CN204246115U - A kind of physiology information detecting and blood processor - Google Patents

Abstract

This utility model provides physiology information detecting and blood processor, and wherein this device comprises: sensor device, for detecting the pressure measurement signal in patient sleeps under noncontact condition; Transmission line; Signal processing circuit is each independently physiological signal for the Signal separator that will measure; Main frame, for extracting useful information and being integrated into physiology case state change information and carrying out sleep index analysis and sleep level evaluation.This utility model detects automatically when patient falls asleep, from data automatic transmission during bed, lives noiseless to monitored people.The timely alarm of exception in sleep.Based on the physiologic information detected in real time in sleep, carry out science parsing, display analysis result.

Description

A kind of physiology information detecting and blood processor

Technical field

This utility model relates to data acquisition process field, particularly physiology information detecting and process in sleep.

Background technology

Research to disease is directly connected to the research of vital sign in sleep, therefore becomes in medical science the topic comparing care.Further investigation reveals that all standing of breathing and heart beating in sleeping has not been an example, it greatly threatens the healthy of people.Reasonably diagnosis and treatment as early as possible, the quality of life can improving patient prevents the generation of various complications, significantly improves the survival rate of patient.Therefore, be prevention and the first step of diagnose and treat diseases to the monitoring of sign in sleep procedure.

Traditional Sleep architecture measuring technique (Rechtschaffen & Kales slightly writes R & K) nineteen sixty-eight is proposed by California, USA university Brain Research Institute Integrated Summary each experience.The Data Source that it is analyzed is made up of 2 road brain electricity, 2 road eye galvanic electricity and 1 road mentalis electricity, analyzes, obtain the Sleep architecture of 6 phases that were divided into these data through comprehensive.From above summary, Sleep architecture is detected with R & K-method, at least to paste 10 pieces with top electrode at examinee's head, certain Physiological Psychology load can be brought to examinee, some is higher to sleep requirement for environmental conditions especially, and comparatively this load of sensitive subjects is sizable.Therefore R & K-method is only suitable for those especially easily sleep person'ss (as suffering from comparatively severe sleep apnea syndrome person).Certain R & K-method is more not suitable for the Sleep architecture monitoring needed for professional job safety.

In fact the many physiological signals on the person, as cardiac cycle, respiratory wave parameter, body move parameter, skin resistance, body temperature etc., all can present corresponding change with the cyclically-varying of sleep each phase.Therefore many scholars of countries in the world all research of coupled relation between the various physiological signal be devoted to beyond brain electricity and Sleep architecture change.But existing technical scheme all fails signal excavation and information fusion to engage, thus exactly at the useful information that sleep physiology signal extraction is potential, cannot can not provide various physiology case state change information in complete reliable sleep.

Therefore, for the problems referred to above existing in correlation technique, at present effective solution is not yet proposed.

Utility model content

For solving the problem existing for above-mentioned prior art, the utility model proposes a kind of physiology information detecting and blood processor, non-contacting approach is utilized to win the situation of change of signal, and under the contrast of R & K-method, sum up the characteristic sum rule that these physiological signals have in Sleep architecture cyclically-varying, obtain non-contacting Sleep architecture measuring technique, work out various physiological signal beyond brain electricity and coupled relation between Sleep architecture change.

This utility model adopts following technical scheme: a kind of physiology information detecting and blood processor, comprising:

Sensor device, for detecting the pressure measurement signal in patient sleeps under noncontact condition;

Transmission line, for transferring to signal processing circuit by physiological signal;

Signal processing circuit, exports for the vibration analyzed in detected physiological signal, and is each independently physiological signal by this Signal separator;

Main frame, for receiving the physiological signal obtained in sleep, and:

Extract useful information, and by these information, under predefine constraints, be integrated into physiology case state change information; And

Based on detected physiological signal, carry out sleep index analysis and sleep level evaluation according to predefine standard.

Preferably, described sensor device comprises air mattress and air pressure probe, the faint pressure change of this air pressure probe sensing mattress.

Preferably, described physiological signal comprises pulse wave signal, respiratory wave signal, and snoring signal, body moves signal.

Preferably, described sensor device also comprises comb filter, for utilizing bandpass filtering modules block to implement FFT process to the data that sensor exports, extracts each physiological signal component under sleep state.

Preferably, described main frame takes redundant computation and credibility to calculate, and to evaluate the reliability of different approaches institute extracting parameter, chooses the result that credibility is greater than predetermined threshold, for sleep architecture stage.

Preferably, the parameter that described sleep architecture stage utilizes comprises: cardiac cycle, breathing cycle, body move information and/from bed information;

Described main frame, based on mass data, finds, based on the Sleep architecture dependency rule obtaining parameter, to set up sleep stage knowledge base; Application uncertain reasoning is theoretical, merges multi-parameter information, carries out the analysis ratiocination of Sleep architecture.

Preferably, described sensor device utilizes Active signal to control, and eliminates the interference of the cycle of intrinsic vibration around to physiological signal in sleep.

Preferably, when occurring that heartbeat is abnormal in described systems axiol-ogy to sleep or respiratory arrest time, system automatic alarm.

Compared to prior art, having the following advantages of the technical solution of the utility model:

1) " passive type " detects, and automatically detects beginning, from data automatic transmission during bed during sleep.Without the need to collection and the transmission of Active participation daily life information, live noiseless to monitored people.

2) abnormality alarming, Sleeping Center is jumped, and breathes, timely alarm when entering abnormal from bed, and sends note.

3) long distance monitoring, jumping based on detecting in real time Sleeping Center, breathing, snoring, stand up and wait physiologic information, carry out science dissection process, show graphic analyses result.Continue detection, can provide, week/moon/year statement-of-health.By mobile phone, the real-time monitorings such as computer.

Accompanying drawing explanation

Fig. 1 is the module map of physiology information detecting according to this utility model embodiment and blood processor.

Fig. 2 is the bio-signal acquisition schematic flow sheet according to this utility model embodiment.

Fig. 3 passes schematic diagram according to the sleep level of this utility model embodiment.

Detailed description of the invention

Detailed description to one or more embodiment of this utility model is hereafter provided together with the accompanying drawing of diagram this utility model principle.Describe this utility model in conjunction with such embodiment, but this utility model is not limited to any embodiment.Scope of the present utility model is only defined by the claims, and this utility model contain many substitute, amendment and equivalent.Set forth many details in the following description to provide thorough understanding of the present utility model.These details are provided for exemplary purposes, and also can realize this utility model according to claims without some in these details or all details.

In sleep physio-pathological condition signal excavate and information fusion technology particular content be: at the useful information that sleep physiology signal extraction is potential, and by these from heterogeneity, dissimilar, the potential information of different approaches, under certain constraints, through mutually supplementing and checking mutually, integrate out than various physiology case state change information in original more complete more reliable sleep.After coupled relation beyond brain electricity between physiological signal and Sleep architecture change finds, with contactless, noiseless state, the technical method obtaining these physiological signals just becomes key of the present utility model.Such as pass through the ballistocardiogram obtained from air mattress, respiratory movement and body move the analysis of waveform, extract cardiac cycle variability, breathing cycle variability, the respiratory wave amplitude variation opposite sex, and body move information, and the information of mutual relation between them, judge Sleep architecture; Then by cardiac cycle during asphyxia, respiratory waveform, ballistocardiogram and body move the feature of change, pick out and whether there is asphyxia, and time-out is obstructive or central, and whether there is the information such as breathing pattern microarousal of exerting oneself.

Generally, we's surface technology scheme be with the physiologic information gathered from sensor special by analog filtering process, be separated into end pulse, breathing, body move, snoring signal, through high speed Fourier transform, comb shape Filtering Processing, and dormant judgement is carried out in calculation.One of key point is how from the signal mixed, correctly to extract physiological signal out.Utilize merely software (FFT calculation) system of filtering to be difficult to realize this function, and filter with suitable hardware simulation, improve S/N ratio.This utility model increases software on this basis and judges, improves precision further.The diagnostic method of one of them software application is, by the pulse that will walk abreast, breathe, the signal that body moves is separated, within the unit interval, number is occurred back for same scope amount and carry out arrangement graphing, solve its envelope, in certain probable range, extract data time-sequencing once again out, establish sleep state cyclically-varying.These numerical value process statistical calculation methods, physiological data Changing Pattern has to be applied extremely widely.

Fig. 1 is the structure chart of physiology information detecting according to this utility model embodiment and blood processor.As shown in Figure 1, the system that this utility model provides is made up of air mattress, air pressure probe, transmission line, signal processing circuit (5-10Hz wave trap, band filter etc.) and main frame.Main frame comprises the 32 bit processor MPU calculated for data, for storing the read only memory ROM of calculation procedure data, for the LCD display device to user's display process result, for reading and writing the internal memory SD card of the signal data before and after computing, for signal being sent to the wireless communication module of other device of remote port, COM1 etc.

Air mattress forms one to the sensor device of faint pressure sensitive together with air pressure probe, detects the measurement of various physiological signal in sleep under noncontact condition.When heartbeat, pulse beat and breathe cause health to have a small vibration time, the responsive mattress of fine motion has corresponding faint change output, is processed by signal processing circuit.

Utilize algorithm in this paper that measured signal segregation is become pulse wave signal, respiratory wave signal, snoring signal, body moves signal, Sleeping Center is dirty beated speed or irregular rhythm time or breathe automatic alarm when occurring stopping.Meanwhile, based on the pulse wave signal obtained, respiratory wave signal, body moves signal and carries out sleep index analysis, carries out the evaluation of sleep level according to R & K standard.

Fig. 3 passes curve chart according to the sleep level of this utility model embodiment.Be followed successively by from top to bottom: according to the sleep level of sleep signal; According to the sleep level of heart arteries and veins signal; The sleep level that the length of one's sleep, relation function was established; Consider that body moves the sleep level of impact establishment.

Such structural design has the feature of safety, easy care.Design mattress has the performance of multi output, and its advantage is: improve detection sensitivity; And implicit certain redundant signals, be beneficial to post processing level and carry out reliable parameter extraction and analysis.

This utility model excavates and information fusion the signal of physio-pathological condition in sleep further.Its particular content is: at the useful information that sleep physiology signal extraction is potential, and by these from heterogeneity, dissimilar, the potential information of different approaches, under certain constraints, through mutually supplementing and checking mutually, integrate out than various physiology case state change information in original more complete more reliable sleep.

1) extraction of life parameters

The initial data obtained from sensor device isolates heart shock wave, pulse wave and respiratory wave, and then extracts the life parameters such as cardiac cycle and breathing rate, and this is basis and the key of whole accurate natural sleep detection technique.Primary signal feature is large by the impact of individual morphology, sleeping position, has the advantages that pattern makes a variation, metamorphosis is large.Thus parameter information wherein has the features such as incomplete, fuzzy, in order to overcome above factor in post processing, take redundant computation, and design credibility calculating, in order to evaluate the reliability of different approaches institute extracting parameter, extract result and choose credibility the greater, and for next step sleep architecture stage.

2) sleep architecture stage

The parameter that sleep architecture stage utilizes comprises: cardiac cycle, breathing cycle, body move information and/from bed information.The problem solved is how by these gain of parameter Hypnograms.Thought and the theory of artificial intelligence have been used in the realization of this sleep stage method.First on the basis investigating mass data, find and summarize a large amount of based on Sleep architecture (expert analyzes gained with the R & K-method) dependency rule of parameter, establish sleep stage knowledge base; Uncertain reasoning in using artificial intelligence is theoretical, according to knowledge base, scientifically merges multi-parameter information, carries out the analysis ratiocination of Sleep architecture.

The utility model proposes cardiac cycle and sleep level relation mathematic model is as follows:

Middle cycle frequency range heart arteries and veins variation h _ut (), passes S with frequency range sleep level _mthe time difference equation of (t):

${\hat{S}}_m\left(t\right)+a_1{\hat{S}}_m(t-1)+...+a_n{\hat{S}}_m(t-n)=b_0{h}_u\left(t\right)+b_1{h}_u(t-1)+...+b_m{h}_u(t-m)---\left(1\right)$

A _n: the denominator coefficients (n=1-5) of difierence equation

B _m; The denominator coefficients (m=1-5) of difierence equation

The continuous sleep level S of middle cycle frequency range _m(t) and middle cycle frequency range heart arteries and veins variation h _u(t)

${\hat{S}}_m\left(t\right)=0.996{\hat{S}}_m(t-1)+0.255h_u\left(t\right)-0.259h_u(t-1)---\left(2\right)$

According to the function His length of one's sleep (k) of the continuous sleep level of heart arteries and veins variation presumption

$\mathrm{his}\left(k\right)=N\·\frac{\mathrm{his}\left(k\right)}{S_h}---\left(3\right)$

Wherein S _hfor amplitude of fluctuation of sleeping continuously

Ask the minima of regular distribution function

$\mathrm{Min}\left(\underset{k=0}{\overset{N}{\mathrm{\Σ}}}{\{\mathrm{his}\left(k\right)-\underset{i=1}{\overset{6}{\mathrm{\Σ}}}{w}_i\frac{1}{{\mathrm{\σ}}_i\sqrt{2\mathrm{\π}}}\exp (-\frac{{(k-m_i)}^2}{2{\mathrm{\σ}}_i^2})\}}^2\right)---\left(4\right)$

Night each sleep level coefficient w _i

w ₆＝0.102，w ₅＝0.161，w ₄＝0.132，

w ₃＝0.472，w ₂＝0.089，w ₁＝0.056， (5)

REM sleeps, and during awakening, heart arteries and veins amplitude of variation is very large, how to distinguish REM sleep very difficult with awakening, in order to accurate judgement, needs to judge that body moves information.Body moves signal intensity and is far better than heart arteries and veins signal intensity.According to heart arteries and veins oscillogram, determine heart arteries and veins change in 1 minute.If move signal in this 1 minute body, establish the ratio of this 1 minute body fatigue resistance.Dynamic with awakening relation at body, REM sleep (Rapid eye movement sleep) relation is as follows:

Sleep stages and body move relation and adopt mathematical model below.

$M^{\′}\left(t\right)=\frac{M\left(t\right)-M_{\max }}{M_{\max }-M_{\min }}$

T: time;

M (t): body moves size;

M ' (t): standardization body moves size

M _max: body moves in size, the meansigma methods of upper 5 numerical value;

M _min: body moves in size, the meansigma methods of the next 5 numerical value

Determine that REM sleeps and awakening judge index Iwr below according to M ' (t) and M (t):

Iwr (t)=M ' (t)/Mo, wherein Mo is the meansigma methods of a M (t) when tester enters bed and time clear-headed before getting up.

A large amount of clinical data proves:

Iwr (t) > 0.87 determines awakening

Iwr (t)≤0.87 determines that REM sleeps

Continuous sleep level is to standard sleep 6 grade transformation

$y_i=e{\left(\frac{{(k-m_i)}^2}{2{\mathrm{\σ}}_i^2}\right)}^2,i=\mathrm{1,2},...6$

M _i: the meansigma methods (i=1-6) of each sleep level

σ _i: the deviation (i=1-6) of each sleep level

K: sleep level continuously

Y _i, i=1,2 ... 6: the probit that each sleep level is corresponding, between 0-1.

A large amount of clinical data is determined

m ₆＝0.62±0.16，σ ₆＝0.15±0.08

m ₅＝0.65±0.10，σ ₅＝0.18±0.05

m ₄＝0.51±0.13,σ ₄＝0.13±0.10

m ₃＝0.44±0.08,σ ₃＝0.20±0.03

m ₂＝0.35±0.09,σ ₂＝0.17±0.06

m ₁＝0.33±0.14,σ ₁＝0.14±0.07

According to mathematical model above, determine sleep level result of determination.

Multi-parameter information merges the uncertain limitation of the insufficient conclusion of information that the application of thought overcomes single parameter source, solve the sleep analysis under sensor as aforementioned prerequisite well, the feasibility of a large amount of verification experimental verifications this method and reliability.

Fig. 2 is the bio-signal acquisition schematic flow sheet according to this utility model embodiment.Describe the decision method of system to each physiological status of this utility model embodiment in detail below in conjunction with Fig. 2, and system acquisition information stores and the detailed step of transmission data.

1 sleep judges

1. key element is extracted out: Pulse Rate, Respiration Rate, body dynamic (body moves the body of size exclusion pulse key element non-active ingredients+significantly and moves time of origin), snoring (snoring time of origin)

2. data sampling interval/calculation object data number: 39ms/sample, about 10 Miao Jian Even continued

3. pulse extracts mode out: extract pulse composition by simulation filtration system loop and implement microcomputer FFT calculation.

Among the Pulse Rate of 48 times ~ 600 times, cutting is carried out with the intervals such as 0 time ~ 59 times, 60 times ~ 119 times, 120 times ~ 179 times, 180 times ~ 239 times, 240 times ~ 299 times, 300 times ~ 359 times, 360 times ~ 419 times, 420 times ~ 479 times, 480 times ~ 539 times, 540 times ~ 599 times

Extract the maximum Pulse Rate among each Pulse Rate interval respectively out.Great majority become in the Pulse Rate composition of multiple proportion, get minimum Pulse Rate as pulse signal.The Pulse Rate that (utilize wave component to a high-profile, improve specific precision) extracted in units of one minute ', calculated by following algorithm:

Pulse Rate A=(Pulse Rate ' [1]+Pulse Rate ' [2]+Pulse Rate ' [3]) ÷ 3

Pulse Rate B=(Pulse Rate ' [4]+Pulse Rate ' [5]+Pulse Rate ' [6]) ÷ 3

Pulse change=(Pulse Rate A-Pulse Rate B)/2

Result of calculation is kept in file.

4. extraction mode is breathed: extract respiratory component by simulation filtration system loop and implement microcomputer FFT calculation.

Using the Pulse Rate among the Respiration Rate of 6 times ~ 90 times as judgement, from the maximum breathing number corresponding to the numerical value that it is medium and small as breathing key element.

Data are preserved, and take to be preserved according to being divided into 6 deciles to get its meansigma methods again with 10 number of seconds.

Directly resolve [alone algorithm] by waveform and carry out repeatedly breathing cycle parsing, return number with the breathing that this extracts a minute.

5. snoring extraction mode: utilize speech band domain (10Hz ~ 100Hz), judge as the breathing moment using certain volume, differentiate with or without snoring with this.

At 60 seconds for unit is used as minute, extract and the snoring time occurs.

6. human body with or without extraction mode: measure human body weight by pressure transducer, with this judge human body with or without.

7. body moves extraction mode:

It is dynamic that the signal of upper note 2. in item pulse each Pulse Rate interval of extracting out tries to achieve body as summation and the ratio of the summation of all values of judgment value.

(summation of all values-as the summation of value [comprising high harmonic] judging signal)=body moves signal

Implement waveform directly to resolve [alone algorithm], to extract the shedder dynamic time in the data sample of [having with 30 seconds for unit of account according to detecting required precision] between 10 seconds out.

8. sleep starts to judge: human body is in bed perception and significantly body occurs move probability perception and breathe fixing perception or press SLEEP ANALYZE SW button and start to judge that sleep starts.

9. to get up judgement: when continuing from bed more than 20 points perception or pulse/breathing can not be extracted for a long time out or press WAKEUP SW and press the button as judgement of getting up.

10. to sleep judgement: get up after judging, following formula carries out sleep judgement.

RSI (indicating the index of REM sleep state), SDI (indicating the index of Depth of sleep) is calculated by the data obtained.

RSI calculates method: to calculate pulse variable signal between first 10 points of object data [between 1 point pulse variable signal], between latter 10 points pulse variable signal as

Rolling average result

SDI calculates method: 0.5log2 × (body moves the summation of all values of signal ÷)

Sleep state judges: with following table as benchmark, compares according to each dormant average time that each age group obtains, and obtains following several state respectively

Awakening/REM sleep/NonREM sleep 1/NonREM sleep 2/NonREM sleep 3/NonREM sleep 4.

REM sleeps judgement

The RSI value calculated is arranged by descending, according to the age information logged in, is judged to be that REM sleeps with the sleep portion till moving close to the largest body in average REM individual's difference scope length of one's sleep [standard deviation].

Awakening/NonREM1 state judges

Except judging the data of REM sleep, SDI data are arranged with descending, according to the age information logged in, with close to average wakefulness

Largest body in time individual's difference scope [standard deviation] move till part be judged to be wakefulness.

Till dynamic close to the largest body in average N onREM1 individual's difference scope length of one's sleep [standard deviation], be partly judged to be that NonREM1 sleeps.

NonREM2/NonREM3/NonREM4 state judges

SDI is arranged by descending, is calculated with following method:

SDI descending [ADR1]-SDI descending [ADR2]=SSDI [ADR1],

SDI descending [ADR2]-SDI descending [ADR3]=SSDI [ADR2],

SDI descending [ADRn-1]-SDI descending [ADRn]=SSDI [ADRn-1].

To be judged to be that NonREM2 sleeps close to till the maximum SSDI area part in average NonREM2 individual's difference scope length of one's sleep [standard deviation].To be judged to be that NonREM3 sleeps close to till the maximum SSDI area part in average NonREM3 individual's difference scope length of one's sleep [standard deviation].And later area part of being slept by NonREM3 is judged to be that NonREM4 sleeps.

SSDI＝Slope of SDI

The data that each Sleep stages is split by sequence are on time preserved hereof with array form.

2 apneas judge

Directly resolve [alone algorithm] to judge according to waveform and breathe no more, continue the occasion of more than 10 seconds, be judged to be an apnea state.

If within discontented 10 seconds, breathe when stopping and do not cause harmful effect to health, this situation happens occasionally usually, is called non-diseases respiratory arrest.

This state exports as mechanical contacts.

3 environmentally, the automatic adjustment of user

When considering the feature of user body weight, bed, sensor setting position, initial setting, heart beating, breathes, stands up, the suitableeest set-up function of the signal extractions such as snoring.

4SD memory storage

Preserve data mode: with the csv file of comma form segmentation between data.

Filename:

Heartbeat signal, H0000000.csv filename starts, and every 2 points of intervals generate 1 file, during more than H0020160.csv, return H0000000.csv, rewritable paper.

Breath signal, B0000000.csv filename start, and every 2 points of intervals generate 1 file, during more than B0020160.csv, return B0000000.csv, rewritable paper.

Physiologic information state, S0000000.csv filename start, and every 2 points of intervals generate 1 file, during more than S0020160.csv, return S0000000.csv, rewritable paper.

Obtain above-mentioned three files in the same time, after letter, numeral is identical.

Sleep state, SLEEP000.csv filename start, and every 2 points of intervals generate 1 file, more than SLEEP999.csv, return SLEEP000.csv, rewritable paper.

Preserve data content (all data are with ASCII character form):

Heartbeat signal data format: pulse body moves 39ms interval and gathers initial data [60 seconds intervals]

Time series data 1 [3 byte 16 system]+comma [1 byte 0x2c]+data 2+ comma+data 3+ teases number 1,535+ comma+data 1,536+CR [1 byte 0x0d]

Breath signal form: 39ms interval gathers initial data [dividing between 60 seconds]

Time series data 1 [3 bytes 16 are entered]+comma [1 byte 0x2c]+data 2+ comma+data 3+ comma

Data 1,535+ comma+data 1,536+CR [1 byte 0x0d]

Status file form:

1. date-time information

2,0,1,3, (year [8 byte 10 system]) 0,5, (moon [4 byte 10 system]) 3,0, (day [4 byte 10 system]) 1,2, (time [4 byte 10 system]) 1,2, (point [4 byte 10 system])

2. Pulse Rate change information

0,0,5, (secondary [6 byte 10 system])

3. Pulse Rate A information

0,9,1, (secondary [6 byte 10 system])

4. Pulse Rate B information

1,0,2, (secondary [6 byte 10 system])

5. body moves size information [SDI value]

3,0,1, (rate [6 byte 10 system])

6. Respiration Rate information

0,1,8, (secondary [8 byte 10 system])

7. apnea temporal information

1,2, (second [4 byte 10 system])

8. to snore temporal information

2,5, (second [4 byte 10 system])

9. pressure sensor body moves temporal information

1,5, (second [4 byte 10 system])

10. human body with/without, sleep start/awakening, warning export with/without mark (FLG)

H, U, 0or1, S, L, 0or1, A, L, 0or1 (17 byte ASCII)

device state FLG

E, R, 0or1,0or1,0or1,0or1,0or1,0or1,0or1,0or1, (20 byte)

The order of abnormal FLG [extremely have=1, abnormal without=0] with as described below, e.g., E, R,, radio equipment has without exception, and maintain communications has without exception, pulse signal has without exception, breath signal has without exception, and snoring signal has without exception, and SD card has without exception, wave analysis module has without exception, and sleep sensor hardware has without exception.

Program version information

V, 1,0,0, (8 byte ASCII)

Each Sleep stages document format data:

Date-time information

2,0,1,3, (year [8 byte 10 system]) 0,5, (moon [4 byte 10 system]) 3,0, (day [4 byte 10 system]) 1,2,

(time [4 byte 10 system]) 1,2, (point [4 byte 10 system])

The identification FLG of each state is respectively, wakefulness: A

REM sleep state: B

NonREM1 sleep state: C

NonREM2 sleep state: D

NonREM3 sleep state: E

NonREM4 sleep state: F gets up the form of state: Z, with comma in time series

State+time is divided and makes a distinction.Each state change frequency is limited with maximum 200, and file size is fixed as 2K byte.

(example) Z, 0,2,5, A, 0,0,5, D, 0,1,0, E, 0,0,3, F, 0,3,0, E, 0,0,2, D, 0,0,5, B, 0,4,2,

5 signal communications

1. communication for conservation port/radio communication function [Zigbee]

During power supply On: communicated according to 5 communication scheme by ancillary equipment, can extract out various setting and preservation data.

In sleep analysis: [supposition 60 seconds is unit now] delivers letters from end end with the fixed form of csv file by described in 6-4 item termly.

In data collecting card, this utility model applies the rapid translating chip of 12, meanwhile, in order to adapt to the sampling of upper frequency real-time under Windows operating system, sampling card installs one 8253 intervalometers additional.Its parameter index:

Analog input channel; Single-ended 16 tunnels (this is for four berths, should use 64 tunnels for 16 berths)

A/D changes figure place: 12

Input voltage range :-5V ~+5V

Conversion time: 7 microseconds

Export code system: offset binary code

Systematic error: 0.1% (comprising: passage, sampling keep, A/D transformed error)

Computational methods: (nominal value-measured value)/ull-scale value * l00%

Input impedance: > 10Mohms

The setting of 8253 intervalometers:

Connect timing signal l.5MHz, the highest count frequency is 1.5MHz.

Under 6 kinds of working methods, counting terminates backward main frame and sends out interruption.(PC bus takies IRQ2, and AT-bus takies IRQ9).

Below running environment and the configuration of this utility model system.

Appearance design:

Waterproofing protection grade: nothing

This body profile cun method: 150mm (W) × 110mm (H) × 40mm (D)

Sensor part profile cun method: 500mm (W) × 250mm (H) × 20mm (D)

This body weight: about Kg

Sensor part: about Kg

Electrical design:

Power input voltage scope: (from the input of AC adapter) below DC5V ± 5%

Consumption electric current: below A/VDC (during usual action)/below A/VDC (during maximum actuation)

Inrush current: maximum below A/VDC (below ms)

Communication mode 1:RS232 [non-synchronous communication] (baud rate 9,600bps/ data bit

8bit/ parity is without/position of rest 1bit)

Communication that communication mode 2:Zigbee or Wi-fi is wireless [2.4GHz band domain]

Environment Design

1. serviceability temperature :+5 ~+35 DEG C

2. storage temperature: 0-+50 DEG C

3. use/preserve humidity: as long as 30% ~ 90% prevents condensation]

4. setting model

4.-1 noncorrosive gases occasion

4.-2 little dust, the occasion that aeration is good

4. the occasion that-3 vibrations are very little

4.-4 without the occasion of forceful electric power Jie Jiqiangci circle

Reliability design

In sum, the physiology information detecting that the utility model proposes and blood processor, have to issue special raw point compared to prior art: outer

1) without physiology information detecting in constraint, contactless sleep.On the bed of tested object, at least more than one nothing constraint, contactless sensor are set, detect that the described tested object of the above-mentioned tested object h.d. in bed body relevant with sleep quality moves, breathe, multiple physiology signals of heartbeat and so on, then comb filter is used to carry out date processing, extract and move signal along with dormant pulse signal and body, the pillar of bed is provided with and eliminates the structural system importing external signal into by bed and prevent external disturbance.The physiology signal that air type pressure transducer is measured, by bandpass filtering modules block, is separated into cardiac cycle part, breathes part, and snoring part, body moves part, waits physiological signal separating treatment.

2) comb filtering physiologic information is separated.The data that this utility model exports sensor, implement FFT process, according to the basic wave of pulse, harmonic component and other component, pulse component and body are moved component and is separated.But with standing up the low frequency component contained near heart rate number such as the body disorder of internal organs waited, therefore, the heart rate number sometimes asked for according to the peaks spectrum of FFT contains error more.In this utility model, propose there is a kind of method, it is in order to effectively utilize the harmonic component of pulse component, and separation pulse and body move accurately, employ comb filter.

3) utilize Active signal to control, eliminate cycle signal around superly, the intrinsic vibration cycles such as super expressway are jumped Sleeping Center, the interference of breath signal.

4) based on the heartbeat signal measured, body moves signal, each Sleep stages average originating rate and standard deviation calculation function.

5) based on the heartbeat signal measured, body moves signal, the algorithm of presumption Sleep stages.

Should be understood that, above-mentioned detailed description of the invention of the present utility model only for exemplary illustration or explain principle of the present utility model, and is not formed restriction of the present utility model.Therefore, any amendment made when not departing from spirit and scope of the present utility model, equivalent replacement, improvement etc., all should be included within protection domain of the present utility model.In addition, this utility model claims be intended to contain fall into claims scope and border or this scope and border equivalents in whole change and modification.

Claims (2)

1. physiology information detecting and a blood processor, is characterized in that, comprising:

Sensor device (1) for detecting the pressure measurement signal in patient sleeps under noncontact condition;

Transmission line (2) is for transferring to signal processing circuit by physiological signal;

Signal processing circuit (3) exports for the vibration analyzed in detected physiological signal, and is each independently physiological signal by this Signal separator;

Main frame (4) is for receiving the physiological signal obtained in sleep.

2. device according to claim 1, is characterized in that, described sensor device (1) comprises air mattress and air pressure probe, the faint pressure change of this air pressure probe sensing mattress.