CN109091141A - A kind of sleep quality monitor and its monitoring method based on brain electricity and eye electricity - Google Patents
A kind of sleep quality monitor and its monitoring method based on brain electricity and eye electricity Download PDFInfo
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- 230000005611 electricity Effects 0.000 title claims abstract description 108
- 210000004556 brain Anatomy 0.000 title claims abstract description 100
- 230000003860 sleep quality Effects 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000012544 monitoring process Methods 0.000 title claims abstract description 14
- 230000008667 sleep stage Effects 0.000 claims abstract description 25
- 230000003321 amplification Effects 0.000 claims abstract description 19
- 238000003199 nucleic acid amplification method Methods 0.000 claims abstract description 19
- 230000007958 sleep Effects 0.000 claims abstract description 18
- 238000010801 machine learning Methods 0.000 claims abstract description 13
- 238000004891 communication Methods 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 4
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- 230000004044 response Effects 0.000 claims description 4
- 230000005059 dormancy Effects 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 239000004615 ingredient Substances 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 2
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- 230000008452 non REM sleep Effects 0.000 description 3
- 230000036385 rapid eye movement (rem) sleep Effects 0.000 description 3
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- 210000003128 head Anatomy 0.000 description 2
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- 230000000366 juvenile effect Effects 0.000 description 1
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- 210000001259 mesencephalon Anatomy 0.000 description 1
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- 210000003478 temporal lobe Anatomy 0.000 description 1
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- A—HUMAN NECESSITIES
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- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4815—Sleep quality
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/296—Bioelectric electrodes therefor specially adapted for particular uses for electromyography [EMG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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Abstract
The invention discloses it is a kind of based on brain electricity and eye electricity sleep quality monitor, comprising: impedance matching module, front end amplification module, filter module, D/A converter module, processor, module, communication module, power module and client is locally stored in signal acquisition module.The invention also discloses a kind of monitoring methods, comprising: with sample electrodes acquisition brain electricity and electro-ocular signal, eliminates interference noise with driven-right-leg circuit；Brain electricity and electro-ocular signal pass sequentially through impedance matching module, front end amplification module, filter module and D/A converter module；Processor handles brain electricity and electro-ocular signal with Time-Frequency Analysis Method to extract characteristic information；The classifier of client machine learning carries out sleep stage, judges sleep stage and sleep quality.Sleep quality monitor provided by the invention and monitoring method can judge the sleep phases stage online, and sleep quality monitor is smaller than existing sleep monitor class small product size, uses the advantages that more convenient, wearing is more comfortable.
Description
Technical field
The present invention relates to wearable health equipment field, in particular to a kind of sleep quality monitoring based on brain electricity and eye electricity
Instrument and its monitoring method.
Background technique
With the acceleration of people's life rhythm with the increasing of operating pressure, sleep quality is poor to become commonplace ask
Topic.And brain electricity and electro-ocular signal can be used to the sleep quality that disconnected subject is judged in objective, quantization, in conjunction with psychology and pharmaceutical means
The sleep quality for improving people can be integrated.
Brain wave is some spontaneous rhythmic neural electrical activities, and frequency variation range, and can mainly in 1~30Hz
It is further divided into four wave bands, i.e. δ wave band (1-3Hz), θ wave band (4-7Hz), α wave band (8-13Hz), beta band (14-
30Hz).In addition to this, when awakening and being absorbed in a certain thing, a kind of frequency of Chang Kejian γ wave higher compared with β wave, frequency is
30~80Hz, wave amplitude range are indefinite；And in sleep it may also occur that the more special normal brain wave of other waveforms, such as hump
Wave, σ wave, λ wave, κ-complex wave, μ wave etc..
δ wave: frequency is 1~3Hz, and amplitude is 20~200 μ V.When people is infancy or intellectual development be immature, adult
Under extremely tired and lethargic sleep or narcosis, this wave band can be recorded in temporal lobe and top.
θ wave: frequency be 4~7Hz, amplitude be 5~20 μ V, adult's wish baffle or depression and mental patient
In this wave it is extremely significant, but this wave is the main component in the electroencephalogram of juvenile (10-17 years old).
α wave: frequency is 8~13Hz (average 10Hz), and amplitude is 20~100 μ V, it is the base of normal brain electric wave
This rhythm and pace of moving things, if not additional stimulation, frequency be it is fairly constant, people is awake, quiet and the rhythm and pace of moving things is the most when closing one's eyes
Obviously, it opens eyes (by light stimulus) or when receiving other stimulations, α wave disappears at once.
β wave: frequency is 14~30Hz, and amplitude is 100~150 μ V.Occur when nervous and excited or excited
This wave, when people wakes from a nightmare with a start, the slow wave rhythm and pace of moving things originally can be substituted by the rhythm and pace of moving things immediately.
Brain wave or electroencephalogram are a kind of than more sensitive objective indicator, and the basic theory that can be not only used for brain science is ground
Study carefully, and prior meaning is the application of its clinical practice, it is closely bound up with the health of the mankind.Brain wave is still current
The most objective foundation of research sleep, changes by E.E.G in monitoring sleep, and people can distinguish the different times in sleep.
Summary of the invention
The purpose of the present invention is to provide a kind of sleep quality monitor based on brain electricity and eye electricity, with versatility and reality
The characteristics of when property, than be currently used for the volume of electroencephalograph class product of medical field it is smaller, using it is more convenient, wear it is more comfortable
And the sleep phases stage judge it is more acurrate.The sleep quality monitoring method based on brain electricity and eye electricity that the present invention also provides a kind of,
The sleep phases stage can be judged online.
A kind of sleep quality monitor based on brain electricity and eye electricity, comprising:
Signal acquisition module, including the sample electrodes for acquiring brain electricity and electro-ocular signal, and for eliminating interference noise
Driven-right-leg circuit；
Impedance matching module is connected between signal acquisition module and front end amplification module, during being used for transmission, by brain
Electricity and electro-ocular signal reach signal amplification module, eliminate the signal for being reflected back source point；
Front end amplification module receives the brain electricity and electro-ocular signal that signal acquisition module is transmitted and amplifies；
Filter module, the brain electricity and electro-ocular signal of the transmission of receiving front-end amplification module, removes industrial frequency noise and myoelectricity interference；
D/A converter module, the brain electricity and electro-ocular signal of the module transfer that accepts filter, is converted to number for its analog signal
Signal；
Processor receives the brain electricity and electro-ocular signal of D/A converter module transmission, extracts the frequency domain of brain electricity and electro-ocular signal
Feature；
Client receives the characteristic information of processor transmission, carries out sleep stage, judgement using the classifier of machine learning
Sleep stage and sleep quality；
Module is locally stored, the data for storage processor output；
Communication module, for the communication between processor and client.
The sample electrodes include brain electrode and eye electrode, and the position of the brain electrode is antinion midpoint FPz, central point
Cz, vertex Pz and pillow point Oz, the position of the eye electrode are horizontal and vertical, acquisition electroculogram EOG.The sample electrodes integration
In the electrode cap using flexible guide face material.
The communication module includes USB module and bluetooth module, and the data-interface end of the USB module connects processor,
The end USB connects client；Bluetooth module is connected to processor by serial ports, and wireless side is connect with the client being located in computer.
The lowpass digital filter includes finite impulse response (FIR) filter and infinite impulse response (IIR) filtering
Device.When extracting the bioelectrical signals such as brain electricity and eye electricity, human body impedance is big, causes jitter, noise serious, needs using low
Logical digital filter is filtered.During handling EEG signals, need to carry out signal using lowpass digital filter straight
The processing of ingredient removal and radio-frequency component removal is flowed, anti-stop signal is raised or drags down, selects in 1-35Hz frequency range
Signal.
EEG signals are very faint, about tens microvolt of numerical values recited, and human body impedance is big, and signal is vulnerable to interference；And by
The body of patient Yu can also be used as antenna can be this dry by the household of electromagnetic interference, especially 50Hz power supply power frequency noise
The bio signal that may be covered is disturbed, so that signal is difficult to measure, therefore adds driven-right-leg circuit to eliminate interference noise, is disappeared
Influence except 50Hz power frequency to feeble computer signals；Driven-right-leg circuit is also referred to as common-mode signal suppression circuit, is generally used for
In bio signal amplifying circuit, to reduce common mode interference.
The front end amplification module includes instrument amplifier；Brain electricity and electro-ocular signal are small, it is therefore desirable to amplify place
Reason；Preferably, the instrument amplifier is the small signal instrument amplifier of IN333.
D/A converter module includes high-precision adc chip；Preferably, the high-precision adc chip is what ADI company produced
AD8232 bioelectrical signals analog/digital conversion chip.
The small signal instrument amplifier of IN333 passes through analog signal line and AD8232 bioelectrical signals analog/digital conversion chip phase
Connection.
The host computer or client, for carrying out visualization processing.
Preferably, described that the NOR flash storage equipment that module includes large capacity is locally stored.
The sleep quality monitor further includes power module, for providing power supply to other modules.
The sleep quality monitor further includes the LED and key being connected with processor.
The present invention also provides a kind of monitoring methods using above-mentioned sleep quality monitor, comprising the following steps:
(1) brain electricity and electro-ocular signal are acquired with the sample electrodes in signal acquisition module, and is eliminated with driven-right-leg circuit
Interference noise；
(2) brain electricity and electro-ocular signal pass sequentially through impedance matching module, front end amplification module, filter module and digital-to-analogue conversion
After module, output digit signals to processor；
(3) processor receives the brain electricity and electro-ocular signal of D/A converter module transmission, with Time-Frequency Analysis Method processing brain electricity
And electro-ocular signal extracts characteristic information；
(4) client receives the characteristic information of processor transmission by communication module, is carried out with the classifier of machine learning
Sleep stage judges sleep stage and sleep quality.
The method that the processor handles brain electricity and electro-ocular signal are as follows:
(3-1) uses lowpass digital filter removal brain electricity and flip-flop and radio-frequency component in electro-ocular signal；
(3-2) carries out short time discrete Fourier transform to the brain electricity and electro-ocular signal of removal flip-flop and radio-frequency component, obtains
The original brain electricity of each sleep stage and the frequency domain character information of electro-ocular signal；Simultaneously to the brain of removal flip-flop and radio-frequency component
Electricity and electro-ocular signal carry out Hilbert transform, obtain each sleep stage original brain electricity and electro-ocular signal δ wave band, θ wave band,
The signal envelope of α wave band, beta band；Gaussian window is carried out to the brain electricity and electro-ocular signal of removal flip-flop and radio-frequency component simultaneously
Fourier transform obtains δ wave band, θ wave band, the α wave band, beta band frequency domain character information on predeterminated frequency.
Specifically, using the brain of doctor's labeled like to sleep dormancy stage and sleep quality in the classifier of machine learning
The parameter of electricity and the classifier of electro-ocular signal data training machine study, with trained classifier according to the frequency domain character of input
Information, time-domain signal envelope and local frequency domain character information carry out sleep stage, judge sleep stage and sleep quality.
The classifier of the machine learning includes the characteristic information of preset various sleep stages, and characteristic information includes frequency domain
Characteristic information, time-domain signal envelope and local frequency domain character information.
The machine learning method of the classifier of the machine learning includes network learning method, support vector machine method
Deng.
The sleep stage includes NREM (non-rapid eye movement sleep) and REM (rapid eye movement sleep).
Sleep quality monitor provided by the invention can acquire brain electricity and electro-ocular signal of the subject in sleep procedure,
NREM with REM difference phase of the subject in sleep is monitored, the sleep matter of subject is judged using the classifier of machine learning
Amount, obtains user's sleep quality grade；The brain electrical feature signal (δ wave band, θ wave band, α wave band, beta band) for obtaining each stage, into
It and is to improve sleep to propose suitable suggest.
FPz-Cz, Pz-Oz may be implemented in sleep quality monitor provided by the invention, and horizontal and vertical EOG etc. is multiple logical
Road brain electricity and eye electricity data acquire simultaneously, and the real-time reception including multichannel brain electric and eye electricity data is shown, multichannel brain electric and
Functions, the sleep stages such as the Real-Time Filtering of the real-time display of the electric data of eye, multichannel brain electric and eye electricity data are judged by stages
And it records as a result, carrying out brain wave exception monitoring simultaneously；And in the real-time monitoring stage, abnormal signal is captured, and by each second
Brain electricity and the amplification storage of eye electricity details, and record number.
Sleep quality monitor provided by the invention mainly using EEG signals, be aided with using electro-ocular signal to sleep carry out
Monitor and then determine sleep quality, and can judge the sleep phases stage online, than be based in existing medical field brain electricity and
Eye electricity sleep monitor class small product size it is smaller, using it is more convenient, wear it is more comfortable, can satisfy family health care, science
The actual needs of research.
Detailed description of the invention
Fig. 1 is the systematic schematic diagram of sleep quality monitor provided by the invention；
Fig. 2 is 1 midbrain of embodiment electricity and electro-ocular signal flow graph；
Fig. 3 is the flow chart handled in embodiment 1 brain electricity and electro-ocular signal.
Specific embodiment
The invention will be further described with attached drawing combined with specific embodiments below.
Embodiment 1
As shown in Figure 1, the sleep quality monitor based on brain electricity and eye electricity includes:
Signal acquisition module, including sample electrodes and driven-right-leg circuit, sample electrodes include brain electrode and eye electrode, are divided
EEG signals and electro-ocular signal Yong Yu not acquired；The position of the brain electrode be antinion midpoint FPz, central point Cz, vertex Pz and
Point Oz is rested the head on, the position of the eye electrode is horizontal and vertical；Driven-right-leg circuit is for sample electrodes in acquisition brain electricity and eye electricity
The interference noise of signal；
Impedance matching module is connected between signal acquisition module and front end amplification module, during being used for transmission, by brain
Electricity and electro-ocular signal reach signal amplification module, eliminate the signal for being reflected back source point；
Front end amplification module receives the brain electricity and electro-ocular signal that signal acquisition module is transmitted and amplifies；In this implementation
In example, the small signal instrument amplifier of IN333 is selected；
Filter module, the brain electricity and electro-ocular signal of the transmission of receiving front-end amplification module, removes industrial frequency noise and myoelectricity interference；
D/A converter module, the brain electricity and electro-ocular signal of the module transfer that accepts filter, is converted to number for its analog signal
Signal；In the present embodiment, using the AD8232 bioelectrical signals analog/digital conversion chip of ADI；
Processor receives the brain electricity and electro-ocular signal of D/A converter module transmission, extracts the frequency domain of brain electricity and electro-ocular signal
Characteristic information, time-domain signal envelope and local frequency domain character information, in the present embodiment, processor selects STM32F411；
Client receives frequency domain character information, time-domain signal envelope and the local frequency domain character information of processor transmission, makes
Sleep stage is carried out with the classifier of machine learning, judges sleep stage and sleep quality.
Module is locally stored, in the present embodiment, equipment is stored using the NOR flash of large capacity；
Communication module, including USB module and bluetooth module, for connecting processor and client；
Power module, for powering.
In the present embodiment, signal acquisition module is using the brain electrode and eye electrode of disposable electrode as electrode wearing side
Formula acquires FPz-Cz, Pz-Oz EEG signals and horizontal and vertical EOG electro-ocular signal；Electrode is incorporated into using flexible guide face material
In electrode cap, electrode cap can be worn on the head of user.
Wherein, the small signal instrument amplifier of IN333 passes through SPI interface and AD8232 bioelectrical signals analog/digital conversion chip
It is connected, converts analog signals into digital signal；Then collected digital signal is connected to by SPI interface
STM32F411, STM32F411 are connected with module is locally stored, and communication module includes USB module and bluetooth module, USB module
Data-interface end connect processor, the end USB is direct-connected to connect client；Bluetooth module is connected to processor by serial ports, wireless side to
Client transmissions data.
Wherein, client operation in a computer, is connected and communicated by bluetooth and USB with sleep quality monitor.
Wherein, brain electricity and electro-ocular signal flow to schematic diagram as shown in Fig. 2, the brain electricity and electro-ocular signal of acquisition are successively by resistance
Anti- matching, signal amplifies, carries out signal conversion, at processor progress signal in D/A converter module in the amplification module of front end
It is being locally stored and is communicating after reason.
Specifically, as shown in figure 3, the method that processor and client handle brain electricity and electro-ocular signal are as follows:
(1) straight using lowpass digital filter removal brain electricity and flip-flop and radio-frequency component in electro-ocular signal, removal
Stream ingredient can prevent brain electricity and electro-ocular signal to be raised or drag down, and remove 50Hz Hz noise.
(2) radio-frequency component is removed, brain electricity and electro-ocular signal in 1-35Hz frequency range are obtained；
(3) the brain electricity and electro-ocular signal progress short time discrete Fourier transform obtained to step (2), obtains the original of each sleep stage
The frequency-region signal of beginning brain electricity and electro-ocular signal；
(4) Hilbert transform is carried out to the brain electricity and electro-ocular signal of removal flip-flop and radio-frequency component, acquisition is respectively slept
The dormancy stage original brain electricity and electro-ocular signal δ wave band, θ wave band, α wave band, beta band signal envelope；
(5) Gaussian window Fourier transform is carried out to the brain electricity and electro-ocular signal of removal flip-flop and radio-frequency component, obtained
δ wave band, θ wave band, α wave band on predeterminated frequency, beta band frequency domain character information；
(6) with trained classifier according to the frequency domain character information of input, time-domain signal envelope and local frequency domain character
Information carries out sleep stage, judges sleep stage and sleep quality.
Claims (8)
1. a kind of sleep quality monitor based on brain electricity and eye electricity, comprising:
Signal acquisition module, including the sample electrodes for acquiring brain electricity and electro-ocular signal, and the right side for eliminating interference noise
Leg driving circuit；
Front end amplification module receives the brain electricity and electro-ocular signal that signal acquisition module is transmitted and amplifies；
Filter module, the brain electricity and electro-ocular signal of the transmission of receiving front-end amplification module, removes industrial frequency noise and myoelectricity interference；
D/A converter module, the brain electricity and electro-ocular signal of the module transfer that accepts filter, is converted to digital signal for its analog signal；
Processor receives the brain electricity and electro-ocular signal of D/A converter module transmission, with Time-Frequency Analysis Method processing brain electricity and eye electricity
Signal extracts characteristic information；
Client receives the characteristic information of processor transmission, carries out sleep stage using the classifier of machine learning, judges to sleep
Stage and sleep quality.
2. the sleep quality monitor according to claim 1 based on brain electricity and eye electricity, which is characterized in that the sleep matter
Measure monitor further include:
Impedance matching module is connected between signal acquisition module and front end amplification module, during being used for transmission, by brain electricity and
Electro-ocular signal reaches signal amplification module, eliminates the signal for being reflected back source point；
Module is locally stored, the data for storage processor output；
Communication module, for the communication between processor and client.
3. the sleep quality monitor according to claim 1 based on brain electricity and eye electricity, which is characterized in that the sampling electricity
Pole includes brain electrode and eye electrode, and the position of the brain electrode is antinion midpoint, central point, vertex and pillow point, the eye electrode
Position be it is horizontal and vertical.
4. the sleep quality monitor according to claim 2 based on brain electricity and eye electricity, which is characterized in that the communication mould
Block includes USB module and bluetooth module, and the data-interface end of the USB module connects processor, and the end USB connects client；It is blue
Tooth module is connected to processor by serial ports, and wireless side is connect with client.
5. the sleep quality monitor according to claim 1 based on brain electricity and eye electricity, which is characterized in that the processor
Including lowpass digital filter, the lowpass digital filter includes finite impulse response filter and infinite impulse response filtering
Device.
6. a kind of monitoring method using any sleep quality monitor based on brain electricity and eye electricity of claim 1-5,
The following steps are included:
(1) brain electricity and electro-ocular signal are acquired with the sample electrodes in signal acquisition module, and is eliminated and is interfered with driven-right-leg circuit
Noise；
(2) brain electricity and electro-ocular signal pass sequentially through impedance matching module, front end amplification module, filter module and D/A converter module
Afterwards, output digit signals are to processor；
(3) processor receives the brain electricity and electro-ocular signal of D/A converter module transmission, with Time-Frequency Analysis Method processing brain electricity and eye
Electric signal extracts characteristic information；
(4) client receives the characteristic information of processor transmission by communication module, is slept with the classifier of machine learning
By stages, judge sleep stage and sleep quality.
7. the monitoring method of the sleep quality monitor according to claim 6 based on brain electricity and eye electricity, which is characterized in that
The method that the processor handles brain electricity and electro-ocular signal are as follows:
(3-1) uses lowpass digital filter removal brain electricity and flip-flop and radio-frequency component in electro-ocular signal；
(3-2) carries out short time discrete Fourier transform to the brain electricity and electro-ocular signal of removal flip-flop and radio-frequency component, and acquisition is respectively slept
The frequency domain character information of the original signal in dormancy stage；Simultaneously to removal flip-flop and radio-frequency component brain electricity and electro-ocular signal into
Row Hilbert transform obtains the original brain electricity of each sleep stage and the time-domain signal envelope of electro-ocular signal；It is straight to removing simultaneously
The brain electricity and electro-ocular signal for flowing ingredient and radio-frequency component carry out Gaussian window Fourier transform, obtain the local frequency domain on predeterminated frequency
Characteristic information.
8. the monitoring method of the sleep quality monitor according to claim 6 based on brain electricity and eye electricity, which is characterized in that
In the classifier of machine learning, the brain of sleep stage and sleep quality electricity and the training of electro-ocular signal data have been marked using doctor
The parameter of the classifier of machine learning, with trained classifier according to the frequency domain character information of input, time-domain signal envelope and
Local frequency domain character information carries out sleep stage, judges sleep stage and sleep quality.
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| CN109893128A (en) * | 2019-04-02 | 2019-06-18 | 深圳市康立高科技有限公司 | A kind of ultralow frequency brain wave detector and its determination method |
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