Intelligent brain biosensor based on biological energy fusion and disease conditioning method

Abstract

The invention discloses an intelligent brain biosensor based on biological energy fusion and a disease conditioning method, and relates to the cross technical field of biomedical technology and intelligent sensing technology, the intelligent brain biosensor comprises a biological energy sensing module, a signal analysis and regulation module, a biological energy wave generation module and a feedback monitoring module, all the modules cooperate to form a closed-loop conditioning system, the bio-energy sensing module is of a non-invasive structure, comprises a flexible electroencephalogram electrode array and a multi-channel bio-electricity collection chip and is used for collecting brain waves and body surface bio-electricity signals, the human body bio-electricity state can be monitored in real time, and bio-energy wave parameters are dynamically optimized; personalized and safe disease conditioning is achieved, the method is suitable for auxiliary conditioning of brain related functional diseases such as sleep disorder, mild cognitive dysfunction and chronic headache, and a new technical path is provided for non-drug disease intervention.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of biomedical technology and intelligent sensing technology, specifically to an intelligent brain biosensor based on bioenergy fusion and a disease treatment method. Background Technology

[0002] With increasing social pressure and a rapidly aging population, the incidence of brain-related functional disorders such as sleep disorders, mild cognitive impairment, and chronic headaches is rising year by year. Currently, treatment methods for these conditions mainly fall into two categories: one is drug therapy, such as sedatives, hypnotics, and analgesics, which can provide short-term symptom relief, but long-term use can easily lead to drug dependence, liver and kidney damage, and other side effects; the other is interventional therapy, such as deep brain stimulation, which requires surgical implantation of electrodes, carries risks of infection and bleeding, and is applicable to a limited population.

[0003] The human body possesses a stable bioelectric system. Brain waves generated by the activity of brain neurons, electrocardiogram (ECG) signals generated by the heartbeat, and electromyographic (EMG) signals generated by muscle contraction collectively constitute the body's bioelectric network. Its balance directly affects the body's physiological functions. When the bioelectric network is imbalanced, it can easily lead to various functional disorders. For example, patients with sleep disorders often exhibit an abnormal proportion of slow waves (delta waves) in their brain waves, and patients with chronic headaches show disordered bioelectric activity in local brain regions.

[0004] While some existing bioelectric monitoring devices (such as electroencephalogram monitors) can be used for disease diagnosis, there is a lack of non-drug, non-invasive technologies that can actively regulate bioelectric balance to achieve disease management. Therefore, developing an intelligent sensing system that can accurately generate bioenergy waves, integrate with human bioelectricity, and regulate its balance has become a key requirement to address the shortcomings of existing management methods. Summary of the Invention

[0005] The purpose of this invention is to provide a disease treatment method based on a drug-free medical bioenergy fusion intelligent brain biosensor. In a non-drug, non-invasive manner, it regulates the bioelectrical balance through the precise fusion of bioenergy waves and human bioelectricity, thereby achieving safe and effective treatment of brain-related functional diseases.

[0006] The technical solution of the present invention to achieve the above objectives is an intelligent brain biosensor based on bioenergy fusion, comprising a bioenergy sensing module, a signal analysis and regulation module, a bioenergy wave generation module and a feedback monitoring module, wherein each module works together to form a closed-loop conditioning system. The bioenergy sensing module is a non-invasive structure, including a flexible EEG electrode array and a multi-channel bioelectric acquisition chip, used to collect brain waves and surface bioelectric signals. The brain waves include alpha waves, beta waves, delta waves and theta waves, with a collection frequency of 256-1024Hz and a signal resolution ≤0.1μV. The signal analysis and regulation module is equipped with an AI algorithm to analyze bioelectric signals, identify abnormal features, and generate personalized bioenergy wave regulation instructions based on a preset bioelectric balance database. The bioenergy wave generation module includes a high-frequency electromagnetic induction coil and a low-frequency pulse generation circuit, used to generate bioenergy waves with a frequency of 0.5-30Hz and an intensity of 5-50μT. The feedback monitoring module and the bioenergy sensing module share the acquisition components, which are used to collect bioelectric signals after the action of bioenergy waves in real time and transmit them to the signal analysis and regulation module to dynamically adjust the parameters.

[0007] Preferably, the flexible EEG electrode array is made of Ag / AgCl material, contains at least 8 acquisition channels, and the contact area covers the forehead, parietal lobe, temporal lobe and occipital lobe of the human body. It can also be attached to key acupoints on the body surface, including Baihui, Fengchi and Sicong acupoints.

[0008] Preferably, the multi-channel bioelectric acquisition chip is model ADS1299 with an input impedance ≥100MΩ, the signal analysis and control module is equipped with an ARM Cortex-A72 processor with a running memory ≥4GB and a storage memory ≥64GB, and the AI ​​algorithm is deployed through the TensorFlowLite framework.

[0009] Preferably, the high-frequency electromagnetic induction coil has a diameter of 4-6mm and a number of turns of 80-120, and the low-frequency pulse generation circuit outputs a voltage of 0-5V, with the intensity of the bio-energy wave precisely controlled by an impedance matching circuit.

[0010] Preferably, the feedback monitoring module has a 50Hz power frequency filtering function and a 1000x signal amplification function, and the data transmission delay is ≤100ms to ensure real-time control.

[0011] Preferably, the bioelectric balance database contains normal bioelectric parameter ranges for brain-related functional disorders, including sleep disorders, mild cognitive impairment, and chronic headaches, and is constructed based on ≥100,000 ethically approved bioelectric samples.

[0012] A disease management method based on a smart brain biosensor using bioenergy fusion includes the following steps: S1. Preprocessing stage: The user wears the intelligent brain biosensor, so that the bioenergy sensing module fits the head and key positions on the body surface. After system initialization, 30-60 minutes of basic bioelectric signals are collected and uploaded to the signal analysis and regulation module to establish the user's personal bioelectric baseline file. S2, Anomaly Identification Stage: The signal analysis and regulation module analyzes the basic bioelectric signals using AI algorithms, compares them with normal parameters in the bioelectric balance database, identifies the state of bioelectric imbalance and the corresponding disease type, and outputs the disease risk assessment results. S3, Energy Wave Generation and Action Stage: The signal analysis and regulation module generates personalized parameter instructions based on the anomaly identification results. The bio-energy wave generation module generates bio-energy waves of specific frequency and intensity according to the instructions. These waves are then applied to the human body through the flexible conduction layer, achieving the fusion of bio-energy waves and human bioelectricity and regulating bioelectric imbalance. S4. Dynamic Feedback Phase: The feedback monitoring module collects the bioelectrical signal after the treatment every 5-10 minutes and transmits it to the signal analysis and regulation module. If the bioelectrical parameters do not reach the normal range, the parameters are adjusted within the range of frequency ±0.2Hz and intensity ±5μT. If the parameters are normal, the current parameters are maintained until the end of the single treatment. The single treatment time is 20-40 minutes. S5. Periodic Assessment Phase: After each 7-day conditioning cycle, the system generates a report on changes in bioelectric parameters to assess the conditioning effect. If the effect is significant, the plan is maintained; if the effect is poor, the bioenergy wave parameters are re-optimized until the bioelectric balance is stable.

[0013] Preferably, the AI ​​algorithm is a fusion model of convolutional neural network and long short-term memory network, with an accuracy of ≥92% in identifying abnormal bioelectric features.

[0014] Preferably, the diseases include sleep disorders, mild cognitive dysfunction, and chronic headaches, with a treatment cycle of 7 days / cycle and 1-2 treatments per day; when treating sleep disorders, the initial bioenergy wave frequency is set to 1-2Hz and the intensity is set to 15-25μT to increase the proportion of delta waves in the brain waves.

[0015] Preferably, the criteria for judging the effectiveness in step S5 include a reduction in sleep onset time of ≥30% in patients with sleep disorders.

[0016] The beneficial effects of this invention are: 1. High safety: The entire process uses non-drug and non-invasive methods, and the intensity of the bio-energy wave is controlled within the safe electromagnetic standard range for the human body. There are no drug side effects or surgical risks, making it suitable for long-term conditioning.

[0017] 2. Highly Personalized: Based on AI algorithms, the system analyzes the user's individual bioelectric baseline and dynamically adjusts bioenergy wave parameters, avoiding a "one-size-fits-all" approach to treatment and improving the targeted nature of the treatment.

[0018] 3. Real-time controllability: A closed-loop control is formed through the feedback monitoring module, which can correct the conditioning parameters in real time, ensuring the accuracy of bioelectric regulation and improving conditioning efficiency.

[0019] 4. Wide range of applications: It can be used as an adjunct treatment for various brain-related functional diseases such as sleep disorders, mild cognitive impairment, and chronic headaches, without the need to change equipment for different diseases, thus reducing the cost of use. Detailed Implementation

[0020] The present invention will be further described below with reference to the embodiments.

[0021] Example: This invention provides a disease management method based on a smart brain biosensor using bioenergy fusion. The smart brain biosensor system upon which this method relies includes the following modules: Bioenergy sensing module: Composed of a flexible EEG electrode array and a multi-channel bioelectric acquisition chip, it non-invasively fits the head and key acupoints on the body surface (such as Baihui, Fengchi, Si Cong, etc.) to collect brain waves (alpha, beta, delta, theta waves) and bioelectric signals from the body surface in real time. The acquisition frequency is 256-1024Hz, and the signal resolution is ≤0.1μV, ensuring high-fidelity acquisition of bioelectric signals.

[0022] Signal Analysis and Regulation Module: Equipped with AI algorithms (such as a fusion model of convolutional neural network and long short-term memory network), it performs real-time analysis on the bioelectric signals collected by the bioenergy sensing module, identifies abnormal features in the signals (such as the proportion of delta waves in patients with sleep disorders being lower than the normal threshold, and abnormal enhancement of beta waves in local brain regions in patients with chronic headaches), and generates personalized bioenergy wave regulation instructions based on the preset "bioelectric balance database" (containing normal bioelectric parameter ranges for different disease types).

[0023] Bioenergy wave generation module: Based on the instructions of the signal analysis and regulation module, it generates bioenergy waves of specific frequency (0.5-30Hz, covering the main frequency band of brain waves) and intensity (5-50μT, in line with human safety electromagnetic standards) through a high-frequency electromagnetic induction coil and a low-frequency pulse generation circuit. The bioenergy waves are then applied to the human head and body surface through a flexible conductive layer.

[0024] - Feedback Monitoring Module: Collects human bioelectric signals after the action of bioenergy waves in real time, transmits them to the signal analysis and regulation module, compares them with the bioelectric parameters before the treatment, and dynamically adjusts the frequency, intensity and duration of the bioenergy waves to form a closed-loop treatment process of "monitoring-analysis-regulation-feedback".

[0025] The specific steps of the treatment method applied for are as follows: 1. Preprocessing stage: The user wears the intelligent brain biosensor system, and the bioenergy sensing module is attached to key positions on the head and body surface. After system initialization, the system collects the user's basic bioelectric signals and uploads them to the signal analysis and regulation module to establish the user's personal bioelectric baseline file.

[0026] 2. Anomaly Identification Stage: The AI ​​algorithm analyzes the basic bioelectrical signals and compares them with the "Bioelectrical Balance Database" to determine whether there is a bioelectrical imbalance and the corresponding disease type (such as sleep disorders, mild cognitive impairment), and outputs the disease risk assessment results.

[0027] 3. Energy Wave Generation and Effect Stage: Based on the anomaly identification results, the signal analysis and regulation module generates personalized bio-energy wave parameter instructions. The bio-energy wave generation module generates specific bio-energy waves according to the instructions, which are then applied to the human body through the flexible conduction layer, allowing the bio-energy waves to merge with the human body's bioelectricity and regulate the imbalance of bioelectricity.

[0028] 4. Dynamic Feedback Phase: The feedback monitoring module collects bioelectrical signals every 5-10 minutes after the treatment and transmits them to the signal analysis and regulation module. If the bioelectrical parameters still do not reach the normal range, the frequency (e.g., in sleep disorder treatment, if the proportion of delta waves does not increase, the frequency of the bioenergy waves is slightly adjusted from 1Hz to 2Hz) or the intensity (adjusted within ±5μT). If the bioelectrical parameters return to normal, the current parameters are maintained until the end of the treatment cycle (each treatment session lasts 20-40 minutes, 1-2 times daily).

[0029] 5. Periodic assessment phase: After each 7-day treatment cycle, the system generates a report on changes in bioelectrical parameters to assess the treatment effect. If the effect is significant (e.g., a reduction of ≥30% in the time it takes for patients with sleep disorders to fall asleep), the current treatment plan is maintained; if the effect is not good, the bioenergy wave parameters are re-optimized until the bioelectrical balance is stable.

[0030] System hardware parameters of this application Bioenergy sensing module: The flexible EEG electrode array uses silver-silver chloride (Ag / AgCl) material and has 8 graphene channels. The contact areas include the forehead (Fp1, Fp2), parietal lobe (P3, P4), temporal lobe (T3, T4) and occipital lobe (O1, O2). The bioelectric acquisition chip is model ADS1299, with a sampling rate of 512Hz and an input impedance ≥100MΩ, ensuring accurate acquisition of weak EEG signals.

[0031] Signal analysis and control module: equipped with an ARM Cortex-A72 processor, 4GB of RAM and 64GB of storage; the AI ​​algorithm is deployed through the TensorFlow Lite framework, and the model training dataset contains 100,000 bioelectrical samples of different disease types (approved by ethics review), with an anomaly identification accuracy of ≥92%.

[0032] Bio-energy wave generation module: The high-frequency electromagnetic induction coil has a diameter of 5mm and 100 turns, which can generate a continuously adjustable frequency of 0.5-30Hz; the low-frequency pulse generation circuit outputs a voltage of 0-5V, and the bio-energy wave intensity is controlled at 5-50μT through an impedance matching circuit, which complies with the "Electromagnetic Environment Control Limits" (GB 8702-2014) standard.

[0033] Feedback monitoring module: Shares an electrode array with the bioenergy sensing module, collects bioelectric signals in real time and performs filtering (50Hz power frequency filtering) and amplification (1000 times gain) processing, with a data transmission delay of ≤100ms.

[0034] Case study of sleep disorder treatment: Fifty patients (aged 25-55 years, 25 men and 25 women) with primary insomnia who met the diagnostic criteria of the International Classification of Sleep Disorders, 3rd Revision (ICSD-3) were selected and treated with the method of this invention for 4 weeks. The specific process is as follows: 1. Pretreatment: Patients wore sensors to collect 30 minutes of basic EEG signals. AI algorithms identified that all patients had bioelectrical abnormalities with a delta wave (0.5-4Hz) ratio of less than 15% (normal adult sleep delta wave ratio ≥20%).

[0035] 2. Treatment plan: The initial parameters of the bio-energy wave are set to a frequency of 1.5Hz and an intensity of 20μT. The treatment is performed once a day, 1 hour before bedtime, for 30 minutes each time.

[0036] 3. Dynamic feedback: During the treatment, the feedback monitoring module collects EEG signals every 8 minutes. If the proportion of delta waves increases to 18%-20%, the current parameters are maintained; if the proportion of delta waves is still <18%, the frequency is increased by 0.2Hz or the intensity is increased by 5μT.

[0037] 4. Effect evaluation: After 4 weeks, the time to fall asleep in 42 patients (84%) was shortened from 65±15 minutes before treatment to 30±10 minutes, and the proportion of delta waves remained stable at 20%-25%; the time to fall asleep in 8 patients (16%) was shortened to 45±12 minutes after parameter adjustment due to differences in bioelectric baseline. The overall effective rate was 100%, and there were no adverse reactions.

[0038] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An intelligent brain biosensor based on bioenergy fusion, characterized in that, It comprises a bioenergy sensing module, a signal analysis and regulation module, a bioenergy wave generating module, and a feedback monitoring module, which cooperatively form a closed-loop conditioning system. The bioenergy sensing module is of a non-invasive structure, comprising a flexible electroencephalogram electrode array and a multi-channel bioelectricity acquisition chip, for collecting brain electroencephalogram waves and body surface bioelectricity signals, wherein the brain electroencephalogram waves include α waves, β waves, δ waves, and θ waves, the acquisition frequency is 256-1024 Hz, and the signal resolution is ≤0.1 μV. The signal analysis and regulation module is loaded with an AI algorithm, for analyzing bioelectricity signals, identifying abnormal features, and generating individualized bioenergy wave regulation instructions based on a preset bioelectricity balance database. The bioenergy wave generating module comprises a high-frequency electromagnetic induction coil and a low-frequency pulse generating circuit, for generating bioenergy waves with a frequency of 0.5-30 Hz and an intensity of 5-50 μT. The feedback monitoring module shares the acquisition components with the bioenergy sensing module, for collecting bioelectricity signals after the action of bioenergy waves in real time, and transmitting them to the signal analysis and regulation module for dynamic adjustment of parameters.

2. The intelligent brain biosensor based on bio-energy fusion according to claim 1, characterized in that, The flexible electroencephalogram electrode array is made of Ag / AgCl material, contains at least 8 acquisition channels, and is attached to the forehead, parietal lobe, temporal lobe, occipital lobe, and key acupoints on the body surface.

3. The intelligent brain biosensor based on bioenergy fusion according to claim 1, characterized in that, The multi-channel bioelectricity acquisition chip is of the model ADS1299, with an input impedance ≥100 MΩ, the signal analysis and regulation module is loaded with an ARMCortex-A72 processor, with a running memory ≥4 GB and a storage memory ≥64 GB, and the AI algorithm is deployed through a TensorFlowLite framework.

4. The intelligent brain biosensor based on bioenergy fusion according to claim 1, characterized in that, The high-frequency electromagnetic induction coil has a diameter of 4-6 mm and a number of turns of 80-120, and the low-frequency pulse generating circuit outputs a voltage of 0-5 V, which is accurately controlled through an impedance matching circuit to control the intensity of bioenergy waves.

5. The intelligent brain biosensor based on bioenergy fusion according to claim 1, characterized in that, The feedback monitoring module has a 50 Hz power frequency filtering function and a 1000 times signal amplification function, with a data transmission delay ≤100 ms, ensuring real-time regulation.

6. The intelligent brain biosensor based on bioenergy fusion according to claim 1, characterized in that, The bioelectricity balance database contains the normal bioelectricity parameter range of brain-related functional diseases, including sleep disorders, mild cognitive dysfunction, and chronic headache, and is constructed based on ≥100,000 bioelectricity samples approved by an ethics review.

7. A disease management method based on the bioenergy fusion intelligent brain biosensor according to any one of claims 1-6, characterized in that, The method comprises the following steps: S1, a pretreatment stage: the user wears a smart brain biosensor, the bioenergy sensing module is attached to the head and key positions on the body surface, the system collects 30-60 minutes of basic bioelectricity signals after initialization, and uploads them to the signal analysis and regulation module to establish a user's personal bioelectricity baseline profile; S2, an abnormality identification stage: the signal analysis and regulation module analyzes the basic bioelectricity signals through an AI algorithm, compares them with the normal parameters in the bioelectricity balance database, identifies the bioelectricity imbalance state and corresponding disease types, and outputs a disease risk assessment result; S3, energy wave generation and action stage: the signal analysis and regulation module generates individualized parameter instructions according to the abnormal identification results, the bioenergy wave generation module generates bioenergy waves of specific frequency and intensity according to the instructions, and the bioenergy waves act on the human body through the flexible conductive layer to realize the fusion of bioenergy waves and biological electricity and adjust the biological electricity imbalance; S4, dynamic feedback stage: the feedback monitoring module collects the biological electricity signal after the action every 5-10 minutes, and transmits it to the signal analysis and regulation module. If the biological electricity parameter does not reach the normal range, the parameter is adjusted within the frequency ±0.2 Hz and the intensity ±5 μT. If the parameter is normal, the current parameter is maintained until the single conditioning is completed. The single conditioning time is 20-40 minutes. S5, periodic evaluation stage: after completing a 7-day conditioning cycle, the system generates a biological electricity parameter change report to evaluate the conditioning effect. If the effect is significant, the scheme is maintained. If the effect is not good, the bioenergy wave parameters are optimized again until the biological electricity balance state is stable.

8. The disease management method based on bio-energy fusion intelligent brain biosensor according to claim 7, characterized in that, The AI algorithm is a fusion model of convolutional neural network and long short-term memory network, and the recognition accuracy of biological electricity abnormal features is ≥92%.

9. The disease management method based on bio-energy fusion intelligent brain biosensor according to claim 7, characterized in that, The diseases include sleep disorders, mild cognitive impairment, and chronic headache. The conditioning cycle is 7 days / cycle, and the daily conditioning is 1-2 times. For sleep disorder conditioning, the initial bioenergy wave frequency is set to 1-2 Hz, and the intensity is set to 15-25 μT to improve the proportion of δ waves in the brain waves.

10. The disease management method based on bio-energy fusion intelligent brain biosensor according to claim 7, characterized in that, The evaluation criteria for significant effect in step S5 include that the sleep disorder patient's sleep time is shortened by ≥30%.