CN104983411A - Real-time calculation method for measurement of complexity of dynamic pulse rate variant signal - Google Patents
Real-time calculation method for measurement of complexity of dynamic pulse rate variant signal Download PDFInfo
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Abstract
The invention discloses a real-time calculation method for measurement of the complexity of a dynamic pulse rate variant signal. The real-time calculation method aims to rapidly and accurately achieve measurement calculation of the complexity of the dynamic pulse rate variant signal and is used for online monitoring and early warning of cardiovascular diseases. The real-time calculation method comprises the steps that 1, a dynamic pulse signal is detected and processed; 2, the dynamic pulse rate variant signal is preprocessed, extracted and marked as PP(i); 3, a dynamic pulse rate variant signal sampling point PP(N) is updated in a window sliding way; 4, the time sequence where a PP(1) and the PP(N) are located is reconstructed; 5, an X(1) sequence and an X(N-m+1) sequence are symbolized; 6, the storage positions of a [S1(j)] code and a [SN-m+2(j)] code are generated; 7, an entropy value is iterated and updated.
Description
Technical field
The present invention relates to pulse signal and detect real-time Treatment Analysis, be specifically related to a kind of dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique based on sliding window iteration base-scale entropy.Can be used for dynamic pulse frequency extract real-time and real time complexity calculating.
Background technology
Cardiovascular system of human body is a kind of dynamic system of complexity, and under detecting its dynamic variation and distinguishing different time sections or different physiological status, the change of complexity, very important to the Diagnosis and Treat of cardiovascular disease.
Heart rate variability signals results from the change of heart beat cycles, contains physiology and the pathological information of abundant relevant cardiovascular system.But heart rate variability signals obtains from electrocardiosignal, numerous and diverse line and electrode make it apply in portable wearable Medical Instruments to be restricted.Pulse frequency Variability Signals, also results from the change of heart beat cycles, similar to heart rate variability signals, also contains a large amount of physiology about cardiovascular system and pathological information.Compare electrocardiosignal, pulse frequency Variability Signals obtains from pulse signal, and acquisition process is simple, can conveniently for portable wearable Medical Instruments.Dynamic pulse frequency Variability Signals extracts from dynamic pulse signal, compared to off-lined signal analysis, online real-time analysis to the monitoring of cardiovascular disease and timely early warning very important.
At present, to the analytical method of pulse frequency Variability Analysis Primary Reference heart rate variability, conventional is time domain, frequency domain, time-frequency domain and nonlinear analysis method.Pulse frequency Variability Signals is the time varying signal of non-stationary, and nonlinear method can extract the complexity change of signal more effectively.Some nonlinear characteristics, as the Analysis of Entropy methods such as Sample Entropy, approximate entropy, symbol sebolic addressing entropy, base-scale entropies, can be used to calculate heart rate variability signals complexity.Wherein, base-scale entropy can analyze heart rate variability signals effectively, for identifying the diseases such as coronary heart disease, achieves good effect.But the operand of base-scale entropy is exponentially level growth along with the increase of signal length, and computing intermediate variable takies a large amount of internal memory of system, brings extreme difficulties to the real-time calculating of dynamic pulse frequency Variability Signals Complexity Measurement.
Summary of the invention
The object of the invention is to calculate, for online monitoring and the early warning of cardiovascular disease for realizing pulse frequency Variability Signals Complexity Measurement rapidly and accurately.
The present invention is dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, the steps include:
(1) to dynamic pulse signal detection and treatment, dynamic pulse signal detection and treatment is completed by photoelectric sphyg sensor, pulse signal detection module, bluetooth module and smart mobile phone module;
(2) dynamic pulse signal pretreatment and dynamic pulse frequency Variability Signals extract, and are designated as
pP(
i), wherein
pP(
i) be
iindividual sampled value;
(3) the mode Regeneration dynamics pulse frequency Variability Signals sampled point of sliding window is adopted
pP(
n), wherein
pP(
n) be
nindividual sampled value;
(4) reconstruct
pP(1) and
pP(
n) time series at place, be designated as respectively
x(1) and
x(
n-
m+ 1), wherein
mfor length of time series;
(5) symbolization
x(1) and
x(
n-
m+ 1) sequence, be designated as respectively
s 1(
j) and
s n-
m+ 2
(
j), wherein
jfor after corresponding sequence symbolization
jpoint value;
(6) produce
s 1(
j) and
s n-
m+ 2
(
j) memory location of encoding;
(7) iteration upgrades entropy.
Usefulness of the present invention is: adopt sliding window iteration base-scale entropy analytic process to refer to that the base-scale entropy adopting sliding window iteration thought to realize calculates, compared to former base-scale entropy analytic process, can realize single sampled point analysis.Under the prerequisite not affecting computational accuracy, the algorithm speed of service can be improved, save Installed System Memory.Sliding window iteration also can be used for other improvement as comentropies such as symbol sebolic addressing entropys to improving one's methods of base-scale entropy.
Heart rate variability signals can reflect that human body autonomic nervous system is active, also can be used for assess sympathetic and vagal balance simultaneously.By the dynamic pulse frequency Variability Signals of sliding window iteration base-scale entropy analytic process real-time analysis, obtain the base-scale entropy that can reflect that heartbeat changes.Thus understand the state of the system of autonomic nerve, realize the monitoring index system of disease.
Accompanying drawing explanation
Fig. 1 is pulse signal detection and treatment system block diagram of the present invention, and Fig. 2 is the algorithm principle figure that sliding window iteration base-scale entropy of the present invention is analyzed.
Detailed description of the invention
As shown in Figure 1 and Figure 2, the present invention is dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, the steps include:
(1) to dynamic pulse signal detection and treatment, dynamic pulse signal detection and treatment is completed by photoelectric sphyg sensor, pulse signal detection module, bluetooth module and smart mobile phone module;
(2) dynamic pulse signal pretreatment and dynamic pulse frequency Variability Signals extract, and are designated as
pP(
i), wherein
pP(
i) be
iindividual sampled value;
(3) the mode Regeneration dynamics pulse frequency Variability Signals sampled point of sliding window is adopted
pP(
n), wherein
pP(
n) be
nindividual sampled value;
(4) reconstruct
pP(1) and
pP(
n) time series at place, be designated as respectively
x(1) and
x(
n-
m+ 1), wherein
mfor length of time series;
(5) symbolization
x(1) and
x(
n-
m+ 1) sequence, be designated as respectively
s 1(
j) and
s n-
m+ 2
(
j), wherein
jfor after corresponding sequence symbolization
jpoint value;
(6) produce
s 1(
j) and
s n-
m+ 2
(
j) memory location of encoding;
(7) iteration upgrades entropy.
According to above-described dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, the detection and treatment stating the dynamic pulse signal described in step (1) is completed by photoelectric sphyg sensor, pulse signal detection module, bluetooth module and smart mobile phone module.Figure 1 shows that the detection and treatment system block diagram of dynamic pulse signal.Realize the detection and treatment of dynamic pulse signal, signal sampling frequency is 250Hz.
According to above-described dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, above-mentioned steps (2) is described carries out pretreatment and extraction to collection dynamic pulse signal, carries out as follows:
(1) the real-time filtering myoelectricity interference of the integral coefficient LP filter by by frequency being 62.5Hz and random noise;
(2) 50Hz and integer harmonics wave trap thereof remove baseline drift and Hz noise;
(3) dynamic difference threshold method is adopted to extract dynamic pulse frequency Variability Signals to filtered signal.Pulse frequency Variability Signals is herein the main ripple interval of pulse signal, is designated as
pP(
i), wherein
pP(
i) be
iindividual sampled value, unit is ms.
According to above-described dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, the described mode Regeneration dynamics pulse frequency Variability Signals sampled point adopting sliding window of above-mentioned steps (3)
pP(
n), wherein
pP(
n) be N number of sampled value, setting data relief area, the dynamic pulse frequency Variability Signals that buffer memory extracts, note data buffer length is
nindividual sampled point.Take data buffer zone as window, realized the renewal of dynamic pulse frequency Variability Signals sampled point by the mode of sliding window, while renewal sampled point, calculate base-scale entropy by the mode of iteration.Carry out as follows:
(1) first at internal memory opening space, the sampled point that buffer memory is new, is designated as
pP(
n+ 1);
(2) sampled point of buffer memory is the earliest rejected
pP(1), high-order sampled point moves to low level,
pP(
i)=
pP(
i+ 1);
(3) again by buffer memory in advance
pP(
n+ 1) highest order of buffer area is placed on, namely
pP(
n)=
pP(
n+ 1).
According to above-described dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, above-mentioned steps (4) described reconstruct
pP(1) and
pP(
n) time series at place, wherein
mfor length of time series, according to the principle of base-scale entropy, need by
nthe sampled point reconstruct of individual buffer memory produces
n-
m+ 1 length is
mtime series, each time series represents a kind of heartbeat pattern.In order to reduce restructuring procedure amount of calculation and memory space, the present invention adopts iterative manner to realize the calculating of entropy.Carry out as follows:
(1) will
pP(1) time series at place is reconstructed into:
(1)
(2) will
pP(
n) time series at place is reconstructed into:
(2)。
According to above-described dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, above-mentioned steps (5) described symbolization
x(1) and
x(
n-
m+ 1) sequence, wherein
jfor after corresponding sequence symbolization
jpoint value, carries out as follows:
(1) symbolization
x(1) sequence.Wherein, following formula pair is adopted
pP(1) symbolization:
(3)
In formula,
μ 1for time series
x(1) average,
αfor constant, be used for regulating symbolization border.
bS 1for cardinal scales, be used for determining symbolization border.
bS 1for:
(4)
(2) symbolization
x(
n-
m+ 1) sequence.Process, with (1), is designated as { S
n-
m+ 2
(
j),
j=1 ...,
m.
According to above-described dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, above-mentioned steps (6) described generation
s 1(
j) and
s n-
m+ 2
(
j) memory location of encoding.Each different time series represents a kind of different heartbeat pattern, therefore comprises 4 kinds of symbols and length is
mtime series can represent 4
m plant heartbeat pattern.Adding up each different mode accounts for whole
n-
mthe probability of+1 pattern, is used for calculating whole seasonal effect in time series entropy, so, need upgrade
s 1(
j) and
s n-
m+ 2
(
j) appearance and number.Wherein
s 1code storage position is:
(5)。
According to above-described dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, the described iteration of above-mentioned steps (7) upgrades entropy.
The mode of iteration is adopted to realize the renewal of entropy, therefore need deduct from last result of calculation
s 1(
j) entropy, add
s n-
m+ 2
(
j) entropy, the calculating of all sampled point entropy can be realized.Each time after Data Update,
s 1(
j) representated by heartbeat number of modes subtract 1, {
s n-
m+ 2
(
j) representated by number of modes add 1.Number is used respectively
n(
h) and
n(
k) represent, as Fig. 2 shows.
Upgrade
s 1(
j) and
s n-
m+ 2
(
j) number after, just can iteration upgrade entropy.Before the sliding window of note, entropy is
bS '(
m),
s 1(
j) number be
n' (
h), occurrence probability is
p' (
h),
s n-
m+ 2
(
j) number be
n' (
k), occurrence probability is
p' (
k); After sliding window, entropy is
bS(
m),
s 1(
j) number be
n(
h), occurrence probability is
p(
h),
s n-
m+ 2
(
j) number be
n(
k), occurrence probability is
p(
k).Initially
bS'=0, in iterative process
n(
h)=
n' (
h)-1,
n(
k)=
n' (
k)+1.According to the Changing Pattern of two kinds of number of modes, and in entropy computational process, the antilog of logarithm must be greater than 0;
Carry out as follows:
(1) before the sliding window of note, entropy is
bS '(
m),
s 1(
j) number be
n' (
h), calculate probability of occurrence, be designated as
p' (
h),
s n-
m+ 2
(
j) number be
n' (
k), calculate probability of occurrence, be designated as
p' (
k);
(2) after sliding window, entropy is
bS(
m),
s 1(
j) number be
n(
h), calculate probability of occurrence, be designated as
p(
h),
s n-
m+ 2
(
j) number be
n(
k), calculate probability of occurrence, be designated as
p(
k);
(3) initial
bS'=0, in iterative process
n(
h)=
n' (
h)-1,
n(
k)=
n' (
k)+1;
(4) iteration upgrades.According to the Changing Pattern of two kinds of number of modes, and in entropy computational process, the antilog of logarithm must be greater than the restriction of 0, has following four kinds of iteration update modes:
(a)
n(
h) >0,
n(
k) >1, represent
s 1(
j) and
s n-
m+ 2
(
j) representated by pattern all exist in the front and back of sliding window.So, in the process calculating entropy, do not need to consider that logarithm antilog is the situation of 0.Due to:
(6)
(7)
In formula,
m=4
m .Before and after sliding window, only have
s 1(
j) and
s n-
m+ 2
(
j) number of representative pattern there occurs change, other pattern is constant, then-
p(1) log
2 p(1)=-
p '(1) log
2 p '(1) ... ,-
p(
m) log
2 p(
m)=-
p '(
m) log
2 p '(
m).So formula (6) subtracts formula (7) and can obtain:
(8)
Further to formula (8) abbreviation:
(9)
Special, before and after sliding window
s 1(
j) and
s n-
m+ 2
(
j) when representing same mode.First,
s 1(
j) number of representative pattern subtracts 1,
n(
h)=
n' (
h)-1; Then,
s n-
m+ 2
(
j) representative number of modes adds 1,
n(
k)=
n' (
k)+1=
n(
h)+1=
n' (
h).Then formula (8) is:
(10)
That is, slide window before and after entropy do not change.Because
s 1(
j) and
s n-
m+ 2
(
j) when representing same mode, before and after sliding window, the number of each pattern does not change.
Or (b)
n(
h)=0,
n(
k) >1, represent
s 1(
j) representated by pattern disappear after sliding window.Then
n' (
h)=1,
p(
h)=0,
p' (
h)=1/ (
n-
m+ 1).Then obtained by formula (8):
(11)
Or (c)
n(
h) >0,
n(
k)=1, represent
s n-
m+ 2
(
j) representated by pattern disappear after sliding window.Then
n' (
h)=
n(
h)+1,
n(
k)=1,
n' (
k)=0,
p' (
k)=0,
p(
k)=1/ (
n-
m+ 1).Then can be obtained by formula (8):
(12)
Or (d)
n(
h)=0,
n(
k)=1, represent
s 1(
j) representated by pattern disappear, {
s n-
m+ 2
(
j) representated by pattern first time occur, therefore the number of assemble mode is constant.Then
n' (
h)=1,
n' (
k)=0,
p' (
k)=
p(
h)=0,
p' (
h)=
p(
k)=1/ (
n-
m+ 1).Can be obtained by formula (8):
(13)
Through type (9)-Shi (13) can obtain the cardinal scales entropy of dynamic pulse frequency Variability Signals, by the change of entropy, realizes the calculating of dynamic pulse frequency Variability Signals Complexity Measurement in real time.
Claims (7)
1. dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique, is characterized in that, the steps include:
(1) to dynamic pulse signal detection and treatment, dynamic pulse signal detection and treatment is completed by photoelectric sphyg sensor, pulse signal detection module, bluetooth module and smart mobile phone module;
(2) dynamic pulse signal pretreatment and dynamic pulse frequency Variability Signals extract, and are designated as
pP(
i), wherein
pP(
i) be
iindividual sampled value;
(3) the mode Regeneration dynamics pulse frequency Variability Signals sampled point of sliding window is adopted
pP(
n), wherein
pP(
n) be
nindividual sampled value;
(4) reconstruct
pP(1) and
pP(
n) time series at place, be designated as respectively
x(1) and
x(
n-
m+ 1), wherein
mfor length of time series;
(5) symbolization
x(1) and
x(
n-
m+ 1) sequence, be designated as respectively
s 1(
j) and
s n-
m+ 2
(
j), wherein
jfor after corresponding sequence symbolization
jpoint value;
(6) produce
s 1(
j) and
s n-
m+ 2
(
j) memory location of encoding;
(7) iteration upgrades entropy.
2. dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique according to claim 1, is characterized in that step (2) is described and carries out pretreatment and extraction to collection dynamic pulse signal, carry out as follows:
(1) the real-time filtering myoelectricity interference of the integral coefficient LP filter by by frequency being 62.5Hz and random noise;
(2) 50Hz and integer harmonics wave trap thereof remove baseline drift and Hz noise;
(3) dynamic difference threshold method is adopted to extract dynamic pulse frequency Variability Signals to filtered signal;
Pulse frequency Variability Signals is herein the main ripple interval of pulse signal, is designated as
pP(
i), wherein
pP(
i) be
iindividual sampled value.
3. dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique according to claim 1, is characterized in that the described mode Regeneration dynamics pulse frequency Variability Signals sampled point adopting sliding window of step (3)
pP(
n), wherein
pP(
n) be
nindividual sampled value, carry out as follows:
(1) first at internal memory opening space, the sampled point that buffer memory is new, is designated as
pP(
n+ 1);
(2) sampled point of buffer memory is the earliest rejected
pP(1), high-order sampled point moves to low level,
pP(
i)=
pP(
i+ 1);
(3) again by buffer memory in advance
pP(
n+ 1) highest order of buffer area is placed on, namely
pP(
n)=
pP(
n+ 1).
4. dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique according to claim 1, is characterized in that step (4) described reconstruct
pP(1) and
pP(
n) time series at place, be designated as respectively
x(1) and
x(
n-
m+ 1), wherein
mfor length of time series, carry out as follows:
(1) will
pP(1) time series at place is reconstructed into:
(1)
(2) will
pP(
n) time series at place is reconstructed into:
(2)。
5. dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique according to claim 1, is characterized in that step (5) described symbolization
x(1) and
x(
n-
m+ 1) sequence, be designated as respectively
s 1(
j) and
s n-
m+ 2
(
j), wherein
jfor after corresponding sequence symbolization
jpoint value, carries out as follows:
(1) symbolization
x(1) sequence:
Wherein, following formula pair is adopted
pP(1) symbolization:
(3)
In formula,
μ 1for time series
x(1) average,
αfor constant, be used for regulating symbolization border;
bS 1for cardinal scales, be used for determining symbolization border;
bS 1for:
(4)
(2) symbolization
x(
n-
m+ 1) sequence:
Process, with (1), is designated as { S
n-
m+ 2
(
j),
j=1 ...,
m.
6. dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique according to claim 1, it is characterized in that step (6) described generation
s 1(
j) and
s n-
m+ 2
(
j) memory location of encoding, wherein
s 1code storage position is:
(5)。
7. dynamic pulse frequency Variability Signals Complexity Measurement real-time computing technique according to claim 1, is characterized in that the described iteration of step (7) upgrades entropy, carries out as follows:
(1) before the sliding window of note, entropy is
bS '(
m),
s 1(
j) number be
n' (
h), calculate probability of occurrence, be designated as
p' (
h),
s n-
m+ 2
(
j) number be
n' (
k), calculate probability of occurrence, be designated as
p' (
k);
(2) after sliding window, entropy is
bS(
m),
s 1(
j) number be
n(
h), calculate probability of occurrence, be designated as
p(
h),
s n-
m+ 2
(
j) number be
n(
k), calculate probability of occurrence, be designated as
p(
k);
(3) initial
bS'=0, in iterative process
n(
h)=
n' (
h)-1,
n(
k)=
n' (
k)+1;
(4) iteration upgrades: according to the Changing Pattern of two kinds of number of modes, and in entropy computational process, the antilog of logarithm must be greater than the restriction of 0, has following four kinds of iteration update modes:
(a)
n(
h) >0,
n(
k) >1, represent
s 1(
j) and
s n-
m+ 2
(
j) represented by pattern all exist in the front and back of sliding window;
Entropy iterative formula is:
(6)
Special, before and after sliding window,
s 1(
j) and
s n-
m+ 2
(
j) when representing same mode; First,
s 1(
j) number of representative pattern subtracts 1,
n(
h)=
n' (
h)-1; Then,
s n-
m+ 2
(
j) representative number of modes adds 1,
n(
k)=
n' (
k)+1=
n(
h)+1=
n' (
h); Entropy iterative formula is:
(7)
Or (b)
n(
h)=0,
n(
k) >1, represent
s 1(
j) representated by pattern disappear after sliding window; Then
n' (
h)=1,
p(
h)=0,
p' (
h)=1/ (
n-
m+ 1); Entropy iterative formula is:
(8)
Or (c)
n (h)>0,
n (k)=1, represent
s n-
m+ 2
(
j) representated by pattern disappear after sliding window; Then
n' (
h)=
n(
h)+1,
n(
k)=1,
n' (
k)=0,
p' (
k)=0,
p(
k)=1/ (
n-
m+ 1); Entropy iterative formula is:
(9)
Or (d)
n(
h)=0,
n(
k)=1, represent
s 1(
j) representated by pattern disappear, {
s n-
m+ 2
(
j) representated by pattern first time occur, therefore the number of assemble mode is constant; Then
n' (
h)=1,
n' (
k)=0,
p' (
k)=
p(
h)=0,
p' (
h)=
p(
k)=1/ (
n-
m+ 1); Entropy iterative formula is:
(10)
Through type (6)-Shi (10) can obtain the cardinal scales entropy of dynamic pulse frequency Variability Signals, by the change of entropy, realizes the calculating of dynamic pulse frequency Variability Signals Complexity Measurement in real time.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109497973A (en) * | 2018-12-24 | 2019-03-22 | 兰州理工大学 | The detection system and detection method of pulse and blood oxygenation information under daily unsupervised state |
| CN110430805A (en) * | 2016-11-30 | 2019-11-08 | 利得高集团有限公司 | Improve the hemodynamic monitors of filtering function |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103690152A (en) * | 2014-01-06 | 2014-04-02 | 山东大学 | Arterial elasticity evaluating device based on pulse analysis |
| CN104573458A (en) * | 2014-12-30 | 2015-04-29 | 深圳先进技术研究院 | Identity recognition method, device and system based on electrocardiogram signals |
-
2015
- 2015-07-16 CN CN201510416062.5A patent/CN104983411A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103690152A (en) * | 2014-01-06 | 2014-04-02 | 山东大学 | Arterial elasticity evaluating device based on pulse analysis |
| CN104573458A (en) * | 2014-12-30 | 2015-04-29 | 深圳先进技术研究院 | Identity recognition method, device and system based on electrocardiogram signals |
Non-Patent Citations (3)
| Title |
|---|
| 丑永新等: "基于手机的动态脉率变异性信号提取与分析", 《中国医疗器械杂志》 * |
| 丑永新等: "基于改进滑窗迭代DFT的动态脉率变异性提取", 《仪器仪表学报》 * |
| 严碧歌等: "应用多尺度化的基本尺度熵分析心率变异性", 《物理学报》 * |
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| CN110430805A (en) * | 2016-11-30 | 2019-11-08 | 利得高集团有限公司 | Improve the hemodynamic monitors of filtering function |
| CN110430805B (en) * | 2016-11-30 | 2022-07-05 | 利得高集团有限公司 | Hemodynamics monitor with improved filtering function |
| US11382567B2 (en) | 2016-11-30 | 2022-07-12 | Lidco Group Plc | Haemodynamic monitor with improved filtering |
| CN109497973A (en) * | 2018-12-24 | 2019-03-22 | 兰州理工大学 | The detection system and detection method of pulse and blood oxygenation information under daily unsupervised state |
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