CN103816007A - Equipment and method for tinnitus treatment based on electroencephalogram frequency-domain characteristic indexation algorithm - Google Patents
Equipment and method for tinnitus treatment based on electroencephalogram frequency-domain characteristic indexation algorithm Download PDFInfo
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Abstract
The invention relates to equipment and a method for tinnitus treatment based on an electroencephalogram frequency-domain characteristic indexation algorithm. The equipment comprises a headset, an electroencephalogram signal acquisition device, an IC (integrated circuit) circuit, a signal transmitting device and a terminal device. The electroencephalogram signal acquisition device is used for sampling and quantifying simulated brain waves generated by human brain activities and converting the brain waves into discrete digital signals before subsequent processing. The IC circuit is used for amplifying the quantified digital signals to improve antijamming capability during processing and transmitting. A signal processor is used for denoising preprocessing of acquired signals to improve intensity of brain wave signals. The signal transmitting device is connected with the IC circuit and the terminal device to transmit parameters extracted by the IC circuit to the terminal device. A PC (personal computer) serves as the terminal device to process the parameters, display and feed back. By the aid of the equipment and the method for tinnitus treatment based on the electroencephalogram frequency-domain characteristic indexation algorithm, treatment effects can be objectively judged according to electroencephalograms of patients, and high accuracy, avoidance of subjective judgment of patients and applicability to both hospitals and families are realized.
Description
Technical field
The present invention relates to a kind of tinnitus treatment Apparatus and method for based on brain electricity frequency domain character indexing algorithm.
Background technology
Tinnitus is common clinical, frequently-occurring disease, and its cause of disease does not have clear and definite conclusion at present.Conventionally adopt the treatment means of sound mask, select the sound of different frequency to listen to patient, allow it feel that can that sound make it feel that tinnitus is alleviated or disappearance, then continues this sound to listen the object that has reached treatment or alleviated to patient.This method being felt by patient is not objective, poor accuracy.
Summary of the invention
Object of the present invention, provides a kind of tinnitus treatment equipment based on brain electricity frequency domain character indexing algorithm, objectively to judge, to assess tinnitus treatment effect.
Technical scheme of the present invention is as follows:
Based on a tinnitus treatment equipment for brain electricity frequency domain character indexing algorithm, described equipment comprises earphone, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, it is characterized in that:
Eeg signal acquisition device comprises eeg signal acquisition electrode, EEG signals reference electrode and signal processor, and the simulation brain wave that described harvester produces people's cerebration is sampled, quantized, and becomes discrete digital signal, carries out follow-up processing;
Described IC circuit comprises signal amplifier and signal processor, and described signal amplifier, for for signal preamplifier, amplifies the digital signal of quantification the capacity of resisting disturbance in enhancement process and transmitting procedure;
Described signal processor carries out noise suppression preprocessing to the signal gathering, and strengthens the intensity of eeg signal, and therefrom extracts the parameter that reflection people cognitive state changes, the state of assessment user;
Described signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit;
Described terminal unit is a PC, above-mentioned parameter is processed, and shown and feed back.
Further, described earphone is headband receiver, and eeg signal acquisition device and IC are circuit integrated in earphone body, and is further integrated with battery compartment and on and off switch.
Further, described eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
Further, described signal transmitting apparatus is wireless signal transmission.
Based on a tinnitus treatment method for brain electricity frequency domain character indexing algorithm, it is characterized in that, described method, described method step is as follows:
(1) initialization apparatus hardware, setting can acceptable conditions;
(2) sound source automatically-broadcasting voice gathers eeg data simultaneously;
(3) to the data filtering gathering, denoising, time frequency analysis, calculating brain electricity index;
(4) the brain electricity index of calculating is transferred to client and shows;
(5) judge whether to meet acceptable conditions; If met, set to the sound of playing and can accept labelling; If do not met, again carry out correlation step to step (2).
Further, described indexing algorithm is specific as follows:
(1) pretreatment: the brain wave quantizing is carried out to digital filtering, remove the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter;
(2) feature representation and extraction: extract the basic index of the comprehensive cognitive state of reflection the EEG signals after pretreatment, specifically comprise alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz); Use time-frequency analysis technology that these indexs are extracted from original time-domain signal, the time series form with energy or power on frequency domain is expressed;
(3) indexing represents: above-mentioned basic index is carried out to standardization, make the same index of different users and same user different time have identical physical meaning; Described algorithm output Vigilance level and two indexs of tensity level, the horizontal index of described Vigilance and specific as follows with the horizontal index of tensity:
A (t), b (t) and c (t) represent respectively the clock signal of alpha, beta and tri-frequency ranges of theta, and they are realized by the energy accumulation via time frequency analysis selected special frequency channel of original EEG signals respectively;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represent respectively the energy of alpha and theta;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represent respectively the energy of beta and theta;
(4) judgement of attention level: with normal user in the situation that not having sleepy, fatigue state to occur, the horizontal index of Vigilance that continues to keep to note 2 minutes and and the sequential average of two index series of the horizontal index of tensity 60% as decision threshold, be tired generation lower than this threshold judgement, show that the sound now producing by earphone well restrains tinnitus, show that lower than threshold value the sound that earphone produces is undesirable to tinnitus inhibition.
Further, in described step (1), the low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
Further, the specific algorithm of described step (2) feature representation submodule is as follows:
Adopting Morlat function is mother wavelet function, and brain electricity time-domain signal is carried out to continuous wavelet transform; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, the multiple time series signal that obtains a series of different frequency ranges with above mother wavelet function convolution and after conversion is wavelet coefficient, wherein time and input signal length are consistent, frequency range is to being 1-35Hz, retains wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, represents with power; According to band limits height, extract respectively alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz) time series of mould of multiple wavelet coefficient of corresponding frequency band, power represents the timing variations of band energy.
Further, described step (3) indexing represents to adopt feature normalization algorithm, that is:
The ratio that a certain band energy is accounted for to gross energy is as index:
Wherein, t express time, f represents frequency, P represents power, thus P
f(t) represent the time dependent function of energy within the scope of a certain frequency f, the denominator part of formula represents the energy accumulation summation arriving in 35Hz frequency range 1; According to above model by P
f(t) divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, uses P
f '(t) represent.
Beneficial effect of the present invention is:
The present invention is based on the patient attention index of brain electricity frequency domain character indexing algorithm while calculating tinnitus treatment, thus can be according to the objectively effect of judgement treatment of patient's brain electricity, have accuracy high, without advantages such as patient's subjective judgment.Both can be used for hospital, also can be for family.
Accompanying drawing explanation
Fig. 1 is device structure schematic diagram of the present invention.
Fig. 2 is method flow schematic diagram of the present invention.
Wherein, 1-earphone, 2-eeg signal acquisition electrode, 3-EEG signals reference electrode, 4-IC circuit, 5-battery compartment, 6-on and off switch, 7-terminal unit.
The specific embodiment
As shown in Figure 1, it is device structure schematic diagram of the present invention, comprise earphone, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, eeg signal acquisition device comprises eeg signal acquisition electrode and EEG signals reference electrode, the simulation brain wave that described harvester produces people's cerebration is sampled, is quantized, become discrete digital signal, carry out follow-up processing; These two electrodes are integrated mutually with earphone, and in order to gather more accurately EEG signals, eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
IC circuit comprises signal amplifier and signal processor, and described signal amplifier is signal preamplifier, the digital signal of quantification is amplified to the capacity of resisting disturbance in enhancement process and transmitting procedure; IC circuit is also mounted in the body of earphone.Signal processor carries out noise suppression preprocessing to the signal gathering, and strengthens the intensity of eeg signal, and therefrom extracts the parameter that reflection people cognitive state changes, the state of assessment user; Signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit.
Terminal unit is a PC, above-mentioned parameter is processed, and shown and feed back.In general, between terminal unit and IC circuit, adopt transmission of wireless signals.
Earphone is headband receiver, and eeg signal acquisition device and IC are circuit integrated in earphone body, and is further integrated with battery compartment and on and off switch.
A kind of tinnitus treatment method based on brain electricity frequency domain character indexing algorithm of the present invention, described method step is as follows:
(1) initialization apparatus hardware, setting can acceptable conditions;
(2) sound source automatically-broadcasting voice gathers eeg data simultaneously, generally gathers 10 seconds;
(3) to the data filtering gathering, denoising, time frequency analysis, calculating brain electricity index;
(4) the brain electricity index of calculating is transferred to client and shows;
(5) judge whether to meet acceptable conditions; If met, set to the sound of playing and can accept labelling; If do not met, again carry out correlation step to step (2).
Wherein, indexing algorithm is specific as follows:
(1) pretreatment: the brain wave quantizing is carried out to digital filtering, remove the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter; The low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
(2) feature representation and extraction: extract the basic index of the comprehensive cognitive state of reflection the EEG signals after pretreatment, specifically comprise alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz); Use time-frequency analysis technology that these indexs are extracted from original time-domain signal, the time series form with energy or power on frequency domain is expressed;
(3) indexing represents: above-mentioned basic index is carried out to standardization, make the same index of different users and same user different time have identical physical meaning; Described algorithm output Vigilance level and two indexs of tensity level, the horizontal index of described Vigilance and specific as follows with the horizontal index of tensity:
A (t), b (t) and c (t) represent respectively the clock signal of alpha, beta and tri-frequency ranges of theta, and they are realized by the energy accumulation via time frequency analysis selected special frequency channel of original EEG signals respectively;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represent respectively the energy of alpha and theta;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represent respectively the energy of beta and theta;
(4) judgement of attention level: with normal user in the situation that not having sleepy, fatigue state to occur, the horizontal index of Vigilance that continues to keep to note 2 minutes and and the sequential average of two index series of the horizontal index of tensity 60% as decision threshold, be tired generation lower than this threshold judgement, show that the sound now producing by earphone well restrains tinnitus, show that lower than threshold value the sound that earphone produces is undesirable to tinnitus inhibition.
Wherein, the specific algorithm of feature representation submodule is as follows:
Adopting Morlat function is mother wavelet function, and brain electricity time-domain signal is carried out to continuous wavelet transform; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, the multiple time series signal that obtains a series of different frequency ranges with above-mentioned mother wavelet function convolution and after conversion is wavelet coefficient, wherein time and input signal length are consistent, frequency range is to being 1-35Hz, retains wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, represents with power; According to band limits height, extract respectively alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz) time series of mould of multiple wavelet coefficient of corresponding frequency band, power represents the timing variations of band energy.
Wherein, described step (3) indexing represents to adopt feature normalization algorithm, that is:
The ratio that a certain band energy is accounted for to gross energy is as index:
Wherein, t express time, f represents frequency, P represents power, thus P
f(t) represent the time dependent function of energy within the scope of a certain frequency f, the denominator part of formula represents the energy accumulation summation arriving in 35Hz frequency range 1; According to above model by P
f(t) divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, uses P
f '(t) represent.
the collaborative level index of brain represents:
On the basis of above two basic indexs, inherent physiology, psychological pattern that we represent according to the different rhythm and pace of moving things again, the brain that proposition utilizes the synchronicity between rhythm and pace of moving things signal to carry out concentrated expression user is worked in coordination with state, thereby whether the state of comprehensive representation cerebral activity is applicable to work.
Index calculation process is as follows:
From primary signal, extract respectively alpha and the theta ripple of 8-13Hz and 4-7Hz band limits, with a (t) and c (t) expression, wherein t express time.
Brain electricity range signal a (t) and b (t) are carried out respectively to Hilbert conversion, obtain its phase signals, phi a (t) and φ b (t), represent the time dependent situation of signal phase;
Calculate the synchronicity index S between alpha and theta energy time sequence, represent in a period of time (with representing), two phase difference values that frequency band signals is overall, i.e. the quality of synchronicity, for weighing the degree of full brain participation sustained attention level, synchronicity is better, the cognitive resources that more can transfer full brain maintains higher attention level, can guarantee Vigilance, the factors such as customer service fatigue, improve the working ability stimulating to external world, thereby keep good duty.
The computation model of index S is as follows:
Wherein, S represents to intend the synchronicity index of calculating, wherein represents selected a period of time length, and signal progressively calculates the length according to this time period from primary signal, and e represents natural constant, and its value is about 2.71828; T represents a certain moment in section seclected time; φ (t) represents the phase information of the rhythm and pace of moving things; The difference of the phase place in a period of time is added up and can calculate overall phase synchronism, represent to guarantee that with natural logrithm form index is between 0 and 1.
In this patent, seclected time, segment length was 1s, and every 1s exports above index S once, to follow the tracks of in real time the variation of attention index, was transferred to terminal and was pointed out.
Flow chart can be undertaken by following flow process:
Pretreatment-> feature representation and extraction-> feature normalization-> characteristic index represent
Alpha
Theta
Beta
The first two is synthesized A: Alertness;
Latter two synthetic B: tensity;
First and the 3rd degree of depth are synthesized C: concertedness index;
Then point to thresholding differentiation and index output.
Claims (9)
1. the tinnitus treatment equipment based on brain electricity frequency domain character indexing algorithm, described equipment comprises earphone, eeg signal acquisition device, IC circuit, signal transmitting apparatus and terminal unit, it is characterized in that:
Eeg signal acquisition device comprises eeg signal acquisition electrode, EEG signals reference electrode and signal processor, and the simulation brain wave that described harvester produces people's cerebration is sampled, quantized, and becomes discrete digital signal, carries out follow-up processing;
Described IC circuit comprises signal amplifier and signal processor, and described signal amplifier, for for signal preamplifier, amplifies the digital signal of quantification the capacity of resisting disturbance in enhancement process and transmitting procedure;
Described signal processor carries out noise suppression preprocessing to the signal gathering, and strengthens the intensity of eeg signal, and therefrom extracts the parameter that reflection people cognitive state changes, the state of assessment user;
Described signal transmitting apparatus connects IC circuit and terminal is established, and the parameter of IC circuit extraction is transferred to terminal unit;
Described terminal unit is a PC, above-mentioned parameter is processed, and shown and feed back.
2. equipment according to claim 1, is characterized in that: described earphone is headband receiver, and eeg signal acquisition device and IC are circuit integrated in earphone body, and is further integrated with battery compartment and on and off switch.
3. equipment according to claim 1, is characterized in that: described eeg signal acquisition electrode is positioned at head, and EEG signals reference electrode clamp is positioned at ear.
4. equipment according to claim 1, is characterized in that: described signal transmitting apparatus is wireless signal transmission.
5. the tinnitus treatment method based on brain electricity frequency domain character indexing algorithm, is characterized in that, described method, and described method step is as follows:
(1) initialization apparatus hardware, setting can acceptable conditions;
(2) sound source automatically-broadcasting voice gathers eeg data simultaneously;
(3) to the data filtering gathering, denoising, time frequency analysis, calculating brain electricity index;
(4) the brain electricity index of calculating is transferred to client and shows;
(5) judge whether to meet acceptable conditions; If met, set to the sound of playing and can accept labelling; If do not met, again carry out correlation step to step (2).
6. method according to claim 5, is characterized in that, described indexing algorithm is specific as follows:
(1) pretreatment: the brain wave quantizing is carried out to digital filtering, remove the interfering noises such as myoelectricity; Described wave filter is infinite-duration impulse response (IIR) band filter;
(2) feature representation and extraction: extract the basic index of the comprehensive cognitive state of reflection the EEG signals after pretreatment, specifically comprise alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz); Use time-frequency analysis technology that these indexs are extracted from original time-domain signal, the time series form with energy or power on frequency domain is expressed;
(3) indexing represents: above-mentioned basic index is carried out to standardization, make the same index of different users and same user different time have identical physical meaning; Described algorithm output Vigilance level and two indexs of tensity level, the horizontal index of described Vigilance and specific as follows with the horizontal index of tensity:
A (t), b (t) and c (t) represent respectively the clock signal of alpha, beta and tri-frequency ranges of theta, and they are realized by the energy accumulation via time frequency analysis selected special frequency channel of original EEG signals respectively;
Alertness index:
S1 (t)=c (t)/a (t), wherein t express time, a and c represent respectively the energy of alpha and theta;
Tensity index:
S2 (t)=b (t) * c (t), wherein t express time, b and c represent respectively the energy of beta and theta;
(4) judgement of attention level: with normal user in the situation that not having sleepy, fatigue state to occur, the horizontal index of Vigilance that continues to keep to note 2 minutes and and the sequential average of two index series of the horizontal index of tensity 60% as decision threshold, be tired generation lower than this threshold judgement, show that the sound now producing by earphone well restrains tinnitus, show that lower than threshold value the sound that earphone produces is undesirable to tinnitus inhibition.
7. algorithm according to claim 6, is characterized in that: in described step (1), the low pass initial frequency of band filter is 1Hz, and high pass cut off frequency is 35Hz.
8. algorithm according to claim 5, is characterized in that:
The specific algorithm of described step (2) feature representation submodule is as follows:
Adopting Morlat function is mother wavelet function, and brain electricity time-domain signal is carried out to continuous wavelet transform; Input signal is the discrete brain electricity time series that brain wave acquisition arrives of singly leading, the multiple time series signal that obtains a series of different frequency ranges with above mother wavelet function convolution and after conversion is wavelet coefficient, wherein time and input signal length are consistent, frequency range is to being 1-35Hz, retains wherein 1-35Hz for extracting prosodic feature; For specific moment and frequency, coefficient represents the time-frequency distributions situation of signal, to its delivery, represents with power; According to band limits height, extract respectively alpha ripple (8-13Hz), beta ripple (13-20Hz), delta(1-4Hz), theta(4-7Hz) time series of mould of multiple wavelet coefficient of corresponding frequency band, power represents the timing variations of band energy.
9. algorithm according to claim 5, is characterized in that:
Described step (3) indexing represents to adopt feature normalization algorithm, that is:
The ratio that a certain band energy is accounted for to gross energy is as index:
Wherein, t express time, f represents frequency, P represents power, thus P
f(t) represent the time dependent function of energy within the scope of a certain frequency f, the denominator part of formula represents the energy accumulation summation arriving in 35Hz frequency range 1; According to above model by P
f(t) divided by after gross energy normalization, the relative energy of each frequency range becomes the numerical value within the scope of 0-1, uses P
f '(t) represent.
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| CN113261979A (en) * | 2021-07-19 | 2021-08-17 | 季华实验室 | Tinnitus identification system based on electroencephalogram signals |
| CN113456087A (en) * | 2021-08-18 | 2021-10-01 | 乔月华 | Tinnitus diagnosis and treatment system based on neurobiological feedback therapy and use method thereof |
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| CN103816007B (en) | 2016-04-06 |
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