CN111477299B - Method and device for regulating and controlling sound-electricity stimulation nerves by combining electroencephalogram detection and analysis control - Google Patents

Method and device for regulating and controlling sound-electricity stimulation nerves by combining electroencephalogram detection and analysis control Download PDF

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CN111477299B
CN111477299B CN202010270606.2A CN202010270606A CN111477299B CN 111477299 B CN111477299 B CN 111477299B CN 202010270606 A CN202010270606 A CN 202010270606A CN 111477299 B CN111477299 B CN 111477299B
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electroencephalogram
brain
stimulation
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CN111477299A (en
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赵勇
赵金萍
徐鹏
陈晓禾
张孝文
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Guangzhou Abrun Medical Technology Co ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • 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
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/36025—External stimulators, e.g. with patch electrodes for treating a mental or cerebral condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • 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
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/3603—Control systems
    • A61N1/36031—Control systems using physiological parameters for adjustment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0027—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the hearing sense
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2230/00—Measuring parameters of the user
    • A61M2230/08—Other bio-electrical signals
    • A61M2230/10—Electroencephalographic signals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Abstract

The invention discloses a noninvasive sound and electricity stimulation nerve regulation and control method and device combined with electroencephalogram objective detection analysis feedback control, and the method comprises the following steps: the system comprises an electroencephalogram detection and signal processing and analyzing system, an intelligent diagnosis and treatment control system, an acoustic stimulation treatment system, a transcranial electric pulse stimulation treatment system and a cognitive behavior psychotherapy system, wherein the electroencephalogram detection of a plurality of brain functional areas is carried out on a human body before, during and after noninvasive acoustic stimulation and/or electric pulse stimulation and/or cognitive psychotherapy, and a closed loop of detection, analysis and feedback treatment regulation and control is realized through electroencephalogram signal acquisition, real-time electroencephalogram space-time modeling, objective analysis of characteristic parameters of a neural network and establishment of a correlation model with curative effect evaluation. The method and the device disclosed by the invention can be used for establishing a tracking objective evaluation and prediction method of the multi-course or multi-time nerve regulation and control treatment effect, qualitatively and quantitatively regulating and optimizing a method for implementing a subsequent treatment scheme and selecting scheme parameters according to an expected curative effect, and objectively evaluating and predicting the nerve function state.

Description

Method and device for regulating and controlling sound-electricity stimulation nerves by combining electroencephalogram detection and analysis control
Technical Field
The invention relates to the technical field of medical treatment, in particular to a noninvasive acoustic-electric stimulation neural regulation method (non-invasive acoustic-electric stimulation neural regulation therapy) and a device which are combined with electroencephalogram (EEG) objective detection analysis feedback control.
Background
Cranial nerve dysfunction is a change and/or abnormal pattern of changes in the cranial nervous system that is diseased and unable to function properly, resulting in a decrease and/or an increase in neural activity and/or cross-reactivity between brain regions. Neurological disorders (neurological disorders or dysfuntions) are the third largest chronic disease of the elderly after cancer, cardiovascular and cerebrovascular diseases, involving a number of objective neuroelectrophysiological (neuroelectrophysiology) states of the physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and awareness and/or sensory motor brain functional areas, the association and cross-effects of subjective psycho-perception and subjective psycho-activity, and the compensatory and decompensative processes shifts of neurological function, accompanied by psycho-cognitive (cognitive) disorders, which are the traditional and clinically problematic conditions, with corresponding symptoms and signs of neurological damage, including but not limited to tinnitus, deafness, sleep disorders, anxiety, depression, dizziness, ear blockage, neuropathic headache, mental fatigue, alzheimer's disease (dementia), parkinson's disease, and the like. About 80% of elderly people aged 65 and older suffer from at least one neurological disorder. Neuromodulation therapy is directed delivery of stimuli to specific neural sites of the body, actively stimulating the target nerve to produce a natural biological response or controlling physiological levels of neurotransmitters to alter neural activity.
Tinnitus is the result of different pathological abnormalities and changes of many diseases which involve or do not involve the auditory system, has complex etiology and unclear mechanism, is mainly characterized by no corresponding external sound source or electric stimulation, subjectively has sound sensation in ears or intracranial, appears in single ear or double ears, and is mostly continuous or discontinuous, and has varied melody or tone and tone color compound tone or noise with the volume generally not exceeding 20 decibels of hearing threshold. Tinnitus can cause a patient to have an emotional or negative response of restlessness, anxiety, tension, fear, or depression, while an adverse emotional state can exacerbate tinnitus, resulting in a vicious circle between tinnitus and the adverse mood, with psychological factors playing an important role in the onset and development of tinnitus. At present, specific drugs are lacking in the treatment of tinnitus, and acoustic stimulation therapy (acoustic stimulation therapy), transcranial electric pulse stimulation therapy (transcranial electric pulse stimulation therapy) and cognitive behavioral psychotherapy (cognitive behavioral psychotherapy) are gradually adopted clinically:
1. the application of sound therapy is wide, such as various tinnitus therapeutic instruments, and patent application No. 201810011553.5 discloses a method for fitting a hearing aid, which sends an acoustic stimulation signal to a subject through a biological acoustic stimulation device and obtains FPT data through the response of the subject; importing the FPT data into a computer, and correcting the FPT data for the FPT data through an acoustic testing module running in the computer based on a preset verification algorithm; after the bioacoustic stimulation device is connected with a computer, the corrected FPT data is sent to the bioacoustic stimulation device, so that an acoustic stimulation signal sent to a subject is adjusted to be in a comfortable state by a DSP chip in the bioacoustic stimulation device, and the treatment effect is evaluated by the subjective feeling of the subject;
2. the utility model discloses an electro-acoustic stimulator for treating tinnitus, which is used for electrical pulse stimulation treatment, and has the application number of 201420311215.0, the utility model discloses an electro-acoustic stimulator for treating tinnitus, the computer is used for storing the electrical pulse parameters for treating tinnitus, electrical pulses with different waveforms can be generated according to the mode selected by a patient, electrical stimulation is output through an invasive electro-acupuncture device, the tinnitus is treated, and the treatment effect is evaluated through the subjective feeling of the patient;
3. cognitive behavioral psychotherapy, tinnitus generally causes a subjective sensation different from that of the natural voice, and the patient produces an unpleasant psychological response to the appearance of tinnitus such as an incomprehension, doubt or fear. Tinnitus is accompanied with cognitive psychological problems and is a good indication of cognitive behavioral psychotherapy to tinnitus treatment, and the cognitive behavioral psychotherapy adopts the modes of relaxation, thinking of cognitive structure adjustment, correction of maladaptive behaviors and the like, so that tinnitus patients can achieve the aim of psychotherapy. In the patent application No. 201910861100.6, a tinnitus deafness detection testing and treatment system based on a shared cloud computing platform is disclosed, comprising: the system comprises an online cloud computing platform and a plurality of offline intelligent terminals, wherein the cloud computing platform comprises a cloud server and a hearing aid fitting programmer module connected with the cloud server, and an expert or intelligent robot remote technology support, operation and supervision module, a big data module, a hearing test module, a fitting programming software module, a tinnitus physiological acoustic detection and personalized acoustic treatment scheme manufacturing module and a tinnitus acoustic treatment and cognitive behavior psychotherapy scheme library module are arranged in the cloud server. The tinnitus sound treatment and cognitive behavior psychotherapy scheme library module stores an individualized exclusive tinnitus sound treatment scheme, a shared adjustable universal tinnitus sound treatment scheme and a cognitive behavior psychotherapy scheme, the individualized tinnitus sound treatment scheme and the cognitive behavior psychotherapy scheme are formulated according to results of the hearing test module and the tinnitus physiological acoustic detection module, tinnitus treatment is carried out, and the treatment effect is still evaluated through subjective feelings of patients.
In fact, due to the lack of targeted drugs and the problem of drug safety, cranial nerve physiotherapy or non-invasive neuromodulation therapy is currently an important safe and effective physical therapy for treating neurological dysfunction, and the physical factors used include, but are not limited to, sound, light, electricity, magnetism, nuclear rays, heat, cold, and the like. However, the detection and treatment evaluation of the existing neurological dysfunction are subjective, and a diagnosis, treatment and evaluation technology based on objective detection and neural regulation is needed in the market, so that an integrated system diagnosis and treatment means of objective physiology, subjective psychology and subjective soul is strengthened, the diagnosis and treatment process and treatment effect evaluation are objective, quantitative, visual, standardized and intelligent, qualitative and quantitative adjustment of diagnosis and treatment schemes and scheme parameters is guided to achieve the expected diagnosis and treatment effect, the treatment effect is objectively and quantitatively evaluated, the development trend of multiple treatment courses is predicted, the confidence of doctors and patients is enhanced, and the compliance of clinical services is improved.
Disclosure of Invention
The invention mainly solves the technical problem of providing the sound-electricity stimulation nerve regulation and control method and device combined with electroencephalogram detection analysis feedback control, improving the objectivity, quantitative, visual, standardized and intelligent diagnosis and treatment process and treatment effect evaluation, feeding back, guiding, qualitatively and quantitatively adjusting and optimizing diagnosis and treatment schemes and scheme parameters to achieve expected diagnosis and treatment effects, and achieving objective and quantitative evaluation of treatment effects and prediction of multi-course development trend.
In order to solve the technical problems, the invention adopts a technical scheme that: the non-invasive nerve regulation and control method combining electroencephalogram (EEG) objective detection analysis feedback control is provided, and comprises the following steps:
performing cortical brain electrical signal (cortix) detection and signal acquisition on a plurality of brain functional areas corresponding to physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensory movement on a human body before, during and after noninvasive acoustic stimulation (acoustic electrical pulse stimulation) and/or transcranial electrical pulse stimulation (transcranial electrical pulse stimulation) and/or cognitive behavioral psychotherapy (cognitive behavor psychotherapy);
through electroencephalogram signal decoding analysis feedback, acquiring electroencephalogram characteristic parameters (EEG eigenvalue) which are associated with treatment effects and treatment times, acoustoelectric stimulation treatment parameters and/or cognitive behavioral psychotherapy methods, wherein the electroencephalogram characteristic parameters comprise an electroencephalogram Power Spectrum (PSD), electroencephalogram neural network space topology (neural network) and related network statistical attribute parameters (network statistics attribute parameters) and evaluation indexes;
the method comprises the steps of feeding back and guiding to make a tracking evaluation and prediction method of multi-course or multi-time neural regulation treatment effect through objective analysis of electroencephalogram characteristic parameters, qualitatively and quantitatively regulating and optimizing a subsequent treatment scheme implementation and scheme parameter selection method according to an expected treatment effect, establishing an associated model of electroencephalogram characteristic parameters and neural function states according to objective analysis feedback of physical examination or screening or monitoring of electroencephalogram characteristic parameters, qualitatively and quantitatively evaluating and predicting the neural function states, and carrying out early warning;
and auxiliary verification is carried out by means of numerical simulation modeling and analysis of a neural network computer, functional nuclear magnetic imaging fMRI, animal experiments and the like.
In a preferred embodiment of the invention, the onset of the neurological dysfunction patient involves the physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensory-motor interaction among a plurality of brain functional areas, and after the intervention of acoustic stimulation and/or transcranial electric pulse stimulation and/or cognitive psychotherapy and the like, the related brain electrical signals change, and the brain neural network is remodeled (remodelling of brain neural network). Therefore, by collecting the electroencephalogram signals corresponding to a plurality of brain functional cortex, extracting corresponding electroencephalogram space-time and network characteristics, and performing decoding analysis, the disease of the patient with the neurological dysfunction can be objectively, qualitatively and quantitatively evaluated and predicted. In the project, the brain electrical signals can be obtained by arranging dry or wet brain electrode arrays with the total number of not less than 2 leads in the main brain functional area, and the electrode signal transmission and amplifier signal acquisition parameters are as follows: the sampling bit number is not less than 10bits, the input impedance should reach G omega level, the equivalent minimum input noise should not be more than 10 muV, the data transmission rate is not less than 1Mbps, and the signal amplitude range is not more than 200 muV.
In a preferred embodiment of the present invention, component information related to the neural function regulation and control is obtained from the originally recorded electroencephalogram signals, task-related components are described by means of feature vectors (eigenvectors), a machine learning algorithm (machine learning algorithm) is used to classify the feature vectors related to different tasks, decoding of different brain region activity states from the electroencephalogram signals is achieved, and electroencephalogram components closely related to clinical indicators of the neural dysfunction of a patient (such as tinnitus disability table, sleep table, anxiety table, depression table, and the like) are obtained. The decoding precision depends on how much the features extracted by the feature algorithm represent related tasks and how accurately the classification algorithm can distinguish the categories of different tasks, and the electroencephalogram decoding and analyzing method comprises but is not limited to the following steps:
A. preprocessing an electroencephalogram signal: obtaining high-quality electroencephalogram signals, particularly signals including but not limited to delta, theta, alpha, beta and gamma bands, by means of signal filtering screening, denoising discarding, eye movement and black artifacts (eye movement and muscle artifacts) and electromyography (electromyography) artifact removal, principal component analysis and signal reconstruction;
B. extraction of electroencephalogram signal features: various feature information of the electroencephalogram signals is extracted to the maximum extent through analysis including but not limited to electroencephalogram network reconstruction, multi-dimensional discrete wavelet transform (multi-dimensional discrete wavelet transform) and the like, and feature analysis is carried out on the electroencephalogram signals, so that more, more accurate, more comprehensive and more comprehensive information is provided for diagnosis and treatment evaluation;
C. recognizing the characteristics of the electroencephalogram signals and classifying the characteristic parameters: the method adopts characteristics including but not limited to brain neural network topology, electroencephalogram space-time information (including amplitude, energy and the like), and the like, adopts a deep neuron network, a long-time network, a short-time network, a support vector machine (support vector machine), a neural network and the like to perform characteristic identification and classification, and obtains electroencephalogram characteristic parameters including but not limited to: the electroencephalogram characteristic parameters with good sensitivity and relevance to the regulation and control of the neurological dysfunction are obtained through electroencephalogram detection and signal decoding analysis of the same state and the brain areas after longitudinal contrast for multiple times, so that the neural activity change of the brain areas and the correlation change of different brain areas are observed and understood more intuitively and quantificationally, and the treatment effect and the neural abnormal activity correlation effect among the brain areas are evaluated.
D. Obtaining a neural dysfunction patient, a healthy person and the same patient through electroencephalogram signal decoding analysis feedback, wherein the decoding analysis results of electroencephalogram signals of delta wave bands and/or theta wave bands and/or alpha wave bands and/or beta wave bands and/or gamma wave bands obtained before and/or during and/or after each treatment, and obtaining a power spectrum average PSD and/or network statistical attributes (Clu-clustering coefficient, L-characteristic path length, ge-global efficiency, le-local efficiency) and/or neural network topological attributes or network correlation Coh and variation values thereof in the same brain area before and after multiple treatments, and/or after the treatment and the correction of the electroencephalogram signals can be used as treatment effect evaluation indexes;
E. comparing the PSD values of the power spectrums of the patients and healthy people and the characteristic values thereof in the relevant frequency or all frequency ranges, evaluating the relevance between the change amount of the PSD of the power spectrum of the same patient and the increase of treatment times or the improvement of treatment effect, and reflecting the abnormal activity of the nerves in the brain area by one or more frequencies corresponding to the peaks or the troughs of the power spectrum; the larger the power spectrum change amount is, the more obvious the corresponding abnormal activity change of the nerves in the brain area is, namely, the more obvious the treatment effect of the neurological dysfunction is;
F. the change quantity of the network attributes Clu, ge, le and L of the same patient is related to the increase of the treatment times or the treatment effect, the more obvious the network attribute difference is, the larger the improvement of the symptom of the nerve dysfunction is, the network attribute difference has short connection between an auditory area and a sensory area and long connection between the auditory area and an emotional area, the difference mode of the abnormal response is related to tinnitus and nerve fatigue, attention weakness, sensory disturbance, anxiety and depression caused by the nerve dysfunction, the acoustic stimulation treatment, electric pulse treatment and cognitive behavior psychotherapy can be organically related to systematically evaluating the treatment effect through the analysis of the nerve network attribute, and the network space topology difference between brain areas is reflected by the network-related Coh difference, namely the strength difference of the related activity degrees can help to evaluate the effects of different treatment schemes and the effects after multiple treatments;
G. relative to the analysis of power spectrum, the network coherence and network attributes reflect the overall difference between the same brain area and/or different brain areas, and can reflect the abnormal activity change quantity of the brain area, namely the connection strength between physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensory-motor brain functional areas, further reveals that the generation of tinnitus neurological dysfunction is a comprehensive pathogenesis of physiological and cognitive behavioral-psychological interaction, provides relatively reliable evaluation standard parameters for treatment, and further guides the writing of qualitative and quantitative algorithms for tracking, evaluating and predicting treatment effects, according to the PSD change quantity of power spectrum, the correlation between PSD corresponding to multiple peaks or troughs of power spectrum and frequency, the difference of network coherence Coh and the change quantity of network attributes Clu, ge, le and L, the comprehensive system evaluates the curative effect difference, multi-course treatment effect and progress of the acoustic stimulation, transcranial electric pulse stimulation and cognitive behavior psychology which are respectively or jointly implemented, or sets a treatment target and an evaluation standard, predicts the number of required treatment courses or treatment times, regulates and optimizes the parameters of subsequent treatment plans according to the expected treatment effect and uses a method of mixing one or more treatment plans, slightly simplifies the method combining with electroencephalogram objective detection analysis feedback, can be used for physical examination and/or screening and/or prediction of the neurological dysfunction, makes early warning of the neurological dysfunction according to the difference or change of the neurological dysfunction relative to the health state, asks for medical treatment and/or takes positive preventive measures;
H. through clinical treatment of a large sample amount of patients, a large database is established, deep data mining is carried out, artificial intelligent AI analysis is carried out, and a more general curative effect evaluation method and indexes related to the neural dysfunction characteristic classification are obtained;
I. and performing auxiliary verification on the neural activity of the brain functional region before and after the sound and electricity stimulation neural regulation and control treatment, the change of the neural activity, the curative effect evaluation and the like by means of numerical simulation modeling and analysis of a neural network computer and/or functional nuclear magnetic imaging (fMRI) and/or animal experiments and the like.
In a preferred embodiment of the present invention, the method is applied to qualitatively and quantitatively guiding and optimizing diagnosis and/or physical examination and/or screening and/or monitoring and/or predicting the diagnosis and/or diagnosis of neurological dysfunction, wherein the neurological dysfunction is the abnormal change and/or change rule of change of brain nervous system, which leads to the reduction and/or enhancement of brain regional nerve activity and/or brain regional cross action, and the corresponding symptoms and signs of nerve damage in clinic, including but not limited to tinnitus, deafness, sleep disorder, anxiety, depression, vertigo, ear blockage, nervous headache, mental fatigue, epilepsy, alzheimer's disease and Parkinson's disease.
In order to solve the technical problem, the invention adopts another technical scheme that: an acoustoelectric stimulation neural regulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control is provided, which includes but is not limited to: the brain electricity detection and signal processing and analyzing system comprises but is not limited to a brain electricity electrode array module, a brain electricity amplifier module, a brain electricity signal analyzing module, a brain electricity equipment control module, a display module and a power supply module, the intelligent diagnosis and treatment control system comprises but is not limited to an acoustic signal following brain electricity signal change control module, an electric pulse signal following brain electricity signal change control module and a cognitive psychology treatment scheme following brain electricity signal change control module, the intelligent diagnosis and treatment control system shares or independently treats feedback qualitative and quantitative analysis intelligent brain electricity algorithm embedded software through each control module, the method comprises the steps of manually or automatically adjusting and optimizing parameters of each corresponding treatment scheme and/or a using method of the treatment scheme, wherein the acoustic stimulation treatment system comprises but is not limited to a physiological acoustic detection (psychoacoustic) module, an acoustic stimulation treatment scheme manufacturing module, an acoustic stimulation treatment module and an acoustic stimulation treatment display screen module, the transcranial electric pulse stimulation treatment system comprises but is not limited to an electric pulse signal generator module and an electric pulse treatment electrode array module, the cognitive behavior psychotherapy system comprises but is not limited to an inquiry scale module, a cognitive behavior psychotherapy module and a cognitive behavior psychotherapy display screen module, the sound electric stimulation nerve regulation and control treatment device combined with electroencephalogram detection analysis feedback control adopts an integrated device or a separated device or a wearable device, and each system module is independent or shares a display screen and/or a power supply.
In a preferred embodiment of the invention, the wearable device is simplified, including but not limited to wearable electroencephalogram detection and analysis system, cognitive behavior psychotherapy system, electroencephalogram analysis feedback-based remote medical system and a smart phone or tablet computer connected with the system in a wired or wireless manner, the electroencephalogram analysis feedback and evaluation are written into APP to be implanted into the smart phone or tablet computer, namely the APP can be used for neurological dysfunction physical examination and/or screening and/or monitoring and/or prediction, early warning is made, medical inquiry is made and/or positive preventive measures are taken; the cognitive behavior psychotherapy system is implanted into a smart phone or a handheld panel through an APP and used for guiding a wearable device user to select and implement a cognitive behavior psychotherapy training and/or education and/or consultation and/or therapy scheme preset in the APP, and meanwhile, the APP is also used for connecting the wearable device user with a remote expert through a wireless network, providing remote control and/or professional consultation service and carrying out bidirectional communication.
In a preferred embodiment of the present invention, a composite acoustic modulation scheme (composite acoustic modulation therapy) of amplitude or phase angle or mixture of amplitude and phase angles is formulated by an acoustic stimulation therapy system for bioacoustic testing of the unilateral or bilateral ears, including but not limited to hearing, sleep, anxiety, depression, concentration tests or questionnaires, and tinnitus sound matching, plus background natural sounds; for non-otogenic neurological dysfunction, the method mainly guides to select input parameters and formulate a composite acoustic regulation and control scheme according to the results of electroencephalogram detection and analysis; the acoustic regulation and control scheme is characterized in that corresponding electric signals in an acoustic stimulation treatment scheme setting module are converted into sound wave signals through an earphone in the acoustic stimulation treatment module and input into an ear canal, the eardrum and a cochlea are vibrated, hair cells in the cochlea are driven to fluctuate to generate electric stimulation signals, the electric stimulation signals enter a brain central system and connected brain functional areas such as a thalamus, a hippocampus and the like through an auditory pathway to drive or activate electrical excitability of neurons, further amplified electric signals desynchronize disordered signals of nerve functions, a brain nerve network is remodeled, and nerves of all brain functional areas are enabled to recover to a normal working state; the adjustable parameters of the acoustic stimulation treatment system comprise but are not limited to hearing threshold, frequency, wave amplitude, phase angle, wave crest sharpening, wave trough filling, wave delay, gap knocking, background noise and/or natural sound, the treatment effect is objectively detected and evaluated according to electroencephalogram, software is embedded through a qualitative and quantitative algorithm of an intelligent diagnosis and treatment control system, acoustic stimulation treatment scheme selection and treatment scheme parameters are manually or automatically adjusted and optimized, the purposes of enhancing the treatment effect and accelerating rehabilitation are achieved, or the required treatment times or treatment process number is predicted according to the expected treatment effect.
In a preferred embodiment of the invention, the electric pulse signal generator module of the transcranial electric pulse stimulation treatment system sends out electric pulse signals, and the electric pulse signals are released to stimulate the cortex or the nervous system by the transcranial electric pulse through the electric pulse treatment electrode array module arranged on the body surface of the related brain functional region, so as to achieve the purpose of relieving the symptom of the nervous dysfunction; electrical stimulation therapy regimen parameters include, but are not limited to, pulse frequency, pulse waveform, pulse amplitude, pulse width, pulse delay, firing sequence, duration, rest interval, number of repetitions; according to the brain electricity objective detection and the treatment effect evaluation, software is embedded through an intelligent diagnosis and treatment control system qualitative and quantitative algorithm, the parameters of an electrical stimulation treatment scheme are manually or automatically adjusted and optimized, and an amplitude modulation and/or frequency modulation transcranial electrical stimulation scheme is formulated, so that the purposes of enhancing the treatment effect and accelerating the rehabilitation are achieved, or the required treatment times or treatment course number is predicted according to the expected treatment effect.
In a preferred embodiment of the invention, the cognitive behavioral psychotherapy system makes a preliminary cognitive behavioral psychology consultation and/or education and/or training plan through a cognitive behavioral psychology inquiry scale, and the preliminary cognitive behavioral psychology consultation and/or education and/or training plan can be independently carried out or carried out in cooperation with sound-electricity stimulation therapy; according to the brain electricity objective detection and evaluation treatment effect, the cognitive behavior psychotherapy scheme is adjusted and optimized manually or automatically through the embedded software of the qualitative and quantitative algorithm of the intelligent diagnosis and treatment control system, and the cognitive behavior psychotherapy scheme comprises but is not limited to expert consultation, and consultation and/or training and/or education are carried out through reading data and/or audio data and/or video data compiled by experts and stored in the cognitive behavior psychotherapy module, so that the treatment effect is enhanced, the rehabilitation is accelerated, and the required treatment times or treatment course number is predicted according to the expected treatment effect.
The invention has the beneficial effects that: the invention provides a sound-electricity stimulation nerve regulation and control method and a device combining with electroencephalogram detection analysis feedback control, which can make a tracking objective evaluation and prediction method of multi-course or multi-time treatment effect by acquiring electroencephalogram signals, decoding and analyzing the electroencephalogram signals and analyzing electroencephalogram characteristic parameters of a human body in different periods of acoustic stimulation and/or electric pulse stimulation and/or cognitive behavior psychotherapy, and qualitatively and quantitatively regulate and optimize the implementation of a subsequent treatment scheme and the selection of scheme parameters according to the expected treatment effect; the nerve functional state can be qualitatively and quantitatively evaluated and predicted objectively in the physical examination and/or screening and/or monitoring of the nerve functional disorder, early warning is carried out, and the confidence of doctors and patients and the treatment effect are enhanced.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a schematic structural diagram of a preferred embodiment of an acoustoelectric stimulation neural regulation diagnosis and treatment device combining electroencephalogram detection, analysis and feedback control;
FIG. 2 is a schematic diagram of the configuration of the electroencephalogram detection and signal processing and analyzing system of FIG. 1;
fig. 3 is a schematic structural diagram of the intelligent diagnosis and treatment control system in fig. 1;
FIG. 4 is a schematic diagram of the configuration of the acoustic stimulation treatment system of FIG. 1;
fig. 5 is a schematic diagram of the structure of the transcranial electrical pulse stimulation treatment system of fig. 1;
FIG. 6 is a schematic diagram of the cognitive behavioral psychotherapy system of FIG. 1;
FIG. 7 is a schematic diagram of the division of EEG electrode positions corresponding to brain regions during electroencephalogram signal acquisition;
FIG. 8 is a schematic diagram of a brain electrical signal analysis technology route or brain electrical signal decoding path, in which CNN-convolutional neural network, db 6-six-dimensional discrete wavelet;
FIG. 9 is a schematic diagram of a pre-processing technique for electroencephalogram signals;
FIG. 10 is a schematic diagram of a big data intelligent detection technology route or a brain electrical signal decoding path of feature recognition and classification, in the diagram, CNN-convolutional neural network, LSTM-long and short term memory, RBM-substraction method, SVM-support vector machine, KNN-K nearest neighbor algorithm, and GPU-graph processing unit;
FIG. 11 is a graph of the mean power spectrum PSD difference between a healthy population (lower curve) and a tinnitus patient population (upper curve);
FIG. 12 is a graph of the difference in network topological coherence (Coh) after multiple acoustic stimulation treatments, pre and post acoustic stimulation, respectively, with significantly weaker functional network connectivity in the frontal, left temporal, and parietal regions than treatment 1;
FIG. 13 is a correlation diagram of the Pre network attribute change before acoustic stimulation and the treatment effect, wherein the network attributes Clu, ge and Le decrease with the increase of the treatment times, the change quantity of the network attributes Clu, ge and Le is in positive marginal correlation with the treatment effect (the tinnitus induced residual THI score decreases on the vertical axis, i.e. the tinnitus is relieved, the lower the better the change quantity, the L change quantity increases with the increase of the treatment times and is in negative marginal correlation with the treatment effect);
fig. 14 is a diagram of brain electrical detection waveforms.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 14, an embodiment of the present invention includes:
the sound and electricity stimulation nerve regulation diagnosis and treatment device combined with electroencephalogram detection analysis feedback control shown in figure 1 is formed, and comprises the following components: the system comprises an electroencephalogram detection and signal processing and analyzing system 2, an intelligent diagnosis and treatment control system 1, an acoustic stimulation treatment system 4, a transcranial electric pulse stimulation treatment system 3 and a cognitive behavior psychotherapy system 5, and forms a closed loop of treatment-detection analysis-treatment control-treatment loop iteration. The acoustic stimulation nerve regulation and control signal of the acoustic stimulation treatment system 4 stimulates the cochlea through the auditory canal to generate an electrical stimulation signal, and then enters the brain center and each connected brain functional area through an auditory nerve passage to carry out deep brain nerve regulation and control treatment. The transcranial electric pulse stimulation treatment system 3 releases electric pulses through an electrode array which is placed on the scalp of a relevant brain functional area, and the electric pulses pass through a skull stimulation cortex and a nervous system to implement shallow cranial nerve regulation treatment. The cognitive behavioral psychotherapy system 5 performs cognitive behavioral psychotherapy by visual reading or video, or by listening to counseling or audio through the ear. The electroencephalogram detection and signal processing and analyzing system 2 collects electroencephalogram signals through an electroencephalogram electrode array placed on the scalp, and performs signal analysis and feedback. The intelligent diagnosis and treatment control system 1 is connected with the electroencephalogram detection and signal processing and analysis system 2 to obtain electroencephalogram analysis result feedback, the control and command of the acoustic stimulation treatment system 4, the transcranial electric pulse stimulation treatment system 3 and the cognitive behavior psychotherapy system 5 which are connected with the intelligent diagnosis and treatment control system are respectively or uniformly realized according to embedded algorithm software, the selective or predicted personalized acoustic stimulation, transcranial electric pulse stimulation or cognitive behavior psychotherapy of a patient is guided, the diagnosis and treatment effect is objectively evaluated along with electroencephalogram detection, signal acquisition and analysis, and a doctor or the patient is guided to carry out the next treatment.
In this embodiment, the sound-electricity stimulation neural regulation diagnosis and treatment device combining with the electroencephalogram detection analysis feedback control can adopt an integrated device or a separate device or a wearable device, and each system module is independent or shares a display screen and/or a power supply, so that the size can be reduced, and the cost can be reduced.
Along with the popularity of intelligent digital products and home medical treatment, in order to further reduce the volume and improve the portability, the wearable device can be simplified into. The electroencephalogram analysis feedback and evaluation are written into an APP to be implanted into a smart phone or a handheld tablet, so that the device can be used for neurological dysfunction physical examination and/or screening and/or monitoring and/or prediction, early warning is made, medical inquiry is sought and positive preventive measures are taken, and the device can enjoy mobile and/or remote medical services at home or anytime and anywhere.
In this embodiment, cognitive action psychotherapy system 5 implants the smart mobile phone or handheld flat board through the APP, through interface guide, guide the user to select and implement the cognitive action psychotherapy and/or education and/or consultation and/or treatment of presetting in the APP, simultaneously, the APP can also be connected wearable user and remote expert through wireless network, provide remote control and/or professional consultation service, carry out two-way communication, realize online medical treatment, be favorable to overcoming the inconvenience of patient on-the-spot medical treatment, fear, the mimicry scheduling problem, and need not worry the cross infection problem, along with the development of network technology, this kind of remote medical treatment technique will obtain further popularization.
The constitution and the specific condition of symptoms of different patients are different, so that diagnosis and treatment by certain stimulation cannot be limited, and trial, feedback and optimization are required. Therefore, the electroencephalogram detection and signal processing and analyzing system 2, the acoustic stimulation treatment system 4, the transcranial electric pulse stimulation treatment system 3 and the cognitive behavioral psychotherapy system 5 can not work without the coordination control of the intelligent diagnosis and treatment control system 1.
Therefore, the intelligent diagnosis and treatment control system 1 shown in fig. 3 is constructed, and comprises an acoustic signal following electroencephalogram signal change control module 11, an electric pulse signal following electroencephalogram signal change control module 12 and a cognitive psychotherapy scheme following electroencephalogram signal change control module 13. The intelligent diagnosis and treatment control system 1 utilizes all modules to communicate with an electroencephalogram detection and signal processing and analyzing system 2, an acoustic stimulation treatment system 4, a transcranial electric pulse stimulation treatment system 3 and a cognitive behavior psychotherapy system 5, feeds back and guides the implementation of a preferred treatment scheme and the selection of scheme parameters, implements predictable stimulation regulation and control treatment on a patient, obtains an electroencephalogram signal after treatment, analyzes and processes the signal to obtain a new stimulation treatment scheme, and repeats the steps in such a way, so that the curative effect is continuously improved, and the treatment is accelerated.
In fact, the patient's condition, both physiological and psychological, varies, and therefore, the required treatment parameters or protocols are also randomly varied. In this embodiment, the intelligent electroencephalogram algorithm can be embedded with software through the shared or independent treatment feedback qualitative and quantitative analysis of each control module, the adjustment and optimization of each corresponding treatment scheme parameter and/or treatment scheme using method can be manually or automatically realized, the response to acoustic stimulation, transcranial electric pulse stimulation or cognitive behavior psychotherapy is different due to different physical constitutions and symptoms of different patients, the most suitable stimulation mode or stimulation modes can be guided and determined by trying the acoustic stimulation, transcranial electric pulse stimulation or cognitive behavior psychotherapy according to electroencephalogram signal analysis feedback, the parameters of the stimulation mode or stimulation modes can be corrected in real time according to the curative effect, and the treatment scheme can be continuously optimized until the treatment scheme is cured.
In order for the intelligent diagnosis and treatment control system 1 to accurately make objective diagnosis and treatment effect, the electroencephalogram detection and signal processing and analysis system 2 shown in fig. 2 needs to be constructed, and comprises an electroencephalogram electrode array module 23, an electroencephalogram amplifier module 25, an electroencephalogram signal analysis module 26, an electroencephalogram device control module 22, a display module 24 and a power supply module 21, so as to perform electroencephalogram detection, signal processing and analysis on a patient. Obviously, different from subjective curative effect evaluation, electroencephalogram detection is helpful for quantifying effects and objectively evaluating curative effects, so that subsequent treatment parameters can be more accurately adjusted, and the recovery process can be expressed by percentage or a curve graph instead of fuzzy subjective evaluation or diagnosis and treatment such as 'better' and 'less or more dosage'.
In this embodiment, the electroencephalogram electrode array module 23 includes a plurality of electroencephalogram electrodes, the plurality of electroencephalogram electrodes are disposed on the scalp corresponding to the main brain functional area of the patient, electroencephalogram signals are collected, the electroencephalogram electrode array module 23 is connected with the electroencephalogram amplifier module 25 for signal input and amplification, the power supply module 21 is connected with the electroencephalogram device control module 22 for power supply, the electroencephalogram device control module 22 is respectively connected with the electroencephalogram amplifier module 25, the electroencephalogram signal analysis module 26 and the display module 24 for electroencephalogram time-space modeling and signal analysis and display, the electroencephalogram signal analysis module 26 communicates with the intelligent diagnosis and treatment control system 1 for information transmission, the analyzed result is fed back to the intelligent diagnosis and treatment control system 1, and the optimal adjustment of the intelligent diagnosis and treatment control system 1 on subsequent diagnosis and treatment parameters is facilitated.
The application of the acoustic stimulation therapy is wide, the physiological acoustic detection of the single-side or double-side ear can be carried out through the acoustic stimulation therapy system 4, the physiological acoustic detection comprises tests or questionnaires of hearing, sleep, anxiety, depression, concentration and the like, tinnitus sound matching (including compound sound, frequency, loudness, tone, timbre and melody) and background natural sound are included, a compound acoustic regulation and control scheme of wave amplitude or phase angle or wave amplitude and phase angle mixing is formulated, the operation is simple and convenient, the stimulation therapy can be carried out when a patient wears the earphone, and the operation is very convenient.
Therefore, the acoustic stimulation treatment system 4 shown in fig. 4 is constructed, and includes a physiological acoustic detection module 41, an acoustic stimulation treatment scheme making module 42, an acoustic stimulation treatment module 43 and an acoustic stimulation treatment display screen module 44, the physiological acoustic detection module 41 is connected with the acoustic stimulation treatment scheme making module 42 and provides various parameter information required to be input for scheme making, the acoustic stimulation treatment scheme making module 42 respectively inputs the made acoustic stimulation treatment scheme into the acoustic stimulation treatment module 43 for acoustic stimulation treatment, and the acoustic stimulation treatment display screen module 44 displays the process and result of the physiological acoustic detection, the acoustic treatment scheme and the parameters in real time, qualitatively, quantitatively and visually, and under the guidance of the acoustic signal following the electroencephalogram signal change control module 11, controls the modules of the acoustic stimulation treatment system 4 to work.
For non-otogenic nerve dysfunction, an acoustic stimulation nerve regulation and control treatment scheme and parameters thereof are preselected mainly according to electroencephalogram detection and analysis feedback, and a composite acoustic regulation and control scheme and multi-course treatment are fed back and guided to be optimized and formulated subsequently through treatment-electroencephalogram detection and analysis-treatment control-treatment loop iteration, so that the curative effect is continuously improved. Specifically, in the acoustic regulation and control scheme, the corresponding electrical signals in the acoustic stimulation treatment scheme manufacturing module 42 are input into the auditory canal through the earphone in the acoustic stimulation treatment module 43, the tympanic membrane and the cochlea vibrate to drive the hair cells in the cochlea to fluctuate and generate electrical stimulation signals, the electrical stimulation signals enter the brain central system and the connected brain functional areas such as the thalamus, the hippocampus and the like through the auditory nerve channel to drive or activate the electrical excitability of the neurons and further amplify the electrical signals, the disordered signals of the nerve function are desynchronized, the neural network is reshaped, and therefore the normal working state of the nerves is recovered.
Compared with treatment means such as medicines and transcranial electric pulse stimulation, acoustic stimulation is more natural, deep brain stimulation can be more easily controlled and implemented, and in most cases, the deep brain stimulation is pleasant and not unpleasant, and through parameter adjustment and combination with electroencephalogram objective detection and evaluation, a diagnosis and treatment scheme which is easily accepted is more easily found. The adjustable parameters of the acoustic stimulation treatment system 4 include, but are not limited to, hearing threshold, frequency, amplitude, phase angle, peak sharpening, trough filling, wave delay, gap knocking, background noise and/or natural sound, etc., according to the brain electrical objective detection and evaluation treatment effect, the qualitative and quantitative algorithm is embedded into software through the intelligent diagnosis and treatment control system 1, the treatment scheme and the treatment scheme parameter selection are manually or automatically adjusted and optimized, the treatment effect can be enhanced and the rehabilitation is accelerated, the required treatment times or treatment period number can be predicted according to the expected treatment effect, and the patient can conveniently carry out self objective evaluation and scientifically arrange life, work or study outside the treatment.
The electric pulse therapy has the functions of exciting neuromuscular tissues, promoting local blood circulation and the like, and plays an important role in modern medical technology. Therefore, the transcranial electrical pulse stimulation treatment system 3 shown in fig. 5 is constructed, and comprises an electrical pulse signal generator module 31 and an electrical pulse treatment electrode array module 32, wherein the electrical pulse treatment electrode array module 32 comprises a plurality of electrical pulse treatment electrodes arranged on the cortex of the relevant brain functional area, the electrical pulse signal generator module 31 is connected with the electrical pulse treatment electrode array module 32, and under the guidance of the electrical pulse signal following the electroencephalogram signal change control module 12, the electrical pulse signal generator module 31 of the transcranial electrical pulse stimulation treatment system 3 sends out electrical pulse signals, and the electrical pulse is released by the electrical pulse treatment electrode array module 32 to stimulate the intracranial cortex nervous system, so that the purpose of relieving or treating the symptom of the neurological dysfunction is achieved.
Obviously, the parameters of the electrical stimulation treatment scheme are not constant and vary from person to person, and need to be adjusted continuously as the treatment progresses or continues, the parameters of the electrical stimulation treatment scheme include, but are not limited to, pulse frequency, pulse waveform, pulse amplitude (current and/or voltage), pulse width, pulse delay, starting sequence (used alone or in combination with acoustic stimulation treatment), duration, rest interval, repetition number, and the like.
The cognitive behavioral psychotherapy has better curative effect on partial nervous dysfunction diseases, such as anxiety, depression, obsessive compulsive disorder and other emotional and stress problems, and is even better than the drug therapy. Therefore, the cognitive behavioral psychotherapy system 5 shown in fig. 6 is constructed, and includes an inquiry scale module 52, a cognitive behavioral psychotherapy module 53 and a cognitive behavioral psychotherapy display screen module 51, the inquiry scale module 52 is connected to the cognitive behavioral psychotherapy module 53 for information input, and the inquiry scale module 52 and the cognitive behavioral psychotherapy module 53 are respectively connected to the cognitive behavioral psychotherapy display screen module 51 for information and scheme display. The cognitive behavioral psychological counseling and/or training and/or education is performed by reading data and/or audio data and/or video data compiled by experts and stored in the cognitive behavioral psychotherapy module 52. Under the guidance of the cognitive psychotherapy scheme following the electroencephalogram signal change control module 13, the cognitive behavioral psychotherapy of objective detection analysis feedback control is carried out. The existing common cognitive behavior psychotherapy usually carries out subjective evaluation on curative effect and adjustment on subsequent treatment schemes by observing the color of the words, doctors subjectively judge the treatment to occupy a leading position, and pay attention to experience to easily ignore objective change of brain functional areas of patients and individualized cognitive behavior psychology performance. In the embodiment, the objective evaluation of the treatment effect can be performed through electroencephalogram signal analysis and feedback, the adjustment of the subsequent treatment scheme is more accurate, and the improvement of the diagnosis and treatment effect is facilitated.
The steps of cognitive behavioral psychotherapy are as follows:
firstly, the cognitive behavioral psychotherapy system 5 makes a preliminary cognitive behavioral psychology consultation and/or education and/or training plan through a cognitive behavioral psychology inquiry scale, can independently perform or cooperate with sound and electricity stimulation therapy, and adjusts or integrates a therapy scheme through trial and error;
secondly, according to the brain electricity objective detection and the treatment effect evaluation, the intelligent diagnosis and treatment control system 1 is used for embedding software by a qualitative and quantitative algorithm, and the cognitive behavior psychotherapy scheme is manually or automatically adjusted and optimized, for example, expert consultations compiled and stored in the cognitive behavior psychotherapy module 53 by selecting experts, training and/or education are performed by reading image-text data and/or audio data and/or video data, and the treatment effect is enhanced and the rehabilitation is accelerated;
finally, the required treatment times or the treatment course number can be predicted according to the expected treatment effect through objective evaluation of the actual treatment effect, and the method is clear at a glance.
In an embodiment of the present invention, a noninvasive neuromodulation and/or neuromodulation screening and/or neuromodulation method combining electroencephalogram EEG objective detection analysis feedback control is provided, which may be applied to diagnosis and/or physical examination and/or screening and/or monitoring and/or predicting of neurological dysfunction, including but not limited to tinnitus, deafness, sleep disorder, anxiety, depression, vertigo, ear congestion, headache, mental fatigue, epilepsy, alzheimer's disease (dementia), and parkinson's disease, and specifically includes the following:
the scalp electroencephalogram detection of a plurality of brain functional areas is carried out before, during and after the noninvasive acoustic stimulation and/or the transcranial electric pulse stimulation and/or the cognitive psychotherapy of a human body, the electroencephalogram signal acquisition is carried out, and according to individual differences, the acoustic stimulation, the transcranial electric pulse stimulation and the cognitive psychotherapy can be subjected to single selection or combined treatment schemes and parameter selection thereof through qualitative and quantitative judgment or try.
In this embodiment, the electroencephalogram signals can be obtained by arranging a dry or wet electroencephalogram electrode array on the scalp of the main brain functional area (including but not limited to frontal lobe area, parietal area, temporal lobe area) (as shown in fig. 7), and the electrode signal transmission and amplifier signal acquisition parameters are as follows: the sampling bit number is not less than 10bits, the input impedance is required to reach a G omega level, the equivalent minimum input noise is not greater than 10 muV, the data transmission rate is not lower than 1Mbps, and the signal amplitude range is not greater than 200 muV.
The brain network can be used to describe the connections between multiple brain regions, in this embodiment, 21 brain electrical electrodes are selected to avoid the volume effect, and network coherence is one of the most common methods for measuring the interaction strength between network nodes, and is used to represent the linear relationship between two different signals in a specific frequency domain.
Figure DEST_PATH_IMAGE001
C xy (f) Is the network coherence (coherence) of the X and Y signals at frequency point f,P xy (f) Is the cross spectrum (cross spectra) of the X and Y signals,P xx (f) AndP yy (f) Is the self spectrum (self spectrum) of the corresponding signal, coherence in the mth treatment stateC xy (f) m Coherence with the nth treatment stateC xy (f) n The differences in (a) and (b) emphasize changes in neural activity or differences in cyberspace topology due to therapeutic effects.
Network attributes are used to make quantitative measurements of brain networks:
Figure DEST_PATH_IMAGE002
C ij is the network topology coherence (coherence) value between the i electrode and the j electrode; t is the total number of electrodes; d ij the path length from the i node to the j node, the Clu-clustering coefficient, the L-path length, the Ge-global efficiency and the Le-local efficiency.
Actually, the onset of disease of patients with neurological dysfunction relates to interaction among a plurality of brain functional areas and the plurality of brain functional areas, after intervention such as acoustic stimulation, transcranial electric pulse stimulation or cognitive psychotherapy, related electroencephalogram signals can change, a brain neural network is remodeled and changed, electroencephalogram signals corresponding to the cortex of the plurality of brain functional areas are collected, real-time electroencephalogram space-time modeling is carried out, corresponding electroencephalogram space-time and network characteristics are extracted, decoding analysis is carried out, subjective judgment can be abandoned, and diseases of patients with neurological dysfunction are objectively, qualitatively and quantitatively evaluated and predicted.
Through the electroencephalogram signal decoding analysis feedback, as shown in fig. 8, electroencephalogram characteristic parameters of treatment effect and treatment times and acoustoelectric stimulation treatment parameters and/or cognitive behavioral psychotherapy methods can be obtained, which is beneficial to establishing a correlation model with curative effect evaluation and realizing closed loop of detection, analysis, feedback, treatment and regulation.
Specifically, the electroencephalogram characteristic parameters comprise an electroencephalogram power spectrum, an electroencephalogram neural network space topology, related network statistical attribute parameters and evaluation indexes, the electroencephalogram characteristic parameters are mastered, and the prediction of the required treatment times according to the expected treatment effect is facilitated.
Actually, the original record of the electroencephalogram signal contains information related and unrelated to the neural function regulation, firstly, component information related to the neural function regulation needs to be obtained from the originally recorded electroencephalogram signal, task-related components can be described through feature vectors, the feature vectors related to different tasks are classified by adopting a machine learning algorithm, the decoding of activity states of different brain regions from the electroencephalogram signal is realized, electroencephalogram components closely related to clinical indexes (such as tinnitus induced disability scale, sleep scale, anxiety scale, depression scale and the like) of the patient caused by the neural dysfunction are obtained, and the irrelevant electroencephalogram components are abandoned;
the decoding precision depends on how much the features extracted by the feature algorithm represent related tasks, and how accurately the classification algorithm can distinguish the categories of different tasks, and the higher the decoding precision is, the more objective the diagnosis and treatment effect evaluation of the patient diseases is.
In this embodiment, the decoding and analyzing of the electroencephalogram signals mainly includes the following steps:
preprocessing an electroencephalogram signal: as shown in fig. 9, high-quality electroencephalogram signals, especially signals with wave bands including delta (0-4 Hz, deep sleep), theta (4-8 Hz, shallow sleep), alpha (8-13 Hz, closed eye is completely relaxed), beta (14-30 Hz, closed eye is entirely concerned with thinking) and gamma (31 Hz or above, high) are obtained by filtering, screening, denoising, discarding, removing artifacts such as electro-oculogram and myoelectricity, analyzing principal components, reconstructing signals and the like, as shown in fig. 14, when the delta wave band occurs in the deep sleep condition, the wave belonging to the 'unconsciousness level' is required for restoring physical sleep; a brain wave component which occurs in the theta band during superficial sleep and lasts for 0.125S to 0.25S; the alpha band, which sometimes appears in the brain and sometimes disappears when it occurs in relaxed or closed eye conditions, is not always present; when the beta wave band appears in human logic thinking, calculation and high concentration degree or is subjected to reasoning to solve problems, time domain, frequency domain and other computational analysis are carried out on EEG signals obtained by subtracting reference electrode signals from electrode signals in different areas, useful and root-source information can be analyzed, partial useless information is removed, and errors and processing workload are reduced;
extraction of electroencephalogram signal features: by carrying out analysis such as electroencephalogram network reconstruction and multi-dimensional discrete wavelet transformation, various characteristic information of electroencephalogram signals is extracted to the maximum extent, and characteristic analysis is carried out on the electroencephalogram signals, so that more, more accurate, more comprehensive and more comprehensive information is provided for diagnosis and treatment effect evaluation, and the condition that part of characteristic information is omitted and deviates from the objectivity and accuracy of evaluation is avoided;
recognizing the characteristics of the electroencephalogram signals and classifying the characteristic parameters: as shown in fig. 10, the brain neural network topology, brain electrical spatiotemporal information (including amplitude, energy, etc.) and the like can be used as features, a deep neuron network, a long-short time network, a support vector machine, a neural network and the like are adopted to perform feature recognition and classification, brain electrical characteristic parameters are obtained, the brain electrical characteristic parameters comprise that brain waves of the brain area where a target brain electrical electrode is located, the signal amplitude of which changes along with time, are converted into power spectrum PSD of which the brain electrical power changes along with frequency, network topological structures of the brain electrical and related network statistical values comprising Coh-network coherence, clu-clustering coefficients, L-characteristic path lengths, ge-global efficiency, le-local efficiency and the like are obtained through network analysis, brain electrical detection and signal decoding analysis of the same state and the brain area after longitudinal comparison for multiple treatments are carried out, brain electrical characteristic parameters with better sensitivity and relevance to the regulation and control of the neural dysfunction are obtained, and accordingly, the brain area neural activity change and the relevance change and the different brain area relevance change are observed and understood more intuitively and quantificationally, and the relevance model with the curative effect is established, so as to evaluate the therapeutic effect and the neural activity abnormality between multiple brain areas;
by the objective analysis of the electroencephalogram characteristic parameters, a tracking evaluation and prediction method of multi-course or multi-time treatment effects can be fed back and guided to be formulated, a method for qualitatively and quantitatively regulating and optimizing implementation and scheme parameters of a subsequent treatment scheme according to an expected treatment effect can be provided, an association model of the electroencephalogram characteristic parameters and the nerve function state can be established according to objective analysis of physical examination and/or screening and/or monitoring of the electroencephalogram characteristic parameters, the nerve function state can be qualitatively and quantitatively evaluated and predicted, and early warning can be performed.
Specifically, the neural dysfunction patient, a healthy person and the same patient are obtained through electroencephalogram signal decoding analysis feedback, the decoding analysis results of electroencephalogram signals of delta wave bands and/or theta wave bands and/or alpha wave bands and/or beta wave bands and/or gamma wave bands obtained before and/or during and/or after each treatment are obtained in the same brain area before and after a plurality of treatments, the average PSD and/or network statistical attributes (Clu-clustering coefficient, L-characteristic path length, ge-global efficiency and Le-local efficiency) and/or neural network topological attributes or network-related Coh change values are obtained in the same brain area before and after a plurality of treatments, and the obtained result is used as a treatment effect evaluation index.
In this embodiment, the power spectrum PSD values of the patient and the healthy person and the comparison between the characteristic values thereof in the relevant frequency range or the whole frequency range are shown in fig. 11, as shown in the correlation evaluation of the change amount of the power spectrum PSD of the same patient and the increase of the treatment times or the improvement of the treatment effect, and the abnormal activity of the nerves in the brain region represented by one or more frequencies corresponding to the peak or the trough of the power spectrum are larger, the change of the abnormal activity of the nerves in the corresponding brain region is more obvious, that is, the treatment effect of the neurological dysfunction is more obvious.
Experiments prove that the change amount of the network attributes Clu, ge and Le of the same patient has a correlation with the increase of the treatment times or the treatment effect, as shown in fig. 13, the more significant the network attribute difference is, the greater the improvement of the symptom of the neural dysfunction is, the short connection between the auditory region (temporal lobe) and the sensory region (top lobe) and the long connection between the auditory region (temporal lobe) and the emotional region (frontal lobe) exist, the difference mode illustrating the abnormal response may be related to tinnitus and neural fatigue caused by the neural dysfunction, attention weakness, sensory disorder, anxiety and depression, the neural network organically links the acoustic stimulation treatment (such as the temporal lobe region) with the electric pulse treatment (such as the top lobe region) and the cognitive behavior psychotherapy (such as the frontal lobe region) to evaluate the treatment effect, and the network-related Coh difference reflects the network space topological difference between brain regions, as shown in fig. 12, that the strong and weak difference of the activity degree is related to evaluate the effect of different treatment schemes and the effect after a plurality of treatments.
Actually, relative to the analysis of the power spectrum, the network coherence and the network attribute reflect the overall difference between the same brain area and/or different brain areas, and can reflect the abnormal activity change quantity of the brain area, namely the connection strength between various physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensory-motor brain functional areas, further reveals that the generation of the neurological dysfunction such as tinnitus is a comprehensive pathogenesis, provides relatively reliable standard parameters for treatment, and further guides the compiling of qualitative and quantitative algorithms for tracking, evaluating and predicting the treatment effect.
In order to ensure the objectivity of diagnosis and treatment evaluation, the method is obviously a comprehensive project, therefore, according to the PSD change amount of a power spectrum, the correlation between PSD corresponding to a plurality of peaks or troughs of power spectrum and frequency, the network coherence Coh difference and the network attribute Clu, ge, le and L change amount, a comprehensive system evaluates the single or combined implementation of the three treatment schemes of acoustic stimulation, transcranial electric pulse stimulation and cognitive behavior psychology to implement curative effect difference, multi-course treatment effect and progress, or sets a treatment target and an evaluation standard, predicts the number of required courses or treatment times, regulates and optimizes subsequent treatment parameters according to the expected treatment effect and uses a method of mixing one or more treatment schemes, simplifies the electroencephalogram detection analysis feedback method slightly, namely can be used for physical examination, screening, monitoring and prediction of neurological dysfunction, makes early warning of neurological dysfunction according to the difference or change of neurological function states relative to healthy states, and is favorable for seeking medical treatment and taking positive preventive measures in time. In this embodiment, a large database can be established through clinical treatment of a large-sample-size patient, data deep mining can be performed, artificial intelligent AI analysis can be performed, a more general curative effect evaluation method and index related to the neural dysfunction characteristic classification can be obtained, and auxiliary verification can be performed on the neural activity of the brain functional region before and after the acoustic-electric stimulation neural regulation and control treatment, the change of the neural activity, and the curative effect evaluation of the brain functional region through means such as neural network computer numerical simulation modeling and analysis, functional nuclear magnetic image fMRI, animal experiments, and the like.
The method for the objective assessment of the effect of treatment by electroencephalographic detection and analysis is further described below in accordance with the examples:
1. test control group, variable W
1. Healthy persons versus tinnitus patients;
2. the patient is the patient himself.
2. Test State, variable X
1. pre, testing electroencephalogram before treatment;
2. in, testing brain electricity in treatment;
3. post, test brain electrical after treatment.
3. The extraction and analysis of the electroencephalogram characteristic parameters S (difference change of energy and remodeling change of a neural network between brain areas) for setting the brain area (electrode node) variable Y are as follows:
1. the average power spectrum PSD, fig. 11;
2. neural network coherence Coh and average neural network topology property index, as in fig. 12 and 13, (e.g., clu-clustering coefficient; L-characteristic path length; ge-global efficiency; le-local efficiency);
4. brain wave band, variable Z, including:
1. delta band (0-4 Hz, deep sleep);
2. theta band (4-8 Hz, light sleep);
3. alpha (8-13 Hz) band (eye closure fully relaxed);
4. beta (14-30 Hz) band (eye closure with full attention);
5. gamma band (31 Hz and above, high).
5. For the same patient, under the conditions of setting a contrast group W, a test state X, a test brain area Y and an electroencephalogram wave band Z according to the treatment times n of nerve-regulated acoustic-electric stimulation, decoding (preprocessing, feature extraction, feature identification and classification) of EEG signals, calculating electroencephalogram feature parameters S n (W, X, Y, Z) and pre-treatment (n = 0) parameters S 0 Difference of (W, X, Y, Z) Δ S n (W,X,Y,Z) = S n (W,X,Y,Z) – S 0 (W,X,Y,Z)；
6. Using the difference value Delta S of the characteristic parameters of the brain electricity n (W, X, Y, Z) is the ordinate, and the treatment number n is the abscissa, to prepare a two-dimensional treatment effect curve.
Example of obtaining brain electrical judgment parameters and criteria for adjusting stimulation therapy parameters:
1. neural regulation and control acoustoelectric stimulation treatment parameter presetting and effect evaluation (unilateral or bilateral ears)
1. Creating a complex acoustic regulation scheme (complex acoustic regulation therapy of amplitude or phase angle mixture) according to Psychoacoustic Testing (including but not limited to hearing, sleep, anxiety, depression, energy concentration) and tinnitus sound matching (tinitus matching, including but not limited to complex tones, frequencies, loudness, pitch, timbre, melody), and for non-aural neurological dysfunction, selecting parameters for creating the complex acoustic regulation scheme, including but not limited to hearing threshold, frequency, amplitude, phase angle, peak sharpening, valley filling, wave delay, tap notch, background noise and/or natural sound, is guided primarily by the results of electroencephalographic Testing and analysis;
2. neuromodulation transcranial electrical pulse stimulation treatment protocols, parameters including, but not limited to, pulse amplitude and/or frequency modulation associated amplitude, frequency, phase angle, etc., pulse frequency, pulse waveform, pulse amplitude (current or voltage), pulse width, pulse delay;
3. parameters for implementing the acoustic-electric stimulation treatment scheme: start sequence (single or simultaneous start), duration, rest interval; the number of repetitions;
4. and (3) effect evaluation: number of treatments n =0 based on the pre-treatment status as a comparison
Difference value Delta S of characteristic parameters of brain electricity 1 (W, X, Y, Z), increasing or decreasing (in relation to specific characteristic parameters) in accordance with rules given in prior research efforts or clinical findings;
changes in the tinnitus condition are subjectively sensed by the patient and comprise two parts of physiology and cognition psychology, for example, changes of scores evaluated by a tinnitus disability scale THI represent changes of treatment effects; and performing auxiliary verification by means of numerical simulation modeling and analysis of a neural network computer, functional nuclear magnetic imaging (fMRI), animal experiments and the like.
The two evaluation methods are used independently or in combination;
2. subsequent neuromodulation acoustoelectric stimulation treatment parameter adjustment and effect evaluation (unilateral or bilateral ears), before n +1 treatments:
1. by comparing the current Δ S n (W, X, Y, Z) and previous DeltaS n-1 (W, X, Y, Z) trend, intelligent identification, preferably setting acoustic stimulation treatment parameters (including but not limited to hearing threshold, frequency, amplitude, phase angle) and electrical pulse stimulation treatment parameters (including but not limited to waveform, amplitude, wave width, pulse delay) to achieve desired value of S Δ S n+1 (W,X,Y,Z)；
2. The acoustic-electric stimulation treatment scheme implementation parameters are preferably as follows: including but not limited to treatment duration, rest interval, number of repetitions;
3. the two parameter adjustment steps are manually or automatically implemented by a programming algorithm;
4. using the difference value Delta S of the characteristic parameters of the brain electricity n The absolute values of (W, X, Y and Z) are vertical coordinates, the treatment times n are horizontal coordinates, a two-dimensional treatment effect curve is manufactured and displayed on a display screen, and the visualization that the tinnitus curing degree is represented by the tinnitus treatment electroencephalogram detection characteristic parameter change is realized, as shown in figure 13.
Example of acoustic electrical stimulation treatment assessment using big data and artificial intelligence analysis:
method and standard for evaluating therapeutic effect
1. By largeSample size patient clinical treatment data, establishing a large database, performing data mining, performing artificial intelligent AI analysis, and obtaining a brain electrical characteristic parameter difference value delta S n (W, X, Y, Z) expression sound and electricity stimulation treatment effect evaluation standard comprises electroencephalogram characteristic parameters expressed by mean power spectrum PSD, neural network coherence Coh and mean neural network topological attribute indexes including but not limited to Clu-clustering coefficient, L-characteristic path length, ge-global efficiency, le-local efficiency and the like under the conditions of setting contrast group W, test state X, test brain area Y and electroencephalogram wave band Z respectively or in combination
S n (W, X, Y, Z), and evaluation criteria parameters A (W, X, Y, Z), B (W, X, Y, Z) and C (W, X, Y, Z)
a. Recovery,. DELTA.S n (W,X,Y,Z) │≧A(W,X,Y,Z)
b. Remarkably effective, and Delta S is less than or equal to B (W, X, Y, Z) n (W,X,Y,Z) │<A(W,X,Y,Z)
c. Basically effective, C (W, X, Y, Z) is less than or equal to | Delta S n (W,X,Y,Z) │<B(W,X,Y,Z)
d. Basic nullification, - [ Delta ] S n (W,X,Y,Z) │<C(W,X,Y,Z)
2. Evaluation criteria parameters a (W, X, Y, Z), B (W, X, Y, Z) and C (W, X, Y, Z) were selected:
a. given S, W, X, Y, and Z
b. Obtaining A, B and C evaluation standard data of each patient according to effect of multiple patients served clinically
c. And establishing a large database and performing AI deep mining analysis to obtain A, B and C evaluation standard data which are classified relative to various neurological disorder disease characteristics under the common meaning of large sample size.
In summary, the present invention provides a sound-electricity stimulation neural regulation method and device combining with electroencephalogram detection analysis feedback control, which analyzes feedback according to an electroencephalogram objective detection result, qualitatively and quantitatively guides selection of treatment schemes and scheme parameters in order to obtain a desired diagnosis and treatment effect, realizes selection of treatment schemes such as acoustic stimulation, transcranial electrical pulse stimulation, cognitive behavior psychology and the like or optimizes and controls the parameters of the treatment schemes, designs and/or predicts treatment procedure numbers or treatment times according to treatment target settings, performs physical examination and/or screening and/or monitoring and/or predicting of neurological dysfunction, and achieves early warning of neurological dysfunction to see a doctor or timely take preventive measures according to the difference or change of the neurological status relative to a healthy status.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, or direct or indirect applications in other related fields, which are made by the contents of the present specification, are included in the scope of the present invention.

Claims (8)

1. A noninvasive neural regulation, screening and prediction method combining electroencephalogram objective detection, analysis and feedback control is characterized by comprising the following steps:
performing electroencephalogram detection on a plurality of physiological and/or cognitive psychological brain functional areas before, during and after noninvasive acoustic stimulation and/or transcranial electric pulse stimulation and/or cognitive psychotherapy on a human body, and acquiring electroencephalogram signals;
through decoding, analyzing and feeding back an electroencephalogram signal, obtaining objective electroencephalogram characteristic parameters of treatment effect and treatment times, acoustoelectric stimulation treatment parameters and a cognitive behavior psychotherapy method, wherein the electroencephalogram characteristic parameters comprise electroencephalogram power spectral density, electroencephalogram neural network space topology, related network statistical attribute parameters and evaluation indexes;
the method comprises the steps of feeding back and guiding to make a tracking evaluation and prediction method of multi-course neural regulation and control treatment effect through analysis of electroencephalogram characteristic parameters, qualitatively and quantitatively regulating and optimizing a subsequent treatment scheme implementation and scheme parameter selection method according to an expected treatment effect, establishing an associated model of electroencephalogram characteristic parameters and neural function states according to objective analysis feedback of physical examination or screening or monitoring of electroencephalogram characteristic parameters, qualitatively and quantitatively evaluating and predicting neural function states, and early warning;
the pathogenesis of the patient with the neurological dysfunction relates to the interaction between a plurality of brain functional area disorders and a plurality of brain functions, the prognosis is performed after acoustic stimulation and/or transcranial electric pulse stimulation and/or cognitive psychotherapy, related electroencephalogram signals can change and can be remodeled and changed correspondingly to a brain neural network, therefore, corresponding electroencephalogram space-time and network characteristics are extracted through the acquisition of electroencephalogram signals corresponding to a plurality of brain functional area cortex, decoding analysis is performed, the disease of the patient with the neurological dysfunction can be objectively, qualitatively and quantitatively evaluated and predicted, the electroencephalogram signals can be obtained by arranging a dry or wet electroencephalogram electrode array with the total number of not less than 2 leads in a main brain functional area, and the electrode signal transmission and amplifier signal acquisition parameters are as follows: the sampling bit number is not less than 10bits, the input impedance is required to reach a G omega level, the equivalent minimum input noise is not more than 10 muV, the data transmission rate is not lower than 1Mbps, and the signal amplitude range is not more than 200 muV;
acquiring component information related to nerve function regulation from an original recorded electroencephalogram signal, describing related components of nerve regulation tasks through feature vectors, classifying the feature vectors related to different tasks by adopting a machine learning algorithm, decoding different brain region activity states from the electroencephalogram signal, and acquiring electroencephalogram components closely related to clinical indexes of nerve dysfunction of a patient, wherein the decoding precision depends on how much degree the features extracted by the feature algorithm represent the related tasks, and the classification algorithm can accurately distinguish the categories of different tasks, and the electroencephalogram signal decoding analysis comprises the following steps:
A. preprocessing an electroencephalogram signal: obtaining high-quality electroencephalogram signals including signals of delta, theta, alpha, beta and gamma wave bands through signal filtering screening, denoising and discarding, removing electro-oculogram and electromyogram artifacts, principal component analysis and signal reconstruction;
B. extraction of electroencephalogram signal features: by carrying out electroencephalogram network reconstruction and multi-dimensional discrete wavelet transformation, various feature information of electroencephalogram signals is extracted to the maximum extent, and feature analysis is carried out on the electroencephalogram signals, so that more, more accurate, more comprehensive and more comprehensive information is provided for diagnosis and treatment evaluation;
C. recognizing the characteristics of the electroencephalogram signals and classifying the characteristic parameters: the method adopts the brain neural network topology and the electroencephalogram time-space information as the characteristics, adopts the long-time network, the short-time network, the support vector machine and the neural network to carry out characteristic identification and classification, and obtains electroencephalogram characteristic parameters, and comprises the following steps: the electroencephalogram characteristic parameters with good sensitivity and relevance to the regulation and control of the neurological dysfunction are obtained through longitudinally comparing the electroencephalogram characteristic parameters in the same state and in multiple brain areas after multiple treatments and signal decoding and analysis, so that the neural activity change of the brain areas and the correlation change of different brain areas can be observed and understood more intuitively and quantificationally, and therefore the treatment effect and the neural abnormal activity correlation effect among the multiple brain areas can be evaluated.
2. The non-invasive neural regulation, screening and prediction method in combination with electroencephalogram objective detection analysis feedback control according to claim 1,
D. obtaining neural dysfunction patients and healthy people and the same patient through electroencephalogram decoding analysis feedback, obtaining electroencephalogram decoding analysis results including delta wave bands and/or theta wave bands and/or alpha wave bands and/or beta wave bands and/or gamma wave bands obtained before and/or during and/or after each treatment, obtaining power spectral density PSD and/or network statistical attributes of each brain area and each brain area, and/or neural network topological attributes and/or change values of network coherence Coh in the same brain area before and after multiple treatments, and using the change values as treatment effect evaluation indexes, wherein the network statistical attributes include Clu, L, ge and Le;
E. comparing the PSD of the patient with the PSD of a healthy person and the characteristic value of the PSD of the power spectral density in a relevant frequency or a whole frequency range, evaluating the relevance of the change quantity of the PSD of the same patient and the increase of the treatment times or the improvement of the treatment effect, and reflecting the abnormal activity of the nerves of the brain region by one or more frequencies corresponding to the wave crest or the wave trough of the PSD of the power spectral density; the larger the change amount of the power spectral density PSD is, the more obvious the change of abnormal activity of nerves corresponding to the brain area is, namely, the more obvious the treatment effect of the neurological dysfunction is;
F. the change quantity of the network attributes Clu, ge, le and L of the same patient is related to the increase of the treatment times or the treatment effect, the more obvious the network attribute difference is, the larger the improvement of the symptom of the nerve dysfunction is, the short connection between an auditory area and a sensory area and the long connection between the auditory area and an emotional area exist in the network attribute difference, which shows that the network attribute difference can be related to tinnitus and nerve fatigue, attention weakness, sensory disturbance, anxiety and depression caused by the nerve dysfunction, through the analysis of the nerve network attribute, the treatment effect can be systematically evaluated by organically linking the acoustic stimulation treatment with transcranial electric pulse treatment and cognitive behavior psychotherapy, and the network coherence Coh difference reflects the network space topological difference between brain areas, namely the strength difference of the related activity degrees, which can help to evaluate the effects of different treatment schemes and the effects after multiple treatments;
G. relative to the analysis of the power spectral density, the network coherence and the network attribute reflect the overall difference between the same brain area and/or different brain areas, can reflect the change amount of abnormal activity of the brain area, which is the connection strength between various physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensorimotor brain functional areas, further reveals that the generation of tinnitus and neurological dysfunction is a comprehensive pathogenesis of physiological and cognitive behavioral-psychological interaction, provides relatively reliable evaluation standard parameters for treatment, further guides the writing of a qualitative and quantitative algorithm for tracking, evaluating and predicting treatment effect, and further leads to the writing of the qualitative and quantitative algorithm for tracking, evaluating and predicting treatment effect according to the PSD change amount, the correlation between the power spectral density corresponding to a plurality of peaks or troughs of the power spectral density PSD and frequency, the difference of the network coherence Coh and the change amounts of the network attributes Clu, ge, le and L, the comprehensive system evaluates the curative effect difference, multi-course treatment effect and progress of the acoustic stimulation, transcranial electric pulse stimulation and cognitive behavior psychology which are respectively or jointly implemented, or sets a treatment target and an evaluation standard, predicts the number of required treatment courses or treatment times, regulates and optimizes the parameters of subsequent treatment plans according to the expected treatment effect and uses a method of mixing one or more treatment plans, slightly simplifies the method combining with electroencephalogram objective detection analysis feedback, can be used for physical examination and/or screening and/or prediction of the neurological dysfunction, makes early warning of the neurological dysfunction according to the difference or change of the neurological dysfunction relative to the health state, asks for medical treatment and/or takes positive preventive measures;
H. through clinical treatment of a large sample amount of patients, a large database is established, deep data mining is carried out, artificial intelligent AI analysis is carried out, and a more general curative effect evaluation method and indexes related to the neural dysfunction characteristic classification are obtained;
I. the method comprises the following steps of performing auxiliary verification on the neural activity of the brain functional area before and after the sound and electricity stimulation neural regulation and control treatment, the change of the neural activity and the evaluation of the curative effect by means of neural network computer numerical simulation modeling and analysis and/or functional nuclear magnetic imaging (fMRI) and/or animal experiments.
3. The noninvasive neuromodulation, screening and prognostication method in combination with electroencephalogram objective detection analysis feedback control according to claim 1, applied to qualitatively and quantitatively guiding and optimizing diagnosis and/or physical examination and/or screening and/or monitoring and/or prognosticating neurological dysfunction, including tinnitus, deafness, sleep disorders, anxiety, depression, vertigo, deafness, ear blockage, neuropathic headache, mental fatigue, epilepsy, alzheimer's disease and parkinson's disease.
4. The utility model provides a combine the sound electricity stimulation neural regulation and control of brain electricity detection analysis feedback control to diagnose device which characterized in that includes: the brain electricity detection and signal processing and analysis system comprises a brain electricity electrode array module, a brain electricity amplifier module, a brain electricity signal analysis module, a brain electricity device control module, a display module and a power supply module, the brain electricity detection and signal processing and analysis system comprises a brain electricity electrode array module, a brain electricity amplifier module, a brain electricity signal analysis module, a brain electricity device control module, a display module and a cognitive behavior psychology treatment system, the intelligent diagnosis and treatment control system comprises an acoustic signal following brain electricity signal change control module, an electric pulse signal following brain electricity signal change control module and a cognitive behavior psychology treatment scheme following brain electricity signal change control module, the intelligent diagnosis and treatment control system shares or independently carries out treatment feedback qualitative and quantitative analysis intelligent brain electricity algorithm embedded software through the control modules, manual or automatic adjustment and optimization of parameters and/or using methods of the corresponding treatment schemes are achieved, the acoustic stimulation treatment system comprises a physiological acoustic detection module, an acoustic stimulation treatment scheme manufacturing module, an acoustic stimulation treatment module and an acoustic stimulation treatment display screen module, the transcranial electrical pulse treatment system comprises an electric pulse signal generator module and an electric pulse treatment electrode array module, the cognitive behavior treatment system comprises an inquiry treatment module, an acoustic behavior treatment behavior display screen module and a cognitive behavior display screen integrated psychology detection and psychology display device, and a mental treatment screen integrated psychology display device, and a neuro-based on the brain electricity detection and behavior display screen.
5. The sound-electricity stimulation nerve regulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control as claimed in claim 4, characterized in that the device is simplified into a wearable device, and comprises a wearable electroencephalogram detection and analysis system, a cognitive behavior psychotherapy system, a telemedicine system based on electroencephalogram analysis feedback, and a smart phone or a tablet computer connected with the remote medicine system in a wired or wireless manner, wherein the electroencephalogram analysis feedback and evaluation system is written into an APP to be implanted into the smart phone or the tablet computer, so that the APP can be used for neurological dysfunction physical examination and/or screening and/or monitoring and/or prediction, early warning is made, medical inquiry and/or positive preventive measures are taken, the cognitive behavior psychotherapy system is implanted into the smart phone or the handheld tablet computer through the APP and is used for guiding a wearable device user to select and implement a cognitive behavior psychology training and/or education and/or counseling and/or treatment scheme preset in the APP, and meanwhile, the APP is further connected with a remote expert through a wireless network, so that remote control and/or counseling services are provided, and bidirectional communication is developed.
6. The sound-electricity stimulation nerve regulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control as claimed in claim 4, wherein the acoustic stimulation treatment system is used for performing physiological acoustic detection of one or two ears, including hearing, sleep, anxiety, depression, concentration tests or questionnaires, tinnitus sound matching, background natural sound, and making a composite acoustic regulation and control scheme with amplitude or phase angle or amplitude-phase angle mixing, for non-otogenic nerve dysfunction, the composite acoustic regulation and control scheme is guided and made through electroencephalogram detection and analysis result feedback, corresponding electric signals in the acoustic stimulation treatment scheme making module are converted into sound wave signals through an earphone in the acoustic stimulation treatment module and input into an ear canal, a tympanic membrane and a cochlea are vibrated, and hair cells in the cochlea are driven to fluctuate to generate electric stimulation signals, entering physiological and/or auditory and/or psychological and/or emotional and/or memory and/or attention and consciousness and/or sensorimotor functional areas of a central system of the brain through an auditory pathway, driving or activating electrical excitability of neurons and generating amplified electrical stimulation signals, desynchronizing disordered signals of brain functional areas, reshaping a brain neural network, and restoring the nerves of all brain functional areas to a normal working state, wherein adjustable parameters of an acoustic stimulation treatment system comprise hearing threshold, frequency, amplitude, phase angle, peak sharpening, trough filling, wave delay, notch knocking, background noise and/or natural sound, manually or automatically adjusting and optimizing acoustic stimulation treatment scheme selection and treatment scheme parameters through qualitative and quantitative algorithm embedded software of an intelligent diagnosis and treatment control system according to brain electrical objective detection and evaluation treatment effects, enhance the therapeutic effect and accelerate the rehabilitation, and predict the required treatment course according to the expected therapeutic effect.
7. The sound-electricity stimulation nerve regulation and treatment device combined with electroencephalogram detection and analysis feedback control as claimed in claim 4, wherein an electric pulse signal generator module of the transcranial electric pulse stimulation treatment system sends an electric pulse signal, the electric pulse is released to stimulate an intracranial cortex nervous system through an electric pulse treatment electrode array module arranged on a cortex of a relevant brain functional area, so as to achieve the purpose of relieving symptoms of nerve dysfunction, parameters of an electric stimulation treatment scheme comprise pulse frequency, pulse waveform, pulse amplitude, pulse width, pulse delay, starting sequence, duration, rest interval and repetition times, the treatment effect is detected and evaluated according to electroencephalogram objective, manual or automatic adjustment and optimization of parameters of the electric stimulation treatment scheme are fed back and guided, a software is embedded through a qualitative and quantitative algorithm of an intelligent treatment control system, an electric pulse stimulation scheme comprising amplitude modulation and/or frequency modulation is formulated, the treatment effect is enhanced, the rehabilitation is accelerated, and the required number of treatment courses is predicted according to the expected treatment effect.
8. The sound-electricity stimulation neural regulation diagnosis and treatment device combined with electroencephalogram detection and analysis feedback control as claimed in claim 4, wherein the cognitive behavioral psychotherapy system makes a preliminary cognitive behavioral psychology consultation and/or education and/or training plan through a cognitive behavioral psychology inquiry table, can independently perform or cooperate with the sound-electricity stimulation therapy, performs treatment effect feedback according to electroencephalogram objective detection and evaluation, manually or automatically adjusts and optimizes a cognitive behavioral psychotherapy scheme through an intelligent diagnosis and treatment control system qualitative and quantitative algorithm embedded software, performs consultation and/or training and/or education through expert consultation, reads data and/or audio data and/or video data compiled by experts and stored in a cognitive behavioral psychotherapy module, enhances treatment effects and accelerates rehabilitation, and predicts the number of required treatment courses according to expected treatment effects.
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