CN106512206A - Implanted closed-loop brain deep stimulating system - Google Patents
Implanted closed-loop brain deep stimulating system Download PDFInfo
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- A61N1/00—Electrotherapy; Circuits therefor
- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
- A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
- A61N1/36067—Movement disorders, e.g. tremor or Parkinson disease
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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Abstract
The invention provides a fully implanted closed-loop brain deep simulating system based on brain deep electric signal sleep detection. The system utilizes a brain deep electric stimulator electrode for sensing an electrocardiosignal and a brain deep electric signal at a stimulating target point. The system extracts time domain, frequency domain and complexity analysis characteristics and further realizes detection and judgment on a sleep state by means of a classification algorithm, thereby adjusting the switching state and the output parameter of the stimulator and realizing closed-loop nerve stimulation in the sleep state. The implanted closed-loop brain deep simulating system provided by the invention has advantages of reducing side effects of brain deep electric stimulation and saving electric energy of a battery.
Description
Technical field
The present invention relates to a kind of implantable medical devices, more particularly to a kind of implanted closed loop deep brain stimulation system.
Background technology
Brain depth stimulator is a kind of implantable medical devices, is mainly used in treating the dyskinesia and mental disorder class disease
Disease, its indication include parkinson disease, myodystonia, essential tremor and obsession etc..Brain depth stimulator is generally comprised
Pulse generator, extension lead and stimulating electrode etc..
The operation process of brain depth stimulator is that people study above-mentioned disease and the secret of brain opens a fan window.It is logical
Clinography discovery is crossed, when regaining consciousness, parkinson disease, myodystonia, essential tremor and compulsive patient are corresponding at which
Target spot (such as subthalamic nuclei, pallidum, nucleus ventralis intermedius thalami etc.) is stimulated to have visibly different Neural spike train feature.This electric discharge
Feature is closely related with the clinical symptoms of patient.By taking disturbances in patients with Parkinson disease as an example, the local field potentials of target spot subthalamic nuclei are commonly used
Beta frequency ranges (13-30Hz) vibration is abnormal significantly, significantly correlated with the symptom of the stiff of disturbances in patients with Parkinson disease, bradykinesia.In brain
After deep stimulates, the paradoxical discharge feature of Beta frequency ranges is suppressed, and clinical symptoms are also improved.It is in disturbances in patients with Parkinson disease and sleeps
During dormancy state, the local field potentials active frequency band of subthalamic nuclei also has respective discharge characteristic under different sleep stages.Suffer from
Person in sleep 2-4 phases when local field potentials activity based on Delta, Theta and Alpha frequency range, Beta frequency ranges are significantly reduced,
And Beta activities recover under the rapid eye movement phase, liveness even can be higher than clear-headed period.Therefore, brain deep electrical activity can be anti-
Mirror the different genius morbi of patient and state.
With the development in neuromodulation field, the mechanism of action of lesions located in deep brain and associating increasingly for parameters of electrical stimulation
Closely.Lesions located in deep brain is directly intervened to brain deep electrical activity as a kind of, and which is possible with the relation of brain electricity spontaneous activity
Curative effect and the side effect of stimulation can be affected.Clinical research finds that the symptom of the dyskinesia class patient such as parkinson disease is in sleep
Period can be alleviated or even eliminate.Human brain sleep period motor function and clear-headed period be very different.If adopt held
The electricity irritation of continuous parameter constant, will certainly waste electric energy, or even can bring side effect.Studies have reported that, lasting high frequency
Electricity irritation can disturb normal neural circuit to a certain extent, cause patient in the heart of cognition, behavior, etc. aspect produce it is bad
Affect.The problems referred to above cause to develop more is compeled based on the demand of the closed loop brain depth stimulator of brain deep electrical activity feedback signal
Cut.
Brain depth stimulator with perceptive function provides possibility for said process.This brain depth stimulator being capable of profit
The signal of telecommunication at target spot is gathered with existing electrode, and is stored in stimulator internal storage or is passed by way of real-time Transmission
To external monitor supervision platform, to realize the long record of electrical activity of neurons at target spot is realized after being implanted into brain pacemaker entirely in vivo.
This record, reflects the relation between electrical activity of neurons at body state and therapy target in real time.Clinically, at present
The closed loop control realized using the brain depth stimulator with perceptive function is primarily directed to Parkinsonian cardinal symptom, such as stiff
Directly, tremble, bradykinesia and center line symptom etc., its feedback algorithm is mainly by the amplitude triggering thorn of the Beta frequency ranges at target spot
Swash output, while clinical treatment symptom is not weakened, reduce output power consumption, it is to avoid continuous high-frequency electrical stimulation.But, it is this
Method improves too not big meaning for the symptom that patient persistently occurs when clear-headed.In addition, a kind of be based on sleep detection
Integrated form closed loop brain depth stimulator, need the dynamic sensor acquisition feedback signal of integrated extra body, it is difficult to cure in implanted
The internal body portion for treating apparatus is fully achieved.Also a kind of sleep quality assessment system, needs using EEG signal that (EEG signal does not belong to
In the brain deep signal of telecommunication) or the Polysomnography system of outside complete the judgement of sleep stage and quality, the complexity of system
It is higher.
The content of the invention
For the problems referred to above, the purpose of the present invention is to realize a kind of full-implantation type based on brain deep signal of telecommunication sleep detection
Closed loop brain depth stimulator, to realize dormant detection and judgement, so as to the parameter of real-time regulation stimulator output, realizes
Closed loop nerve stimulation under sleep state.
A kind of implanted closed loop deep brain stimulation system, wherein, the implanted closed loop deep brain stimulation system includes：
Pulse generator and stimulating electrode, for exporting electric stimulation pulse and receiving electricity physiological signal；
Signal acquisition module, for gathering the electricity physiological signal of stimulating electrode reception；
Sleep analysis module, for being analyzed to electricity physiological signal, obtains the whether normal judged result of vital sign,
And obtain dormant analysis result；
Closed loop control module, for according to judged result and dormant analysis result adjustment output parameter, with to arteries and veins
The stimulus parameter for rushing generator is adjusted.
Wherein in one embodiment, the sleep analysis module includes：
Signal separation module, for separating to electricity physiological signal, obtains the brain deep signal of telecommunication and electrocardiosignal；
Sleep signal analysis module, the sleep signal analysis module are used for by parser to electrocardiosignal and brain depth
Portion's signal is analyzed, and obtains characteristic index to judge vital sign whether normal and sleep state, and the parser includes
One or more in temporal analysiss, frequency domain analysises, analysis of complexity algorithm and classifier algorithm, this feature index includes
One or more in temporal signatures index, frequency domain character index and complexity features index；The temporal signatures index is used for
Judge whether the basic vital sign of patient is normal；The characteristic index is used to judge sleep state；
Emergency judge module, for judging whether emergency occur according to temporal signatures index.
Wherein in one embodiment, the sleep signal analysis module is further used for：
Temporal signatures index, frequency domain character index and complexity profile are weighted, time domain synthesis analysis is obtained
As a result, frequency domain synthesis analysis result and complexity Comprehensive analysis results；
Judge whether the basic vital sign of patient is normal according to temporal signatures index, according to time domain synthesis analysis result, frequency
One or more in domain Comprehensive analysis results and complexity Comprehensive analysis results, judges sleep state.
Wherein in one embodiment, the sleep signal analysis module is used for：
Time-domain analyses acquisition is carried out to the electrocardiosignal after separation and the brain deep signal of telecommunication with electrocardiosignal and brain deep electricity
The amplitude of signal, the temporal signatures index of time correlation；
Temporal signatures index is weighted, time domain synthesis analysis result is obtained, to electrocardiosignal and brain deep electricity
Signal carries out frequency-domain analysiss and extracts frequency domain character index, and frequency domain character index is weighted, and obtains frequency domain synthesis
Analysis result；
Sleep state is judged according to time domain synthesis analysis result and frequency domain synthesis analysis result.
Wherein in one embodiment, the temporal signatures index includes the amplitude of electrocardiosignal and the brain deep signal of telecommunication
Average E, mean-square value Es, root-mean-square value Esq, variance V, standard deviation Sd, peak-to-peak value P, and the transient state of cross correlation value R, electrocardiosignal
Heart rate CtWith R wave numbers C per minuter, the sleep analysis module is weighted to temporal signatures index, obtains time domain synthesis
Analysis result includes：
T=w1E+w2Es+w3Esq+w4V+w5Sd+w6P+w7R+w8Ct+w9Cr；
Wherein, wi(i ∈ 1,2 ... 9) be each temporal signatures index weight, w1、w2、w3、w6For just or 0, w4、w5、w7、
w8、w9It is time domain synthesis analysis result for negative or 0, T.
Wherein in one embodiment, the sleep signal analysis module is weighted to frequency domain character index, is obtained
Obtaining frequency domain synthesis analysis result includes：
A=w1Iδ+w2Iθ+w3Iα+w4Iβ+w5Iγ+w6Ihigh；
Wherein, Ix(x ∈ δ, θ, α, beta, gamma, high) is brain deep signal of telecommunication δ frequency ranges, θ frequency ranges, α frequency ranges, β frequency ranges, γ frequency
The characteristic index of each frequency range in section and high-frequency band；wiFor the weight of each frequency range, w1≤ 0, w2≤ 0, w3≤ 0, w4>=0, w5
>=0, w6≥0；A is the weighing computation results of each frequency range characteristic index of the brain deep signal of telecommunication, characterizes sleep period diencephalon deep electricity
The mix of activities intensity of signal.
Wherein in one embodiment, the sleep signal analysis module is used for：
The sample entropy S of the brain deep signal of telecommunication is gone out by the ANALYSIS OF CALCULATING of Sample Entropysamp, refer to as complexity features
Mark；
Using multi-scale entropy, multi-scale entropy sequence S is calculatedmsamp(i), as complexity features index；
By the computational methods of correlation dimension, the correlation dimension numerical value D of chaos time sequence is obtained, is referred to as complexity features
Mark；
Repetitive rate Rec, definitiveness Dec, comentropy S, stratiform degree Lam are obtained using the result of recurrence quantification analysis, as
Complexity features index；
Each complexity features index is weighted, complexity Comprehensive analysis results are obtained：
C=w1Ssamp+w2Smsamp(i)+w3D+w4Rec+w5Dec+w6Lam+w7S；
Wherein, wi(i=1,2 ..., 7) be every kind of complexity features index weight, for value and signal complexity into
Positively related characteristic index wiFor just or 0, for value with signal complexity into negatively correlated characteristic index wiFor negative or 0；C is
Brain deep signal of telecommunication complexity weighing computation results, characterize the complexity of the sleep period diencephalon deep signal of telecommunication.
Wherein in one embodiment, the parser that the sleep signal analysis module is adopted further includes classification
Device algorithm, the classifier algorithm is using grader to frequency domain character index, temporal signatures index and complexity features index
Classified, obtained dormant analysis result, including：
The brain deep signal of telecommunication to collecting carries out pretreatment, obtains the pretreated brain deep signal of telecommunication, pretreatment bag
Include high low-pass filtering, garbage signal to reject；
Feature calculation is carried out using the pretreated brain deep signal of telecommunication, the signal obtained corresponding to the signal of telecommunication of brain deep is special
Vector is levied, the signal characteristic vector includes frequency domain character index, temporal signatures index and complexity features index；
Signal characteristic vector input grader is obtained into classification results of sleeping.
Wherein in one embodiment, the closed loop control module is used for：
When sleep analysis module judges into emergency to process, then closed loop control module exports shutdown command to close
Pulse generator；
When sleep analysis module judges that need not enter emergency is processed, then closed loop control module is according to sleep stage
Analysis result exports parameters of electrical stimulation adjust instruction to pulse generator, to adjust the parameters of electrical stimulation of pulse generator.
Wherein in one embodiment, signal transmission module is further included, the signal transmission module is used for will collection
To electricity physiological signal, vital sign whether normal judged result, dormant analysis result, parameters of electrical stimulation transmit to
Monitored in vitro system, and receive to carry out exogenic parameters of electrical stimulation setting and update.
The implanted closed loop deep brain stimulation system that the present invention is provided, is carried out to electricity physiological signal by sleep analysis module
Analysis, judges vital sign and obtains analysis result, and analysis result is being fed back to closed loop control module adjustment stimulation ginseng then
Number, is capable of achieving accurate sleep monitor and closed loop stimulation therapy, inside brain depth stimulator for saving battery electric quantity, drop
The side effect that low lasting electricity irritation brings is significant, and truly realizes internal closed loop control.
In addition, judging whether emergency, awake state occur by being analyzed to electrocardiosignal and brain deep signal
Accurately judged with specific sleep stage, and without the need for the external signal outside deep brain, and patient can slept
Real-time monitoring being carried out during dormancy, the process of emergency can be carried out when there is emergency.
Description of the drawings
Closed loop brain depth stimulator system framework figures of the Fig. 1 for first embodiment of the invention；
The structural representation of the signal acquisition module that Fig. 2 is provided for first embodiment of the invention；
Sleep analysis modular structure schematic diagrams of the Fig. 3 for first embodiment of the invention；
Fig. 4 is the flow chart of sleep stage analysis method provided in an embodiment of the present invention；
The flow chart of the sleep stage analysis method that Fig. 5 is provided for first embodiment of the invention；
Sleep stage parser flow charts of the Fig. 6 for first embodiment of the invention；
Sleep stage parser flow charts of the Fig. 7 for second embodiment of the invention；
Sleep stage parser flow charts of the Fig. 8 for third embodiment of the invention；
Fig. 9 is closed loop control module workflow diagram provided in an embodiment of the present invention；
Figure 10 is signal transmission module operating diagram provided in an embodiment of the present invention.
Specific embodiment
Embodiments of the invention are described below in detail, the example of embodiment is shown in the drawings, wherein identical from start to finish
Or similar label represents same or similar element or the element with same or like function.Retouch below with reference to accompanying drawing
The embodiment stated is exemplary, is only used for explaining the present invention, and is not construed as limiting the claims.
Refer to Fig. 1, the closed loop brain depth stimulator system 100 that first embodiment of the invention is provided include stimulating electrode 1,
Pulse generator 2, signal acquisition module 3, sleep analysis module 4, closed loop control module 5, signal transmission module 6, metal shell 7
With external monitor system 8.The stimulating electrode 1, pulse generator 2, signal acquisition module 3, sleep analysis module 4, closed loop control
Molding block 5, signal transmission module 6, metal shell 7 are implanted in patient's body, wherein pulse generator 2, signal acquisition module
3rd, sleep analysis module 4, closed loop control module 5 and signal transmission module 6 are wrapped up by metal shell 7, are implanted in patient front lock
Below bone；Stimulating electrode 1 is implanted in brain in patients deep, and electrically connects with the pulse generator 2.The pulse generator 2,
Signal acquisition module 3, sleep analysis module 4, closed loop control module 5 and signal transmission module 6 are electrically connected to each other.
The pulse generator 2 is electrically connected with the stimulating electrode 1 and metal shell 7, for exporting electric stimulation pulse；Institute
Stimulating electrode 1 is stated for electric stimulation pulse being exported to patient, and receives electricity physiological signal；The metal shell 7 is used to carry
Each internal module, and receive human body electricity physiological signal；The signal acquisition module 3 is used to gather outside stimulating electrode 1 and metal
The electricity physiological signal that shell 7 is received；The sleep analysis module 4 is analyzed for the electricity physiological signal to collecting, and is obtained
Signal analysis result；The closed loop control module 5 adjusts output parameter according to the signal analysis result of sleep analysis module 4；Institute
The signal analysis result that signal transmission module 6 is stated for sleep analysis module 4 is obtained is sent to external monitor system 8, and
The signal control instruction of receiving body external electronic monitoring system 8, sends it to closed loop control module 5 and pulse generator 2, with to closing
Ring control module 5 is controlled in vitro, is carried out parameter adjustment setting to pulse generator 2, can also be updated sleep analysis module 4
Parser parameter.
Specifically, pulse generator 2 is connected with stimulating electrode 1 and metal shell 7, for exporting electric stimulation pulse, such as permanent
Stream mode or constant voltage mode, electricity physiological signal are transmitted to signal acquisition module 3 by stimulating electrode 1 and metal shell 7, are collected
Electricity physiological signal transmits to sleep analysis module 4 acquisition signal analysis result after being analyzed, the signal analysis result input
Closed loop control module 5 is used to adjust output parameter, and the output parameter after adjustment is transmitted again to pulse generator 2, realizes closed loop brain
Deep stimulates.Further, signal analysis result can be also sent to external monitor system 8 by signal transmission module 6, external to guard
System 8 can also control the work of closed loop control module 5 or the parameter setting of pulse generator 2 by signal transmission module 6.
The stimulating electrode 1 is implanted in brain deep therapy target spot, and multiple stimulation contacts are may include on an electrode.
Pulse generator 2 is electrically connected with stimulating electrode 1 and metal shell 7, for (including gold in any two hard contact
The electric stimulation pulse of predefined parameter is exported between belonging to shell 7), the predefined parameter includes amplitude, frequency, pulsewidth, stimulus modelity
Deng.
The signal acquisition module 3 is electrically connected with stimulating electrode 1 and metal shell 7, in any two hard contact
Between gather electricity physiological signal, including the brain deep signal of telecommunication and electrocardiosignal.
In one embodiment of the invention, signal acquisition module 3 can be while electronic stimulation, to brain deep electricity
Signal and electrocardiosignal are acquired.According to sample rate and the difference of filter amplification circuit, the brain deep signal of telecommunication can adopt office
Portion's field potential (Local Field Potential) or single unit discharge (Spike).
Further, Fig. 2 is seen also, signal acquisition module 3 may include the preposition process mould of channel selecting module 31, signal
Block 32, analog/digital sampling module 33 and signal memory module 34.Wherein, channel selecting module 31 is used to select stimulating electrode 1
With the electrode contacts of various combination in metal shell 7, when any two in stimulating electrode 1 is selected stimulates contact, collection
Electricity physiological signal is the brain deep signal of telecommunication；It is the metal shell 7 and certain electrode contacts on stimulating electrode 1 when what is selected
When, the electricity physiological signal of collection is the mixed signal of electrocardiosignal and the brain deep signal of telecommunication.Specifically, channel selecting module 31 can
Realize with using analog switch group.
The preposition 32 pairs of signals for collecting of processing module of the signal are filtered and amplify.In an enforcement of the present invention
In example, the preposition module of signal 32 includes that filter circuit and differential amplifier circuit are realized.The filter circuit may include RC filtered electricals
Road, active filter etc..
The analog/digital sampling module 33 is used to carry out digital sample.Specifically, the analog/digital sampling module
33 carry out over-sampling to electricity physiological signal first, carry out anti-aliasing filter, carry out down-sampled afterwards, obtain final electro physiology number
Word signals collecting sequence.In one embodiment of the invention, analog/digital can use common a/d converter using module
Realize with low-pass filtering, it is also possible to using the high-precision filter of integrated form, such as delta sigma pattern number converter.
The signal memory module 34 for the electro physiology digital signal acquiring sequence after sampling is stored, Ke Yixuan
Select the conventional embedded system memorizer such as flash storage, SD card.
Fig. 3 is seen also, the structural representation of the sleep analysis module 4 provided for first embodiment of the invention is used for
The brain deep signal of telecommunication and electrocardiosignal are analyzed, judge whether vital sign patient is normal, obtains judged result, and obtains
Dormant analysis result.
Specifically, the sleep analysis module 4 is divided for time-domain analyses, frequency domain are carried out to electrocardiosignal and brain deep signal
Analysis and complexity analyzing, to obtain the related temporal signatures index of electrocardiosignal and brain deep signal, frequency domain character index and answer
Polygamy characteristic index, and obtain time-domain analyses result, frequency-domain analysiss result and complexity analyzing result；And according to temporal signatures
Index judges whether the basic vital sign of patient is normal, and further to temporal signatures index, frequency domain character index and complexity
Index is weighted, and obtains time domain synthesis analysis result, frequency domain synthesis analysis result and complexity Comprehensive analysis results；
Then at least one according to temporal signatures index, frequency domain character index and complexity features index, judges sleep state, or
According at least one in time-domain analyses result, frequency-domain analysiss result and complexity analyzing result, sleep state is judged.Enter one
Step, the sleep analysis module 4 is also using classifier algorithm to temporal signatures index, frequency domain character index and complexity profile
Classified, and sleep stage state is judged according to the classification results of grader.
The sleep analysis module 4 includes that signal separation module 41, sleep signal analysis module 42 and emergency judge
Module 43.Final electro physiology digital signal acquiring sequence is transmitted to sleep analysis module 4, first by signal acquisition module 3
Processed by signal separation module 41, isolated the brain deep signal of telecommunication and electrocardiosignal；Sleep signal analysis module 42 afterwards
Respectively the brain deep signal of telecommunication and electrocardiosignal are analyzed, temporal signatures index is obtained, the temporal signatures index output for obtaining
Emergency judgement is carried out to emergency judge module 43, judges whether vital sign patient is normal；If it is determined that urgent
The vital sign abnormalities such as event, such as patient shock, cardiac arrest or exaltation, then the result is by signal transmission module
6 transmit to external, to play reminding effect.Meanwhile, the dormant analysis result of sleep analysis module 4 is transmitted to closed loop control
Molding block 5.
As the sequence that signal acquisition module 3 is obtained is likely to be the mixed sequence of electrocardiosignal and the brain deep signal of telecommunication,
The signal separation module 41 is for above two physiological signal is separated.In the first embodiment of the present invention, it is described
41 adoptable separation algorithm of signal separation module include Principal Component Analysis Algorithm, independent composition analysis algorithm, it is non-linear it is main into
Divide parser etc..
The sleep signal analysis module 42 is carried out point respectively for the brain deep signal of telecommunication and electrocardiosignal to isolating
Analysis, to obtain the basic vital signss of patient and sleep state.The parser that the sleep signal analysis module 42 is adopted can
Including temporal analysiss, frequency domain analysises, analysis of complexity algorithm and classifier algorithm, with to electrocardiosignal and brain deep signal
The analysis of time-domain analyses, frequency-domain analysiss, complexity analyzing and grader is carried out, corresponding analysis result is obtained.According to analytical calculation
Result, the final vital sign to patient of sleep signal analysis module 12, sleep state include sleeping or awaken, sleep stage
It is analyzed and judges, and result is exported to emergency judge module 43.
The emergency judge module 43 for the basic vital sign according to dormant analysis result and patient,
Judge to stimulate whether patient occurs in that the emergencies such as shock or exaltation.In an embodiment of the present invention, what is used is tight
Anxious event judgment method can utilize the temporal signatures index of electrocardiosignal, frequency domain character index to judge, such as electrocardio amplitude it is too low,
Frequency is too high or too low, that is, judge that patient occurs in that emergency.
The closed loop control module 5 judges whether into promptly for the analysis result obtained according to sleep analysis module 4
Event handling.If current patents are in emergency, lesions located in deep brain are immediately closed off, prevents electricity irritation from bringing further
Harm.If nonemergency, according to during the fructufy of sleep stage analysis or (as per 30s) goes to adjust electricity irritation ginseng off and on
Number, including amplitude, frequency, pulsewidth and stimulus modelity.
The signal transmission module 6 is for by the internal signal for collecting and sleep stage analysis result, related stimulus
Parameter is transmitted to monitored in vitro system 8.The signal transmission module 6 is transmitted to monitored in vitro system by way of radio communication
8, the communication can be using radio communication, optic communication, Zigbee communication, Bluetooth communication, mobile cellular network etc.
Wireless low-power consumption communication mode.The signal transmission module 6 is can be additionally used in pre-designed classifier parameters and required spy
Species is levied, is input into internal sleep analysis module 4, so that sleep analysis module 4 can carry out sleep point using classifier methods
Analysis.
Further, the signal transmission module 6 can also receive to come exogenic stimulus parameter setting and the renewal of algorithm.
For example, doctor, clinical assistant director or sufferers themselves can be guarded platform and be closed by intelligent movable and be stimulated, or appropriate adjustment stimulates ginseng
Number；The data that medical practitioner can be obtained according to monitoring platform, are entered to the closed loop algorithm in patient's body by signal transmission module
Row adjustment and improvement, it is also possible to change stimulus parameter.
Fig. 4 is seen also, the method that the utilization sleep analysis module 4 provided for the present invention is analyzed to sleep stage
Flow chart, comprises the steps：
Step S10, carries out time-domain analyses, frequency-domain analysiss and complexity analyzing to electrocardiosignal and brain deep signal, to obtain
The related temporal signatures index of electrocardiosignal and brain deep signal, frequency domain character index and complexity features index are obtained, when obtaining
Domain analysiss result, frequency-domain analysiss result and complexity analyzing result；
According to temporal signatures index, step S20, judges whether the basic vital sign of patient is normal, and further special to time domain
Levy index, frequency domain character index and complexity profile to be weighted, obtain time domain synthesis analysis result, frequency domain synthesis analysis
And complexity Comprehensive analysis results as a result；
Step S30, divides according to temporal signatures index, frequency domain character index and complexity features index, or time domain synthesis
One or more in analysis result, frequency domain synthesis analysis result and complexity Comprehensive analysis results, or referred to according to every kind of feature
Mark composition characteristic vector, judges sleep state.
Specifically, Fig. 5 and Fig. 6 is seen also, the utilization sleep analysis module 4 pairs that first embodiment of the invention is provided is slept
The method that dormancy is analyzed by stages includes：
Step S11, the electrocardiosignal after separation and the brain deep signal of telecommunication are carried out time-domain analyses acquisition and electrocardiosignal and
The amplitude of the brain deep signal of telecommunication, the temporal signatures index of time correlation, judge whether emergency occur；
Step S12, is weighted to temporal signatures index, obtains time domain synthesis analysis result, to electrocardiosignal and
The brain deep signal of telecommunication carries out frequency-domain analysiss and extracts frequency domain character index, and frequency domain character index is weighted, and obtains
Frequency domain synthesis analysis result；
Step S13, judges sleep state according to time-domain analyses result and frequency-domain analysiss result.
Specifically, to the electrocardiosignal after separation and the brain deep signal of telecommunication, can carry out respectively time-domain analyses and frequency domain point
Analysis.
In step s 11, by time-domain analyses, the correlations such as amplitude, the time of electrocardiosignal and the brain deep signal of telecommunication are obtained
Time domain index, obtains time-domain analyses result, can determine whether whether the basic vital sign of patient is normal.The Time Domain Analysis letter
It is single, rapid so that system effectively can be processed to emergency in time.
Further, mainly include the amplitude of electrocardiosignal and the brain deep signal of telecommunication by the characteristic index that time-domain analyses are extracted
Average E, mean-square value Es, root-mean-square value Esq, variance V, standard deviation Sd, peak-to-peak value P etc., and wink of cross correlation value R, electrocardiosignal
State heart rate CtWith R wave numbers C per minuterDeng, by above-mentioned temporal signatures index, can determine whether whether the basic vital sign of patient is normal,
In emergency circumstances to be processed in time the basic vital sign of patient is abnormal.
In step s 12, the temporal signatures index of said extracted can simply be classified：Weighted average process, power
Value wiCan rule of thumb set, be set also dependent on practical situation：
T=w1E+w2Es+w3Esq+w4V+w5Sd+w6P+w7R+w8Ct+w9Cr；
Wherein, wi(i ∈ 1,2 ... 9) be each temporal signatures index weight, w1、w2、w3、w6For just or 0, w4、w5、w7、
w8、w9It is time domain synthesis analysis result for negative or 0, T, weight wiRule of thumb can set, or according to individual variation after implementing to observe
Carry out personalized setting.It is appreciated that each characteristic index of the time domain can be used as sleep with time domain synthesis analysis indexes
The criterion of state analysiss, wherein time domain synthesis analysis indexes are mainly used in the state for embodying signal in time domain entire scope, and each
Characteristic index emphasis embodies each temporal signatures state of signal, and its viewing angle is different.
In step s 12, electrocardiosignal and the brain deep signal of telecommunication are analyzed by using frequency-domain analysis method, are extracted
Frequency domain character index, obtains frequency-domain analysiss result, to determine whether sleep/wakefulness residing for patient, sleep stage shape
State, microarousal state, and predict whether to awaken.
Specifically, frequency domain analysises can be passed through to the δ frequency ranges of the brain deep signal of telecommunication, θ frequency ranges, α frequency ranges, β frequency ranges, γ frequency ranges
Frequency domain character index extraction is carried out respectively with high frequency (high) frequency range (200-350Hz), and these frequency domain character indexs can reflect
The activity intensity of corresponding band；The frequency domain character index of extraction can include frequency sub-band energy, frequency sub-band relative peak, power spectrum
Density, the meansigma methodss of the amplitude phase coupling of different frequency range, intermediate value, maximum or minima etc..
Further, just it is weighted by the characteristic index to each frequency range, frequency domain synthesis analysis result is obtained：
A=w1Iδ+w2Iθ+w3Iα+w4Iβ+w5Iγ+w6Ihigh；
Wherein, IxThe characteristic index of (x ∈ δ, θ, α, beta, gamma, high) for each frequency range of the brain deep signal of telecommunication；wiFor each frequency
The weight of section, w1≤ 0, w2≤ 0, w3≤ 0, w4>=0, w5>=0, w6≥0；A is each frequency range characteristic index of the brain deep signal of telecommunication
Weighing computation results, characterize the mix of activities intensity of the sleep period diencephalon deep signal of telecommunication.Weight w of each frequency rangeiCan be according to Jing
Unified setting is tested, or personalized setting is carried out according to individual variation after implementing observation.
In step s 13, T is bigger, then sleep degree is deeper, and T is less, then sleep degree is more shallow.For the rapid eye movement phase
Sleep, then judged according to the special distributed area of T value, or the other complexity analysis indexes of auxiliary and frequency-domain analysiss index are entered
Row judges.
Further, for sleep state is judged according to frequency domain synthesis analysis result, when A values are higher than some predetermined threshold value
When, showing more than β frequency ranges (include β frequency ranges) that activity intensity is higher, patient is in awakening, 1 phase of sleep, rapid eye movement device or will
Wakefulness；A values are lower, show δ frequency ranges, θ frequency ranges, α frequency ranges energy it is higher, patient is in -4 phase of Sleep Stage 2, or microarousal
State.Using IxThe active state of labor brain deep signal of telecommunication frequency range, when β frequency ranges, γ frequency ranges or stronger high frequency ranges,
Patient is in wakefulness；When only β frequency ranges are stronger, patient is likely to be at the rapid eye movement phase；When α frequency ranges are stronger, patient is in
Slept for 1 phase；When δ frequency ranges or stronger θ frequency ranges, patient is in Sleep Stage 2,3 phases or 4 phases；When β frequency ranges are recovered from the weaker transient state of activity
To relatively strong, when being returned to weaker state immediately, it is judged as microarousal；When A values gradually change from low to high, judge that patient will
Into wakefulness.
Similar, it will be understood that each characteristic index of frequency domain also can be used as sleep point with frequency domain synthesis analysis indexes
The criterion of analysis, wherein frequency domain synthesis analysis indexes are mainly used in the state for embodying signal in frequency domain entire scope, and each feature refers to
Indicated weight point embodies the significant condition of each frequency band signals, and its viewing angle is different.
Frequency domain character index I of the electrocardiosignal extracted after frequency-domain analysisscIncluding the phase of heart rate, heart rate variability etc.
Close frequency domain character index.For example, when Heart Rate progressively stably rises, with reference to the frequency-domain index of the brain deep signal of telecommunication, can be with
Synthetic determination will enter wakefulness for patient；When Heart Rate is higher than at ordinary times, if the persistent period, within 30s, judges
For microarousal, if the persistent period is more than 1min, can be determined that as the REM phases with reference to the brain deep signal of telecommunication；When heart rate is slower, with reference to
The brain deep signal of telecommunication was can be determined that as 1 phase, 2 phases or 3 phases of sleeping.It is appreciated that the frequency domain character index of electrocardiosignal, typically not
Independent analysis is carried out, but is mutually supported with the analysis of the brain deep signal of telecommunication, improve the accuracy of result.
See also Fig. 7, the utilization sleep analysis module 4 that second embodiment of the invention is provided is carried out to sleep stage point
The flow chart of analysis.It is similar with first embodiment, by carrying out time-domain analyses acquisition to the brain deep signal of telecommunication and electrocardiosignal
The foundation that temporal signatures index is judged as emergency.Further the complexity of the brain deep signal of telecommunication and electrocardiosignal is carried out
Analysis, including Sample Entropy, multi-scale entropy, the calculating of correlation dimension, recurrence quantification analysis etc., to judge sleep state.Specifically, sleep
Dormancy degree is deeper, and the complexity that the indices of the brain deep signal of telecommunication and electrocardiosignal reflect is lower, conversely, sleep degree is got over
Shallow, the complexity of the brain deep signal of telecommunication and electrocardiosignal is higher.For example, the Sample Entropy of the brain deep signal of telecommunication is for the quick eye of difference
Dynamic device and microarousal, awakening phase have great significance.
Specifically, the extraction of the complexity features index of the brain deep signal of telecommunication may include：
Step S21, goes out the sample entropy S of one section of brain deep signal of telecommunication by the ANALYSIS OF CALCULATING of Sample Entropysamp, as
One of complexity features index；
Step S22, using multi-scale entropy, calculates multi-scale entropy sequence Smsamp(i), as complexity features index it
One；
Step S23, by the computational methods of correlation dimension, obtains the correlation dimension numerical value D of chaos time sequence, as complexity
One of characteristic index；
Step S24, obtains repetitive rate Rec, definitiveness Dec, comentropy S, stratiform degree using the result of recurrence quantification analysis
Lam, as one of complexity features index.
The result of above-mentioned complexity analyzing, the available feature index that can be judged as sleep signal by stages.When using simple
Sorting technique when, the basis for estimation of above-mentioned every complexity features index is as follows：
Step S25, is weighted to each complexity features index, obtains complexity Comprehensive analysis results：
C=w1Ssamp+w2Smsamp(i)+w3D+w4Rec+w5Dec+w6Lam+w7S；
Wherein, wi(i=1,2 ..., 7) be every kind of complexity features index weight, for value and signal complexity into
Positively related characteristic index wiFor just or 0, for value with signal complexity into negatively correlated characteristic index wiFor negative or 0；C is
Brain deep signal of telecommunication complexity weighing computation results, characterize the complexity of the sleep period diencephalon deep signal of telecommunication, and C values are higher, signal
Complexity is higher, and C values are lower, and signal complexity is lower.In general, the complexity of signal is lower, and sleep degree is deeper, signal
Complexity it is more shallow, sleep it is more shallow.For the special rapid eye movement phase sleeps, can be carried out especially according to the interval of its complexity
Judge, and aid in other characteristic indexs to be calculated.
Preferably, weight w of each featureiSetting can be unified based on experience value, according to individuality after can also implementing to observe
Difference carries out personalized setting.Accordingly, each characteristic index of complexity analyzing can be made with complexity comprehensive analysis index
For the criterion of sleep analysis, wherein to be mainly used in embodying signal overall under different criterions for complexity comprehensive analysis index
Complex characteristics, and each characteristic index emphasis embodies the state of each complexity features, its viewing angle is different.
Fig. 8 is seen also, and the algorithm of sleep analysis is carried out for the sleep analysis module 4 that third embodiment of the invention is provided
Flow chart.Similar with first embodiment, the time-domain analyses characteristic index of the brain deep signal of telecommunication and electrocardiosignal is used as emergency
The foundation of judgement.The characteristic index of extraction includes the characteristic index of the brain deep signal of telecommunication and electrocardiosignal, including frequency domain character refers to
Mark, temporal signatures index and complexity features index, recycling grader is in the brain deep signal of telecommunication and electrocardiosignal
Above-mentioned each characteristic index is classified, to obtain different sleep/wakefulnesss according to classification results.The method that grader is adopted
Principal component analysiss, independent component analysis, support vector machine, artificial neural network, integrated study, deep learning etc. can be included.
The step of utilization grader carries out sleep analysis to the characteristic index in the signal of telecommunication of brain deep includes：
Step S1, the brain deep signal of telecommunication to collecting carry out pretreatment, obtain the pretreated brain deep signal of telecommunication, in advance
Process includes that high low-pass filtering, garbage signal are rejected；
Step S2, carries out feature calculation using the pretreated brain deep signal of telecommunication, obtains corresponding to the signal of telecommunication of brain deep
Signal characteristic vector, the signal characteristic vector includes that frequency domain character index, temporal signatures index and complexity features refer to
Mark；
Above-mentioned signal characteristic vector input grader is obtained classification results of sleeping by step S3.
Specifically, the method for designing of the utilization grader includes：
Step S31, the brain deep signal of telecommunication to collecting carry out pretreatment, and pretreatment includes high low-pass filtering, useless letter
Number reject etc.；
All effective brain deep electrical signal datas are carried out equal length segmentation by step S32, form isometric brain deep telecommunications
Number；
Step S33, carries out feature calculation using the above-mentioned isometric brain deep signal of telecommunication, obtains every section of brain deep electrical signal data
Corresponding block signal characteristic vector；
Step S34, the sleep stage obtained by block signal characteristic vector and in advance carry out it is corresponding, as primary sample
Collection；Further, for same section of sleep state (such as sleep analysis monitor PSG data sets), carry out repeatedly independent sleep stage
Judge, obtain sleep stage；In the sleep result and brain deep signal characteristics vector for obtaining, consistent characteristic segments are chosen
(classification accuracy 100%), as the senior sample set of classifier training；
Step S35, the partial data randomly choosed in senior sample set carry out classifier training, and remainder data are used for
Prediction sleep stage, the accuracy of the accuracy of prediction as the grader, repetition training grader n times (e.g., N=100), if
The rate of accuracy reached of grader is such as close to 100% to preset requirement, is significantly higher than chance level, then it is assumed that the accuracy of the grader
It is up to standard；
Step S36, characteristic segments in the primary sample set of random selection, carries out above-mentioned point using remaining characteristic segments
Class device is trained, and predicts the sleep stage result corresponding to this feature section with the grader for obtaining, and repeats process M (M >=2) secondary；
Step S37, according to the data segment M subseries results of all primary sample sets, calculates the reliability of grader, with people
The reliability of work point class is compared, if being close to, classifier design is finished；Otherwise, modification grader and Feature Selection are continued,
Until meet requiring.
Step S38, the above-mentioned classifier parameters for designing and required feature species are input into by signal transmission module 6
To internal sleep analysis module 4, Real-time Collection electrocardio and the brain deep signal of telecommunication, are classified in advance by sleep signal analysis module afterwards
Feature needed for device, the good grader of In-put design obtain classification results of sleeping.
It is appreciated that step S34- step S37 can be completed in vitro in the design of grader, then pass through signal transmission again
Module 6 is imported in sleep analysis module 4.Further, the difference according to sleep analysis modular algorithm, can be slept in real time
Analysis, it is also possible to carry out intermittent sleep analysis, is such as once analyzed per 30s.
Fig. 9 is seen also, is the workflow diagram of closed loop control module provided in an embodiment of the present invention 5, including it is as follows
Step：
Step S41, when sleep analysis module 4 judges into emergency to process, then the output of closed loop control module 5 shutdown
Instruct to close pulse generator；
Step S42, when sleep analysis module 4 judges that need not enter emergency is processed, then 5 basis of closed loop control module
The analysis result of sleep stage exports parameters of electrical stimulation adjust instruction to pulse generator 2.
Specifically, the input of closed loop control module 5 is the dormant analysis result of sleep analysis module 4, is output as
Lesions located in deep brain parameter.If current patents are in emergency, closed loop control module 5 is then processed into emergency, at once
Lesions located in deep brain is closed, prevents electricity irritation from bringing further harm.If nonemergency, according to sleep stage analysis
During fructufy or off and on (as per 30s) goes to adjust parameters of electrical stimulation, including amplitude, frequency, pulsewidth and stimulus modelity etc..
Further, in an embodiment of the present invention, step S42 comprises the steps to adjust stimulus parameter
It is whole：
When in 1 phase of sleep, output parameters of electrical stimulation reduction instruction is appropriate to be reduced stimulating amplitude；
When in Sleep Stage 2, output parameters of electrical stimulation reduction instruction is further appropriate to reduce stimulus frequency and pulsewidth；
When in 3 phases of sleep and rapid eye movement phase, export shutdown command to turn off stimulation；
When being in the awakening phase or will enter the awakening phase, daily parameters of electrical stimulation instruction is exported to recover daily thorn
Sharp parameter；
Stimulation modification is not made when in the microarousal phase.
Further, if patient has the sleep barrier such as rapid eye movement device behavior disorder, obstructive respiration syndrome, parasomnias
When hindering, closed loop control module can be according to the further corrected parameter of patient's state in which.
Figure 10 is seen also, is that emergency of the present invention processes schematic diagram.Signal transmission module 6 is by radio communication
Mode, the internal signal for collecting and analysis result, related stimulus parameter are transmitted to monitored in vitro platform.Side wireless communication
Formula can be using wireless low-power consumption communication parties such as radio communication, optic communication, Zigbee communication, Bluetooth communication, mobile cellular networks
Formula.
When sleep analysis module 4 judges that patient is in emergency, drive signal 6 pieces of mould of transmission carries out emergency
Under data-transformation facility.Emergency transmissions to the external monitor system are implemented to report to the police and are remembered by signal transmission module 6
Record, it is possible to which remote intervention is implemented by doctor.In an embodiment of the present invention, external monitor system 8 includes intelligent movable
Monitoring platform 81, remote monitoring server 82 and remote monitoring terminal 83.Intelligent movable monitoring platform 81 be used for sufferers themselves,
Family members or clinical assistant director, Community Doctor are processed to emergency；After access network, remote monitoring server is by emergency
Related content is forwarded to remote monitoring terminal 83, carries out the process of emergency by medical practitioner, gives patient's suggestion or necessary
Direct intervention.When patient is normal, it is also possible to report oneself state by remote monitoring platform, carry out daily prison for doctor
Shield.
Figure 10 show closed loop brain depth stimulator system 100 as telemedicine network in an intelligent node, should
Closed loop brain depth stimulator system 100 can effectively monitor the daily electrocardio of patient, brain electricity condition, for tracking clinically
Treatment makes great sense.
Signal transmission module 6 can also receive to come exogenic stimulus parameter setting and the renewal of algorithm.For example, doctor,
Clinical assistant director or sufferers themselves can be guarded platform and be closed by intelligent movable and be stimulated, or suitably adjust stimulus parameter；Special Medical
The data that life can be obtained according to monitoring platform, are adjusted and are changed to the closed loop algorithm in patient's body by signal transmission module
Enter, it is also possible to change stimulus parameter.
The closed loop deep brain stimulation system that the present invention is provided, has the advantages that：
(1) realize sleeping to awake state and specifically using the brain deep signal of telecommunication (e.g., local field potentials) and electrocardiosignal
Sleep and accurately judged by stages, and without the need for the external signal outside brain deep；
(2) accurate sleep monitor and closed loop stimulation therapy are capable of achieving inside brain depth stimulator, it is electric for saving
The side effect that pond electricity, the lasting electricity irritation of reduction bring is significant, and truly realizes internal closed loop control；
(3) using the brain deep signal of telecommunication and electrocardiosignal, real-time monitoring can be carried out in sleep to patient, in case of promptly
Event, can carry out the process of emergency, notify that family numbers of patients or doctor are given first aid in time；
(4) can as can only be in telemedicine network a kind of Implanted intelligent node, the daily electrocardio, brain depth to patient
Portion's electrical signal status are guarded and are intervened.
Each technical characteristic of embodiment described above arbitrarily can be combined, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope of this specification record is all considered to be.
Embodiment described above only expresses the several embodiments of the present invention, and its description is more concrete and detailed, but and
Therefore the restriction to the scope of the claims of the present invention can not be interpreted as.It should be pointed out that for one of ordinary skill in the art
For, without departing from the inventive concept of the premise, some deformations and improvement can also be made, these belong to the guarantor of the present invention
Shield scope.Therefore, the protection domain of patent of the present invention should be defined by claims.
Claims (10)
1. a kind of implanted closed loop deep brain stimulation system, it is characterised in that the implanted closed loop deep brain stimulation system bag
Include：
Pulse generator and stimulating electrode, for exporting electric stimulation pulse and receiving electricity physiological signal；
Signal acquisition module, for gathering the electricity physiological signal of stimulating electrode reception；
Sleep analysis module, for being analyzed to electricity physiological signal, obtains the whether normal judged result of vital sign, and
Obtain dormant analysis result；
Closed loop control module, for according to judged result and dormant analysis result adjustment output parameter, to send out to pulse
The stimulus parameter of raw device is adjusted.
2. implanted closed loop deep brain stimulation system as claimed in claim 1, it is characterised in that the sleep analysis module bag
Include：
Signal separation module, for separating to electricity physiological signal, obtains the brain deep signal of telecommunication and electrocardiosignal；
Sleep signal analysis module, the sleep signal analysis module are used to believe electrocardiosignal and brain deep by parser
Number being analyzed, characteristic index being obtained to judge vital sign whether normal and sleep state, the parser includes time domain
One or more in analytic process, frequency domain analysises, analysis of complexity algorithm and classifier algorithm, this feature index includes time domain
One or more in characteristic index, frequency domain character index and complexity features index；The temporal signatures index is used to judge
Whether the basic vital sign of patient is normal；The characteristic index is used to judge sleep state；
Emergency judge module, for judging whether emergency occur according to temporal signatures index.
3. implanted closed loop deep brain stimulation system as claimed in claim 2, it is characterised in that the sleep signal analyzes mould
Block is further used for：
Temporal signatures index, frequency domain character index and complexity profile are weighted, acquisition time domain synthesis analysis result,
Frequency domain synthesis analysis result and complexity Comprehensive analysis results；
Judge whether the basic vital sign of patient is normal according to temporal signatures index, it is comprehensive according to time domain synthesis analysis result, frequency domain
One or more in conjunction analysis result and complexity Comprehensive analysis results, judges sleep state.
4. implanted closed loop deep brain stimulation system as claimed in claim 2, it is characterised in that the sleep signal analyzes mould
Block is used for：
Time-domain analyses acquisition is carried out to the electrocardiosignal after separation and the brain deep signal of telecommunication with electrocardiosignal and the brain deep signal of telecommunication
Amplitude, the temporal signatures index of time correlation；
Temporal signatures index is weighted, time domain synthesis analysis result is obtained, to electrocardiosignal and the brain deep signal of telecommunication
Carry out frequency-domain analysiss and extract frequency domain character index, and frequency domain character index is weighted, obtain frequency domain synthesis analysis
As a result；
Sleep state is judged according to time domain synthesis analysis result and frequency domain synthesis analysis result.
5. implanted closed loop deep brain stimulation system as claimed in claim 4, it is characterised in that the temporal signatures index bag
Include amplitude average E, mean-square value E of electrocardiosignal and the brain deep signal of telecommunications, root-mean-square value Esq, variance V, standard deviation Sd, peak-to-peak value
P, and the average heart rate C of cross correlation value R, electrocardiosignaltWith R wave numbers C per minuter, the sleep analysis module is to temporal signatures
Index is weighted, and obtaining time domain synthesis analysis result includes：
T=w1E+w2Es+w3Esq+w4V+w5Sd+w6P+w7R+w8Ct+w9Cr；
Wherein, wi(i ∈ 1,2 ... 9) be each temporal signatures index weight, w1、w2、w3、w6For just or 0, w4、w5、w7、w8、w9
It is time domain synthesis analysis result for negative or 0, T.
6. implanted closed loop deep brain stimulation system as claimed in claim 4, it is characterised in that the sleep signal analyzes mould
Block is weighted to frequency domain character index, and obtaining frequency domain synthesis analysis result includes：
A=w1Iδ+w2Iθ+w3Iα+w4Iβ+w5Iγ+w6Ihigh；
Wherein, Ix(x ∈ δ, θ, α, beta, gamma, high) is brain deep signal of telecommunication δ frequency ranges, θ frequency ranges, α frequency ranges, β frequency ranges, γ frequency ranges and
The characteristic index of each frequency range in high-frequency band；wiFor the weight of each frequency range, w1≤ 0, w2≤ 0, w3≤ 0, w4>=0, w5>=0,
w6≥0；A is the weighing computation results of each frequency range characteristic index of the brain deep signal of telecommunication, characterizes the sleep period diencephalon deep signal of telecommunication
Mix of activities intensity.
7. implanted closed loop deep brain stimulation system as claimed in claim 2, it is characterised in that the sleep signal analyzes mould
Block is used for：
The sample entropy S of the brain deep signal of telecommunication is gone out by the ANALYSIS OF CALCULATING of Sample Entropysamp, as complexity features index；
Using multi-scale entropy, multi-scale entropy sequence S is calculatedmsamp(i), as complexity features index；
By the computational methods of correlation dimension, the correlation dimension numerical value D of chaos time sequence is obtained, as complexity features index；
Repetitive rate Rec, definitiveness Dec, comentropy S, stratiform degree Lam are obtained using the result of recurrence quantification analysis, as complexity
Property characteristic index；
Each complexity features index is weighted, complexity Comprehensive analysis results are obtained：
C=w1Ssamp+w2Smsamp(i)+w3D+w4Rec+w5Dec+w6Lam+w7S；
Wherein, wi(i=1,2 ..., 7) be every kind of complexity features index weight, for value and signal complexity are into positive correlation
Characteristic index wiFor just or 0, for value with signal complexity into negatively correlated characteristic index wiFor negative or 0；C is brain deep
Signal of telecommunication complexity weighing computation results, characterize the complexity of the sleep period diencephalon deep signal of telecommunication.
8. implanted closed loop deep brain stimulation system as claimed in claim 2, it is characterised in that the sleep signal analyzes mould
The parser that block is adopted further includes classifier algorithm, the classifier algorithm using grader to frequency domain character index,
Temporal signatures index and complexity features index are classified, and obtain dormant analysis result, including：
The brain deep signal of telecommunication to collecting carries out pretreatment, obtains the pretreated brain deep signal of telecommunication, and pretreatment includes height
Low-pass filtering, garbage signal are rejected；
Feature calculation is carried out using the pretreated brain deep signal of telecommunication, obtain signal characteristic corresponding to the signal of telecommunication of brain deep to
Amount, the signal characteristic vector include frequency domain character index, temporal signatures index and complexity features index；
Signal characteristic vector input grader is obtained into classification results of sleeping.
9. implanted closed loop deep brain stimulation system as claimed in claim 1, it is characterised in that the closed loop control module is used
In：
When sleep analysis module judges into emergency to process, then closed loop control module exports shutdown command to close pulse
Generator；
When sleep analysis module judges that need not enter emergency is processed, then analysis of the closed loop control module according to sleep stage
As a result parameters of electrical stimulation adjust instruction is exported to pulse generator, to adjust the parameters of electrical stimulation of pulse generator.
10. implanted closed loop deep brain stimulation system as claimed in claim 1, it is characterised in that further include that signal is passed
Defeated module, the signal transmission module for by the electricity physiological signal for collecting, vital sign whether normal judged result, sleep
The analysis result of dormancy state, parameters of electrical stimulation are transmitted to monitored in vitro system, and receive to carry out exogenic parameters of electrical stimulation setting
And update.
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