improve the accuracy of diagnosis；The method is commented by selection analysis section, denoising, calculating HRV index, calculating HRV
Point, it is easy to operate, it realizes and automatically analyzes.Therefore, operation of the present invention is easy, diagnostic accuracy is high, automatically analyzes short distance HRV.
Fig. 7 shows the internal structure chart of computer equipment in one embodiment.The computer equipment specifically can be clothes
Business device and terminal device, the server include but is not limited to high-performance computer and high-performance computer cluster；The terminal
Equipment includes but is not limited to mobile terminal device and terminal console equipment, the mobile terminal device include but is not limited to mobile phone,
Tablet computer, smartwatch and laptop, the terminal console equipment includes but is not limited to desktop computer and vehicle-mounted computer.
As shown in fig. 7, the computer equipment includes processor, memory and the network interface connected by system bus.Wherein, it stores
Device includes non-volatile memory medium and built-in storage.The non-volatile memory medium of the computer equipment is stored with operation system
System, can also be stored with computer program, when which is executed by processor, processor may make to realize that a kind of heart rate becomes
Anisotropic (HRV) analysis method.Computer program can also be stored in the built-in storage, which is executed by processor
When, it may make processor to execute a kind of heart rate variability (HRV) analysis method.It will be understood by those skilled in the art that showing in Fig. 7
Structure out, only the block diagram of part-structure relevant to application scheme, does not constitute and is applied to application scheme
The restriction of computer equipment thereon, specific computer equipment may include than more or fewer components as shown in the figure, or
Person combines certain components, or with different component layouts.
In one embodiment, a kind of heart rate variability (HRV) analysis method provided by the present application can be implemented as one kind
The form of computer program, computer program can be run in computer equipment as shown in Figure 7.The memory of computer equipment
In can store composition heart rate variability (HRV) analytical equipment each process template.For example, selection electrocardiogram management module 601,
Selection analysis section module 603, denoising module 604, clinical settings obtain module 602, HRV grading module 605.
In one embodiment, the present invention also provides a kind of storage mediums, are stored with computer program of instructions, the meter
When calculation machine instruction repertorie is executed by processor, so that the processor realizes following steps when executing:
Obtain ECG data；
According to ECG data selection analysis section, short distance ECG data is obtained；
It is denoised according to the short distance ECG data, obtains ECG data to be analyzed；
It is calculated according to the ECG data to be analyzed, obtains HRV time domain index, HRV frequency-domain index, HRV tri-
Angle index index；
Obtain the clinical settings data of user corresponding with the ECG data；
According to the HRV time domain index, the HRV frequency-domain index, the HRV triangle index index, the clinical settings
Data are calculated, and HRV scoring is obtained.
In one embodiment, the present invention also provides a kind of computer equipments, including at least one processor, at least one
A processor, the memory is stored with computer program of instructions, when the computer program of instructions is executed by the processor,
So that the processor realizes following steps when executing:
Obtain ECG data；
According to ECG data selection analysis section, short distance ECG data is obtained；
It is denoised according to the short distance ECG data, obtains ECG data to be analyzed；
It is calculated according to the ECG data to be analyzed, obtains HRV time domain index, HRV frequency-domain index, HRV tri-
Angle index index；
Obtain the clinical settings data of user corresponding with the ECG data；
According to the HRV time domain index, the HRV frequency-domain index, the HRV triangle index index, the clinical settings
Data are calculated, and HRV scoring is obtained.
It should be noted that above-mentioned heart rate variability (HRV) analysis method, heart rate variability (HRV) analytical equipment, calculating
Machine equipment and computer readable storage medium belong to a total inventive concept, and heart rate variability (HRV) analysis method, heart rate become
Content in anisotropic (HRV) analytical equipment, computer equipment and computer readable storage medium embodiment can be mutually applicable in.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a non-volatile computer and can be read
In storage medium, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, provided herein
Each embodiment used in any reference to memory, storage, database or other media, may each comprise non-volatile
And/or volatile memory.Nonvolatile memory may include that read-only memory (ROM), programming ROM (PROM), electricity can be compiled
Journey ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include random access memory
(RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms, such as static state RAM
(SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhanced SDRAM
(ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) directly RAM (RDRAM), straight
Connect memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
The limitation to the application the scope of the patents therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the concept of this application, various modifications and improvements can be made, these belong to the guarantor of the application
Protect range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. a kind of heart rate variability (HRV) analysis method, which comprises
Obtain ECG data；
According to ECG data selection analysis section, short distance ECG data is obtained；
It is denoised according to the short distance ECG data, obtains ECG data to be analyzed；
It is calculated according to the ECG data to be analyzed, obtains HRV time domain index, HRV frequency-domain index, HRV triangle and refer to
Number index；
Obtain the clinical settings data of user corresponding with the ECG data；
It is calculated according to the HRV time domain index, the HRV frequency-domain index, the HRV triangle index index, obtains HRV and comment
Survey integral；
It is calculated according to the clinical settings data, obtains clinical settings integral；
It is calculated according to HRV evaluation and test integral, clinical settings integral, obtains HRV scoring.
2. the method according to claim 1, wherein described according to the HRV time domain index, the HRV frequency domain
Index, the HRV triangle index index are calculated, and are obtained HRV evaluation and test integral, are specifically included:
Obtain the short distance ECG data corresponding actually detected total time；
It is calculated according to the short distance ECG data corresponding actually detected total time, obtains detection time coefficient；
It is calculated according to the HRV time domain index, the detection time coefficient, obtains HRV time domain index evaluation and test value；
It is calculated according to the HRV frequency-domain index, obtains LF/HF evaluation and test value；
It is calculated according to the HRV triangle index index, obtains triangle index evaluation and test value；
It is calculated, is obtained according to the HRV time domain index evaluation and test value, the LF/HF evaluation and test value, the triangle index evaluation and test value
The HRV evaluation and test integral.
3. according to the method described in claim 2, it is characterized in that, it is described according to the HRV time domain index evaluation and test value, it is described
LF/HF evaluation and test value, the triangle index evaluation and test value are calculated, and are obtained the HRV evaluation and test integral, are specifically included:
Abnormal judgement is carried out according to the HRV time domain index evaluation and test value, obtains the anomaly classification of HRV time domain variable；
Abnormal judgement is carried out according to the LF/HF evaluation and test value, obtains the anomaly classification of LF/HF；
Abnormal judgement is carried out according to the triangle index evaluation and test value, obtains the anomaly classification of HRV triangle index；
According to the exception of the anomaly classification of the HRV time domain variable, the anomaly classification of the LF/HF, the HRV triangle index point
Class is calculated, and the HRV evaluation and test integral is obtained.
4. being obtained the method according to claim 1, wherein described calculated according to the clinical settings data
It integrates, specifically includes to clinical settings:
According to the diabetic history in the clinical settings data, chronic heart failure, ventricular hypertrophy, coronary heart disease myocardial infarction, cardiomyopathy
Or other organic heart diseasies carry out medical histories and judge extremely, obtain the anomaly classification of the diabetic history, the chronic heart failure
Anomaly classification, the anomaly classification of the ventricular hypertrophy, the anomaly classification of the coronary heart disease myocardial infarction, the cardiomyopathy or other
The anomaly classification of organic heart disease；
According to the anomaly classification of the diabetic history, the anomaly classification of the chronic heart failure, the ventricular hypertrophy anomaly classification,
The anomaly classification of the anomaly classification of the coronary heart disease myocardial infarction, the cardiomyopathy or other organic heart diseasies is calculated,
Obtain the clinical settings integral.
5. method according to any one of claims 1 to 4, which is characterized in that described to be selected according to the ECG data
Analystal section obtains short distance ECG data, specifically includes:
Obtain analysis duration parameters；
It selects baseline steadily according to the analysis duration parameters, the ECG data and the big section of R/T ratio is as the short distance heart
Electromyographic data, the short distance ECG data are used for the analysis foundation of heart rate variance analyzing method.
6. method according to any one of claims 1 to 4, which is characterized in that described according to the short distance ECG data
It is denoised, obtains ECG data to be analyzed, specifically include:
It rejects that interference, artifact, telepathy is fought data under non-sinus property ventricle according to the short distance ECG data, obtains to be marked short
Journey ECG data；
Identification mark is carried out according to the short distance ECG data to be marked, obtains the heartbeat attribute of short distance ECG data；
Heartbeat interphase is obtained according to the heartbeat attribute of the short distance ECG data；
ECG data to be analyzed is obtained according to the heartbeat interphase.
7. method according to any one of claims 1 to 4, which is characterized in that it is described according to the HRV evaluation and test integral,
Clinical settings integral is calculated, after obtaining HRV scoring, further includes:
When clinical settings integral≤0.5, medical diagnosis on disease result is that HRV detects no abnormality seen；
When HRV evaluation and test integral≤0.5, medical diagnosis on disease result is that HRV detects no abnormality seen；
When the clinical settings integrate > 0.5, the HRV evaluation and test integral > 0.5, HRV scoring >=3, medical diagnosis on disease knot
Fruit is HRV abnormal；
When the clinical settings, which integrate > 0.5, the HRV evaluation and test, integrates the scoring < 3 of HRV described in > 0.5,1 <, medical diagnosis on disease
It as a result is the suspicious exception of HRV；
When the clinical settings integrate > 0.5, the HRV evaluation and test integral > 0.5, HRV scoring≤1, medical diagnosis on disease knot
Fruit is without exception.
8. a kind of heart rate variability analysis device, which is characterized in that described device includes:
Electrocardiogram management module, for recording ECG data；
Selection analysis section module, according to ECG data selection analysis section, obtains short for obtaining ECG data
Journey ECG data；
It denoises module and obtains ECG data to be analyzed for being denoised according to the short distance ECG data；
Clinical settings obtain module, for obtaining the clinical settings data of user corresponding with the ECG data；
HRV grading module obtains HRV time domain index, HRV frequency for being calculated according to the ECG data to be analyzed
Domain index, HRV triangle index index refer to according to the HRV time domain index, the HRV frequency-domain index, the HRV triangle index
Mark is calculated, and is obtained HRV evaluation and test integral, is calculated according to the clinical settings data, obtains clinical settings integral, according to
The HRV evaluation and test integral, clinical settings integral are calculated, and HRV scoring is obtained.
9. a kind of storage medium, is stored with computer program of instructions, which is characterized in that the computer program of instructions is by processor
When execution, so that the processor is executed such as the step of any one of claims 1 to 7 the method.
10. a kind of computer equipment, which is characterized in that including at least one processor, at least one processor, the memory
It is stored with computer program of instructions, when the computer program of instructions is executed by the processor, so that the processor executes
Such as the step of any one of claims 1 to 7 the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910648951.2A CN110448289B (en) | 2019-07-18 | 2019-07-18 | Heart Rate Variability (HRV) analysis method, device, storage medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910648951.2A CN110448289B (en) | 2019-07-18 | 2019-07-18 | Heart Rate Variability (HRV) analysis method, device, storage medium and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110448289A true CN110448289A (en) | 2019-11-15 |
| CN110448289B CN110448289B (en) | 2022-02-22 |
Family
ID=68482883
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910648951.2A Active CN110448289B (en) | 2019-07-18 | 2019-07-18 | Heart Rate Variability (HRV) analysis method, device, storage medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110448289B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113520406A (en) * | 2021-07-27 | 2021-10-22 | 香港心脑血管健康工程研究中心有限公司 | Frequency domain parameter analysis method and device for diagnosing ST-segment elevated myocardial infarction |
| CN113749665A (en) * | 2021-08-19 | 2021-12-07 | 深圳邦健生物医疗设备股份有限公司 | Method, device, equipment and medium for capturing abnormal indexes |
| 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 |
| CN114764581A (en) * | 2022-06-16 | 2022-07-19 | 合肥心之声健康科技有限公司 | Atrial fibrillation classification method, device and system based on RR interphase space characteristics |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040181159A1 (en) * | 2003-03-11 | 2004-09-16 | Kuo Terry B.J. | Method and apparatus for detecting yin-yang and asthenia-sthenia |
| CN104127194A (en) * | 2014-07-14 | 2014-11-05 | 华南理工大学 | Depression evaluating system and method based on heart rate variability analytical method |
| CN105228508A (en) * | 2013-03-08 | 2016-01-06 | 新加坡健康服务有限公司 | A kind of system and method measured for the risk score of classifying |
| CN108836313A (en) * | 2018-07-13 | 2018-11-20 | 希蓝科技（北京）有限公司 | A kind of intelligence chooses the ambulatory ECG analysis method of lead |
-
2019
- 2019-07-18 CN CN201910648951.2A patent/CN110448289B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040181159A1 (en) * | 2003-03-11 | 2004-09-16 | Kuo Terry B.J. | Method and apparatus for detecting yin-yang and asthenia-sthenia |
| CN105228508A (en) * | 2013-03-08 | 2016-01-06 | 新加坡健康服务有限公司 | A kind of system and method measured for the risk score of classifying |
| CN104127194A (en) * | 2014-07-14 | 2014-11-05 | 华南理工大学 | Depression evaluating system and method based on heart rate variability analytical method |
| CN108836313A (en) * | 2018-07-13 | 2018-11-20 | 希蓝科技（北京）有限公司 | A kind of intelligence chooses the ambulatory ECG analysis method of lead |
Non-Patent Citations (2)
| Title |
|---|
| 胡殿宇 等: "《临床医学概论》", 31 March 2016 * |
| 范利 等: "《心脏医疗保健学》", 30 September 2014 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113520406A (en) * | 2021-07-27 | 2021-10-22 | 香港心脑血管健康工程研究中心有限公司 | Frequency domain parameter analysis method and device for diagnosing ST-segment elevated myocardial infarction |
| CN113520406B (en) * | 2021-07-27 | 2024-05-17 | 香港心脑血管健康工程研究中心有限公司 | Frequency domain parameter analysis method and device for diagnosing ST elevation type myocardial infarction |
| CN113749665A (en) * | 2021-08-19 | 2021-12-07 | 深圳邦健生物医疗设备股份有限公司 | Method, device, equipment and medium for capturing abnormal indexes |
| CN113749665B (en) * | 2021-08-19 | 2024-02-02 | 深圳邦健生物医疗设备股份有限公司 | Method, device, equipment and medium for capturing abnormal index |
| 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 |
| CN114764581A (en) * | 2022-06-16 | 2022-07-19 | 合肥心之声健康科技有限公司 | Atrial fibrillation classification method, device and system based on RR interphase space characteristics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110448289B (en) | 2022-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110448289A (en) | A kind of heart rate variability (HRV) analysis method, device, storage medium and equipment | |
| Strauss et al. | ECG quantification of myocardial scar in cardiomyopathy patients with or without conduction defects: correlation with cardiac magnetic resonance and arrhythmogenesis | |
| US8929975B2 (en) | System for heart monitoring, characterization and abnormality detection | |
| US8755875B2 (en) | System for heart performance characterization and abnormality detection | |
| US8321005B2 (en) | System for continuous cardiac pathology detection and characterization | |
| CN113712569B (en) | High-frequency QRS wave group data analysis method and device | |
| US10085663B2 (en) | Automated ECG analysis and diagnosis system | |
| CN103961089B (en) | Based on the heart rate turbulence trend-monitoring method of sectional straight line fitting | |
| US20160135703A1 (en) | Patient Signal Analysis Based on Vector Analysis | |
| Alcaraz et al. | Electrocardiographic spectral features for long-term outcome prognosis of atrial fibrillation catheter ablation | |
| Zhou et al. | Localization of ventricular activation origin from the 12-lead ECG: a comparison of linear regression with non-linear methods of machine learning | |
| US11399727B2 (en) | Non-invasive analysis of sinoatrial node and autonomic nervous input to heart function | |
| Marques et al. | Characterization of atrial arrhythmias in body surface potential mapping: A computational study | |
| CN114732419A (en) | Exercise electrocardiogram data analysis method and device, computer equipment and storage medium | |
| Bonizzi et al. | Systematic comparison of non-invasive measures for the assessment of atrial fibrillation complexity: a step forward towards standardization of atrial fibrillation electrogram analysis | |
| Orozco-Duque et al. | Semi-supervised clustering of fractionated electrograms for electroanatomical atrial mapping | |
| KR102150057B1 (en) | Method and apparatus for realtime detecting type of arrhythmia | |
| Balasundaram et al. | Tracking Rotors With Minimal Electrodes: Modulation Index–Based Strategy | |
| US11596341B2 (en) | Systems and methods for identifying split activation histograms in cardiac mapping procedures | |
| CN106456033A (en) | Ecg evaluation | |
| Accardo et al. | Detection of subjects with ischemic heart disease by using machine learning technique based on heart rate total variability parameters | |
| Vila et al. | Atrial flutter mechanism detection using directed network mapping | |
| Li et al. | Classification of fibrillation organisation using electrocardiograms to guide mechanism-directed treatments | |
| US9538930B2 (en) | Linear multi-domain electrocardiogram | |
| Bojarnejad et al. | Non-invasive estimation of left atrial dominant frequency in atrial fibrillation from different electrode sites: insight from body surface potential mapping |
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 |