Intelligent traditional Chinese medicine dialectical system based on autonomic nerve evaluation

Abstract

The invention discloses an intelligent traditional Chinese medicine dialectical system based on autonomic nerve evaluation, which is characterized in that a data acquisition subsystem measures a signal reflecting cardiopulmonary resonance of a tested person and acquires a traditional Chinese medicine dialectical syndrome shape and an intervention prescription of the tested person determined by a traditional Chinese medicine expert, and an expert case subsystem is used for processing the measured signal and sending the processed signal to a data processing subsystem; and according to the normalized cardiopulmonary resonance index series, physical and psychological health indexes and disease risk coefficients are calculated. The machine learning subsystem establishes a machine learning model, takes the cardiopulmonary resonance index series, the physical and psychological health indexes and the disease risk coefficient as input, takes the traditional Chinese medicine dialectical syndrome type as output, and trains the machine learning model. Moreover, the trained model is used to speculate the traditional Chinese medicine dialectical information of the testee in the absence of traditional Chinese medicine experts according to the cardiopulmonary resonance measurement signals, and an intervention prescription is given according to the traditional Chinese medicine dialectical information. According to the invention, the problem that the traditional Chinese medicine is based on subjective judgment and lacks standard and consistency is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a health assessment system and equipment, specifically an intelligent Chinese medicine dialectical system based on autonomic nerve assessment. Background Art

[0002] The autonomic nervous system, composed of the sympathetic and parasympathetic nervous systems, forms a network with the central nervous system, regulating the body and organs in a rhythmic manner, ensuring the normal functioning of metabolism, cognition, and activity. Therefore, the central autonomic nervous system is central to both human health and disease. Assessing and measuring the regulatory state of the central autonomic nervous system is key to diagnosis and treatment; improving autonomic regulation is also the goal of treatment and rehabilitation.

[0003] At rest, breathing modulates heart rate: the heartbeat speeds up during inhalation and slows down during exhalation. This phenomenon is medically known as "Respiratory Sinus Arrhythmia (RSA)." Quantitative evaluation of RSA is of great value in medical research and clinical applications. First, the amplitude of RSA represents the body's oxygen metabolism function and efficiency under autonomic nervous system regulation: synchronization of breathing and heart rate changes allows for a balanced pulmonary blood flow, enabling better oxygen absorption and delivery to organs and the body through the bloodstream. Second, RSA measures vagal tone. The vagus nerve is connected to the hypothalamus and participates in the regulation of the endocrine and immune systems, serving as an indicator of the status of these two systems.

[0004] Numerous studies have demonstrated that the modulation of heart rate by respiration is a resonance phenomenon caused by the relatively low-frequency respiratory signal within the cardiovascular and pulmonary neural control circuits. Since the natural frequency of oxygenation and delivery for the entire cardiovascular and pulmonary vascular system is approximately 0.1 Hz, the resonance peak is reached when respiration is around 6 breaths per minute. The frequency of free breathing is between 0.2 and 0.3 Hz, deviating from the circuit's natural frequency and exhibiting a smaller amplitude.

[0005] Research has also shown that while breathing modulates the heart rate, it also modulates the amplitude of electrical signals in the brain's cognitive function areas, namely the prefrontal cortex, amygdala, and hippocampus. This is the resonance of the central autonomic neural network triggered by breathing, which is closely related not only to oxygen metabolism but also to emotions, behavior, memory, and other cognitive functions.

[0006] The inventor of this application's invention patent, Chinese invention patent number ZL 202111030099.6, proposes a "cardiopulmonary respiration test and personalized deep breathing and oxygen therapy system and equipment." This system collects the subject's electrocardiogram and respiratory signals, measures the subject's blood pressure, blood oxygen, and respiratory tidal volume; and calculates respiratory system indicators (RSI), including the main respiratory rate (MRR) and respiratory stability (SRR), to assess the subject's respiratory function and performance during the cardiopulmonary respiration test. Cardiovascular system indicators (CSI) include mean heart rate (MHR), heart rate variability, mean blood pressure (MBP), and blood pressure variability (BPV). Cardiopulmonary interaction indicators (CPII) include respiratory heart rate amplitude (AHR), respiratory heart rate modulation (RCM), and respiratory heart rate correlation coefficient (CRH). The patent also proposes personalized rhythmic deep breathing and oxygen therapy prescriptions to guide, monitor, and direct patients' deep breathing training and oxygen therapy implementation plans.

[0007] Chinese invention patent ZL 202111030099.6 is the first internationally to propose a digital measurement of respiratory sinus arrhythmia (RSA), and a corresponding personalized rhythmic deep breathing prescription, guidance, and monitoring system to enhance RSA. However, sinus arrhythmia (RSA) is merely a physiological phenomenon and does not fundamentally describe the resonance of the central autonomic nervous system (CAN) induced by breathing. How to utilize this resonance of the CAN to quantitatively assess physical and mental health remains an unresolved issue in the industry. Summary of the Invention

[0008] (1) Technical issues to be solved

[0009] The technical problem addressed by this invention is that traditional Chinese medicine relies on subjective judgments based on experts' observation, listening, questioning, and palpation, lacking standardization and consistency, making it difficult to pass on knowledge. This invention also addresses the difficulty of standardizing the process, data, and indicator analysis of cardiopulmonary resonance measurement to develop a standardized scoring system for physical and mental health.

[0010] (2) Technical solution

[0011] In order to solve the above problems, the present invention proposes, on the one hand, an intelligent Chinese medicine dialectical system based on autonomic nervous system assessment, comprising a data acquisition subsystem, an expert case subsystem and a machine learning subsystem, wherein the data acquisition subsystem is used to measure a signal reflecting the cardiopulmonary resonance of a subject, obtain a measurement signal, and collect the Chinese medicine dialectical syndrome type and intervention prescription of the subject from a Chinese medicine expert; the expert case subsystem is used to store the measurement signal, the Chinese medicine dialectical syndrome type and the intervention prescription, and process and analyze the measurement signal to obtain a series of cardiopulmonary resonance indicators, physical and mental health indicators and disease risk coefficients; the machine learning subsystem is used to establish a machine learning model, using the cardiopulmonary resonance index series, physical and mental health indicators and disease risk coefficients stored in the expert case subsystem as input, and the Chinese medicine dialectical syndrome type as a calibration output, and training the machine learning model to determine the parameters of the machine learning model.

[0012] According to a preferred embodiment of the present invention, the measurement signal is an electrocardiogram and respiratory signal, or a PPG signal; and the process of measuring to obtain the measurement signal includes measuring a free breathing phase and at least two guided rhythmic breathing phases of the subject.

[0013] According to a preferred embodiment of the present invention, the expert case subsystem is used to extract the NN sequence of the electrocardiogram signal and the corresponding respiratory signal from the measurement signal, and calculate the cardiopulmonary resonance index series based on the NN sequence of the electrocardiogram signal and the corresponding respiratory signal; the cardiopulmonary resonance index series includes at least one of the following: respiratory smoothness RS, cardiopulmonary resonance amplitude CRA, cardiopulmonary resonance factor CRF, mean heart rate MHR, NN sequence standard deviation SDNN, NN difference sequence standard deviation RMSSD, NN sequence Fourier power spectrum low frequency 0.04~0.15Hz relative value LF, NN sequence Fourier power spectrum high frequency 0.15~0.4Hz relative value HF.

[0014] According to a preferred embodiment of the present invention, the physical and mental health indicators and disease risk coefficients include at least one of the following: oxygen metabolism capacity MetA, mental health status MenH, cell lifespan Clife, stress resistance StrC, heart rate recovery HRec, mental health risk Mrisk, and cardiopulmonary metabolic disease risk CPrisk.

[0015] According to a preferred embodiment of the present invention, the TCM syndrome differentiation type is at least one of the following: Yin-Yang deficiency and excess syndrome differentiation type, Eight-Principle syndrome differentiation type, Zang-Fu syndrome differentiation type, Qi-Blood-Body-Fluid syndrome differentiation type; the Yin-Yang deficiency and excess syndrome differentiation type is composed of 7 syndrome types: Yin-Yang balance, low-level Yin-Yang balance, Yin-Yang deficiency, Yang excess, Yin deficiency, Yin excess, and oxygen deficiency.

[0016] According to a preferred embodiment of the present invention, the expert case subsystem is also used to generate a subject report, which includes at least one of the following: a cardiopulmonary resonance index series, physical and mental health indicators, a disease risk coefficient, a Chinese medicine syndrome type and an intervention prescription.

[0017] According to a preferred embodiment of the present invention, the machine learning model adopts a support vector machine or a multi-layer neural network learning algorithm.

[0018] The second aspect of the present invention proposes an intelligent Chinese medicine dialectical system based on autonomic nervous system assessment, which is used for AI diagnosis, including a data acquisition subsystem, a digital case subsystem and a machine dialectical subsystem. The data acquisition subsystem is used to measure the signal reflecting the cardiopulmonary resonance of the subject to obtain a measurement signal; the digital case subsystem is used to store the measurement signal, and process and analyze the measurement signal to obtain a series of cardiopulmonary resonance indicators, physical and mental health indicators and disease risk coefficients; the machine dialectical subsystem includes a trained machine learning model, which is used to automatically generate Chinese medicine dialectical syndrome types based on the cardiopulmonary resonance indicator series, physical and mental health indicators and disease risk coefficients.

[0019] According to a preferred embodiment of the present invention, the measurement signal is an electrocardiogram and respiratory signal, or a PPG signal; and the process of measuring to obtain the measurement signal includes measuring a free breathing phase and at least two guided rhythmic breathing phases of the subject.

[0020] According to a preferred embodiment of the present invention, the digital case subsystem is used to extract the NN sequence of the electrocardiogram signal and the corresponding respiratory signal from the measurement signal, and calculate the cardiopulmonary resonance index series based on the NN sequence of the electrocardiogram signal and the corresponding respiratory signal; the cardiopulmonary resonance index series includes at least one of the following: respiratory smoothness RS, cardiopulmonary resonance amplitude CRA, cardiopulmonary resonance factor CRF, mean heart rate MHR, NN sequence standard deviation SDNN, NN difference sequence standard deviation RMSSD, NN sequence Fourier power spectrum low frequency 0.04~0.15Hz relative value LF, NN sequence Fourier power spectrum high frequency 0.15~0.4Hz relative value HF.

[0021] (3) Beneficial effects

[0022] The present invention standardizes a standardized scoring system for physical and mental health based on cardiopulmonary resonance measurement, and can provide TCM syndrome differentiation information and intervention prescriptions without TCM experts, thereby improving the efficiency and experience of TCM diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a module architecture diagram of the first embodiment of the intelligent Chinese medicine dialectical system based on autonomic nervous system assessment.

[0024] Figure 2 The figure shows one of the application examples of standardization of Yin-Yang deficiency-excess syndrome types in the first embodiment of the present invention.

[0025] Figure 3 This is a module architecture diagram of the second embodiment of the intelligent Chinese medicine dialectical system based on autonomic nervous system evaluation. DETAILED DESCRIPTION

[0026] The inventors of the present invention have noticed that Chinese medicine regards the harmony of yin and yang as health, which is completely consistent with the "homeostasis" of Western medicine. The homeostasis of Western medicine is based on the regulation of the central autonomic nervous system in anatomy, physiology and pathology. The central autonomic nervous system is the center of health and disease, and the resonance level of the central and autonomic nervous systems caused by breathing is the best representation of the physical and cognitive health of the human body. Therefore, the present invention proposes that the resonance phenomenon of the central autonomic nervous system caused by breathing can be evaluated to provide numerical indicators of physical and mental health. Moreover, the present invention further points out that the resonance phenomenon occurs synchronously in the pulmonary and cardiovascular neural regulatory circuits, as well as the prefrontal cortex, amygdala and hippocampus functional areas of the brain. Therefore, the measured resonance index can also measure the oxygen metabolism capacity, as well as the cognitive function status of the person's emotions, behavior and memory.

[0027] Furthermore, the present invention proposes to use artificial intelligence algorithms to form Chinese medicine dialectics based on measurement data and analysis algorithms.

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0029] An embodiment of the present invention provides an intelligent TCM Yin-Yang deficiency-excess syndrome differentiation system based on autonomic nervous system assessment. When in use, the system is divided into an AI training mode and an AI diagnosis mode.

[0030] Figure 1 This is a module architecture diagram of the first embodiment of the intelligent Chinese medicine dialectical system based on autonomic nerve assessment. This first embodiment is the architecture when the system is in AI training mode. Figure 1 As shown, the system includes a data collection subsystem 110 , an expert case subsystem 210 and a machine learning subsystem 310 .

[0031] 1. Data acquisition subsystem

[0032] The data acquisition subsystem 110 includes a cardiopulmonary resonance measurement module 111 and an expert diagnosis and acquisition module 112. The cardiopulmonary resonance measurement module 111 measures signals reflecting cardiopulmonary resonance, such as the subject's electrocardiogram (ECG) and respiratory signals, or photoplethysmography (PPG) signals. The expert diagnosis and acquisition module 112 collects TCM expert-derived syndrome types and intervention prescriptions for the subject.

[0033] In this embodiment, the cardiopulmonary resonance measurement module 111 is composed of an electrocardiogram and respiratory signal acquisition device and an acquisition control device. The signal acquisition device can be a wearable electrocardiogram and respiratory signal acquisition instrument, and the acquisition control device can be composed of an intelligent device with data processing and signal transmission capabilities, such as a smart phone or tablet computer equipped with a measurement and acquisition APP or applet. The electrocardiogram and respiratory signal acquisition device and the acquisition control device can communicate in any way, for example, the two can be connected via Bluetooth. The APP or applet of the smart device uploads the electrocardiogram and respiratory signals collected by the signal acquisition device to the expert case subsystem 210. The uploading method can be a wireless network or a wired network.

[0034] In a first embodiment, when the cardiopulmonary resonance measurement module 111 is measuring, the smart device APP or applet prompts the subject to wear the measuring device in the form of voice prompts. After preparing for a resting state, the electrocardiogram and respiratory signal acquisition equipment is started to collect and measure signals. Under the language prompts of the smart device APP or applet, the subject first breathes freely for 3 minutes, and then follows the APP or applet voice and music rhythm to perform rhythmic breathing of 9 times and 6 times per minute for 3 minutes each. That is, the present invention is divided into three stages when measuring cardiopulmonary resonance, namely a free breathing stage and two guided rhythmic breathing stages, each of which is performed for a period of time, such as 3 minutes or 2 minutes. If the subject cannot complete the measurement due to functional impairment, the cardiopulmonary resonance measurement is terminated. In other embodiments, more guided rhythmic breathing stages or a longer 5-minute time for each stage can also be used.

[0035] The cardiopulmonary resonance measurement module 111 uses three phases, from free breathing to low-frequency rhythmic deep breathing, to collect respiratory signals and the resulting heart rate changes. Resonance occurs around 6 beats per minute, with heart rate changes synchronized with the respiratory signal, reaching a peak. Therefore, synchronous acquisition of ECG and respiratory signals is crucial for analyzing cardiopulmonary resonance indicators.

[0036] In another embodiment, the cardiopulmonary resonance measurement module 111 may also be comprised of a PPG acquisition device. The PPG signal contains not only heartbeat information but also blood and vascular information. A PPG acquisition device, for example, is a convenient fingertip acquisition device. When a PPG acquisition device is used in place of an ECG and respiratory signal acquisition device, subsequent analysis of the cardiopulmonary resonance data utilizes the heart rate and respiratory signals extracted from the PPG signal.

[0037] The expert diagnosis collection module 112 is used to collect the TCM syndrome differentiation type and intervention prescription of the subject by TCM experts. This module can be any device with a user input and output interface and interface, for example, it can be implemented by a smart phone or tablet computer installed with a specific APP or applet. The APP or applet provides a template that defines the Yin-Yang deficiency and excess syndrome differentiation and intervention prescription. Experts can fill in the TCM syndrome differentiation type and intervention prescription according to the template by text or voice input. The expert diagnosis collection module 112 also uploads the collected TCM syndrome differentiation type and intervention prescription to the expert case subsystem 210. The TCM syndrome differentiation type is the Yin-Yang deficiency and excess syndrome differentiation type, the eight-category syndrome differentiation type, the viscera syndrome differentiation type, the qi, blood and body fluid syndrome differentiation type, etc. However, the present invention is more preferably the Yin-Yang deficiency and excess syndrome differentiation type, and uses a standardized system consisting of 7 syndrome types: Yin-Yang balance, low-level Yin-Yang balance, Yin-Yang deficiency, Yang excess, Yin deficiency, Yin excess, and Yang deficiency.

[0038] 2. Expert Case Subsystem

[0039] The expert case subsystem 210 is used to standardize the data collected by the data collection subsystem, and includes a cardiopulmonary resonance index module 211, a physical and mental health assessment module 212, a yin and yang deficiency and excess syndrome differentiation information module 213, and a report and intervention prescription module 214.

[0040] Cardiopulmonary resonance index module

[0041] The cardiopulmonary resonance index module 201 is used to process the data measured and collected by the cardiopulmonary resonance measurement module 111 to obtain a series of standardized cardiopulmonary resonance indexes. When the cardiopulmonary resonance measurement module 111 collects ECG and respiratory signals, it calls the corresponding signal processing program for the ECG and respiratory signals of the three respiratory stages uploaded by the cardiopulmonary resonance measurement module 111 to obtain the RR interval of the ECG signal, and further detects RR abnormalities such as premature beats, removes or replaces the RR abnormalities, and obtains a normal interval NN sequence. It should be noted that when processing RR abnormalities, the respiratory signal needs to be processed synchronously to maintain time synchronization between the two signals. When the cardiopulmonary resonance measurement module 111 collects PPG signals, similar processing is also performed to obtain the NN sequence and the corresponding respiratory signal. Since the processing of the above-mentioned ECG, respiratory and PPG signals to obtain the NN sequence can adopt existing technologies, it will not be described in detail here.

[0042] Next, the obtained NN sequence and respiratory signal are processed to obtain a normalized cardiopulmonary resonance index series.

[0043] In the respiratory measurement scenario, the respiratory signal is approximately cosine, and the heart rate variability caused by the respiratory signal is mixed with the heart rate variability caused by other neural inputs. Based on the processed NN sequence and respiratory signal, a causal relationship model of the respiratory-induced heart rate variation is established, and a frequency domain function of the amplitude of the respiratory effect on heart rate is derived (for details, see Wu Jiankang's book "Digital Health: Autonomic Nervous System Large Model, Digital Evaluation and Intervention", University of Science and Technology of China Press, May 2025), which is called the cardiopulmonary resonance function G(f):

[0044]

[0045] The cardiopulmonary resonance series of indicators are defined as follows:

[0046] The cardiopulmonary resonance amplitude (CRA) is defined as the maximum value of the cardiopulmonary resonance function G(f), that is:

[0047] CRA=Max(G(f))

[0048] CRA is the maximum heart rate change generated by the respiratory signal, which occurs at the respiratory center frequency f A The polyvagal theory suggests that CRA is proportional to the combined gain of vagal afferents, vagal regulation, and sinoatrial node receptors. Therefore, CRA is also considered by the biomedical community to be a representation of vagal tone.

[0049] Respiratory stability RS is the degree to which the respiratory frequency deviates from the center frequency, which is defined as the power spectrum of the respiratory signal R(f) at the respiratory center frequency f A Concentration nearby:

[0050]

[0051] Where c is the frequency range in which the power spectrum of the respiratory signal is required to be concentrated, and c = 0.1f is usually selected. A Respiratory stability significantly impacts the cardiorespiratory resonance amplitude (CRA). This means that only by remaining calm, eliminating distractions, and maintaining steady breathing during CRA measurements can one effectively complete CRA measurements and achieve CRA. Furthermore, conditions such as heart failure, stress, and inflammation can also affect respiratory stability. Therefore, respiratory stability (RS) is also an indicator of both physical and mental health.

[0052] During the free breathing phase, the cardiopulmonary resonance function G(f) is only a small part of the power spectrum of the heart rate sequence, distributed in the high-frequency part between 0.15 and 0.5 Hz. The low-frequency part of the heart rate power spectrum, as well as some high-frequency components, are produced by other internal and external factors such as stress and inflammation, as neural inputs, through the regulation of the sympathetic and vagus nerves. If these are collectively referred to as the damping or interference of the cardiopulmonary resonance circuit, in most cases, the cardiopulmonary resonance interference will weaken as the cardiopulmonary resonance amplitude increases. Therefore, we define the cardiopulmonary resonance factor CRF as the ratio of the integral of the cardiopulmonary resonance function G(f) to the integral of the heart rate power spectrum HR(f):

[0053]

[0054] Obviously, the cardiopulmonary resonance interference factors caused by pressure, inflammation, organ status, etc. are written as (Cardiopulmonary Resonance Resistance) CRR, and CRR = 1-CRF.

[0055] Many patients with cardiopulmonary diseases and diabetes have relatively low CRF. Not only do they suffer from low vagal tone, leading to imbalances in autonomic nervous system regulation of the cardiopulmonary and digestive systems, but they also experience psychological stress. Therefore, rehabilitation for cardiopulmonary patients must be a dual-care approach (cardiopulmonary and psychological).

[0056] At the same time, based on the NN sequence, the heart rate variability HRV indicators are calculated: mean heart rate (MHR), NN sequence standard deviation SDNN, NN difference sequence standard deviation RMSSD, LF: relative value of the low frequency 0.04-0.15Hz of the NN sequence Fourier power spectrum, HF: relative value of the high frequency 0.15-0.4Hz of the NN sequence Fourier power spectrum, LF / HF.

[0057] Thus, the names, reference values ​​and clinical significance of each index of the cardiopulmonary resonance index series (CRI) listed in Table 1 were obtained.

[0058] Table 1. Cardiopulmonary resonance index series CRI and its reference values ​​and clinical significance

[0059]

[0060] Physical and mental health assessment module

[0061] The physical and mental health assessment module 212 is based on a model of the resonance phenomenon of the central autonomic nervous system caused by breathing, analyzes the changes in the cardiopulmonary resonance series indicators during the three breathing measurement stages, and derives various indicators of physical and mental health.

[0062] (1) Oxygen metabolism capacity.

[0063] Respiratory signals, particularly rhythmic deep breathing, stimulate the nucleus tractus solitarius (NTS) in the hypothalamus through baroreceptors and chemoreceptors. The hypothalamus integrates information from the NTS and central nervous system, regulating heart rate and pulse through the sympathetic and parasympathetic nervous systems. When the conditions are met, resonance between the lungs and cardiovascular system occurs. At this point, deep breathing fully opens the alveoli, allowing for efficient oxygenation of the surrounding capillaries. Red blood cells in the blood carry oxygen to various tissues, supplying cells, including neurons and mitochondria. This efficient oxygen supply enhances cell activity and prolongs mitochondrial life.

[0064] The resonance of the cardio-cerebral-pulmonary nervous system caused by breathing can be represented by a series LCR resonant circuit. The respiratory signal serves as the signal source, and oxygen is equivalent to the current in the entire series resonant circuit. The first oxygen exchange: oxygen leaves the alveoli, enters the capillaries, and enters the red blood cells, which is equivalent to charging the circuit's capacitor C. Blood flow to the body is driven by the LC electromagnetic wave. The second oxygen exchange: oxygen leaves the red blood cells, exits the capillaries, enters the body's cells, and enters the mitochondria, which is equivalent to discharging the circuit's capacitor. When resonance occurs, the circuit current reaches its peak, equivalent to peak oxygen supply.

[0065] Therefore, oxygen metabolism capacity (MetA) is defined as a function of the amplitude of cardiopulmonary resonance during two rhythmic deep breathing phases:

[0066] MetA=c MetA *(a1CRA1+ba2CRA2),

[0067] Among them, c MetA is the normalization coefficient, CRA1 and CRA2 are the normalized cardiopulmonary resonance amplitudes of the two rhythmic deep breathing stages, and a1 and a2 are the contribution ratios of rhythmic breathing in these two stages.

[0068] (2) Mental health status.

[0069] The resistance R in an equivalent series resonant circuit damps the resonance. The current I in the resonant circuit is inversely proportional to R. In the heart, brain, and lung resonant circuit, the damping R reflects factors such as inflammation within the circuit, particularly decreased cardiopulmonary function, and reduced oxygen exchange efficiency caused by psychological stress and pressure.

[0070] According to the definition of cardiopulmonary resonance factor (CRF), mental health (MenH) is the weighted sum of the CRFs of the three respiratory phases:

[0071] MenH=c MenH *(b0CRF0*RS0+b1CRF1*RS1+b2CRF2*RS2),

[0072] Among them, c MenHis the normalization coefficient, CRF0, CRF1 and CRF2 are the cardiopulmonary resonance factors of free breathing and two rhythmic deep breathing stages, RS0, RS1 and RS2 are the respiratory stability of these three breathing stages, b0, b1 and b2 are the contribution ratios of these three stages.

[0073] (3) Cell lifespan.

[0074] The psychological state is reflected in the ability to calm down and complete the measurement with high breathing stability according to guidance. It is also reflected in the resonance factor of the cardio-cerebral-pulmonary circuit, or the quality factor of the resonant circuit. It is inversely proportional to the damping (pressure, stress, inflammation) in the circuit. When the cardio-cerebral-pulmonary circuit reaches resonance, the oxygen supply to the cell mitochondria reaches its peak. Therefore, cell lifespan (Clife) is also related to human lifespan and chromosome telomere length, and can be calculated as follows:

[0075] Clife=c Clife (MetA+MenH), among which c Clife is the normalization coefficient.

[0076] (4) Ability to withstand stress.

[0077] Rhythmic deep breathing achieves resonance in the heart, brain, and lung circuits, and is also a stress-response tool for people facing competition, pain, and other situations. Therefore, the improvement in oxygen metabolism and mental health during resonance and free breathing is defined as stress-resistance (StrC):

[0078] StrC=c StrC [d1(CRF2-CRF0)+d2(CRA2-CRA0)], where c StrC is the normalization coefficient, d1 and d2 are the improvements of cardiopulmonary resonance factor CRF and cardiopulmonary resonance amplitude CRA, respectively.

[0079] (5) Heart rate recovery.

[0080] For people with a heart rate above 75 BPM, especially those with hypertension, as the rhythmic breathing rate decreases, the vagal tone increases, the heart rate and blood pressure decrease, and cardiovascular function improves. This is how Heart Rate Recovery (HRec) is defined:

[0081] HRec=(MHR2-MHR0).

[0082] The physical and mental health assessment module 202 simultaneously derives the mental health risk M risk and the cardiopulmonary metabolic disease risk CP risk:

[0083] Mrisk=1-MenH;

[0084] CPrisk=1-(Clife+StrC) / 2.

[0085] At the same time, the gas and rhythm of rhythmic deep breathing pass through the nasal cavity and lung tract respectively, affecting the olfactory bulb OB and nucleus tract solitarius NTS, and further acting on the prefrontal cortex, locus coeruleus (LC), central nucleus of the amygdala (CeA) and hippocampus (HC). Together with the interaction between them, the regulation of anxiety, attention and memory is achieved.

[0086] Chinese Medicine Dialectics Module

[0087] The TCM syndrome differentiation module 213 is used to standardize the TCM syndrome differentiation patterns collected by the expert diagnosis collection module 112 to obtain standardized TCM syndrome differentiation pattern information.

[0088] The TCM syndrome differentiation module utilizes various TCM syndrome types, including Yin-Yang deficiency and excess, Eight-Principle syndrome differentiation, Zang-Fu differentiation, and Qi-Blood-Body-Fluid differentiation. Yin-Yang deficiency and excess differentiation plays a central role in TCM diagnosis. Yin-Yang is the general principle of TCM theory, summarizing the unity of opposites. Deficiency and excess are fundamental assessments of the strength of the body's vital energy and the rise and fall of pathogenic energy. Yin-Yang deficiency and excess differentiation forms the foundation of all diagnostic methods in TCM. Other principles within the Eight-Principle syndrome differentiation (exterior and interior, cold and heat) are interrelated to Yin-Yang deficiency and excess, forming the framework for comprehensive disease diagnosis in TCM. Specific diagnostic methods, such as Zang-Fu differentiation and Qi-Blood-Body-Fluid differentiation, also rely on the assessment of Yin-Yang deficiency and excess, building upon this foundation to further analyze the specific manifestations of the disease in the Zang-Fu organs, Qi-Blood-Body-Fluid, and other areas.

[0089] Yin-Yang deficiency and excess syndrome differentiation provides a high-level overview of the essential characteristics of a disease. By comprehensively analyzing a patient's symptoms, signs, and other aspects of the body, the complex and diverse manifestations of a disease can be grouped into distinct Yin-Yang deficiency and excess syndromes. This allows doctors to grasp the key points of the disease and provide clear guidance for subsequent treatment and prognosis. Therefore, the present invention preferably utilizes Yin-Yang deficiency and excess syndrome differentiation.

[0090] Figure 2 The figure shows one application example of standardization of Yin-Yang deficiency-excess syndrome differentiation in the first embodiment of the present invention. In other embodiments, the present invention can also be applied to other syndrome differentiations such as Eight Principles Syndrome Differentiation, Zang-Fu Syndrome Differentiation, Qi-Blood-Body-Fluid Syndrome Differentiation, etc.

[0091] like Figure 2 As shown, the present invention uses numbers to replace the Yin-Yang deficiency and excess syndrome types to facilitate data processing of the machine learning model. The Yin-Yang deficiency and excess syndrome types corresponding to the ordinal numbers in the figure are:

[0092] 1. Balance of Yin and Yang: A healthy state, or "homeostasis" based on autonomic nervous system regulation;

[0093] 2. Low level of yin and yang balance: sub-health;

[0094] 3. Deficiency of both Yin and Yang: Equivalent to sympathetic and parasympathetic neuropathy; nourishing Yin and tonifying Yang;

[0095] 4. Yang Sheng: syndrome of excess heat, sympathetic hyperactivity; exogenous pathogenic heat; clearing away heat;

[0096] 5. Yin deficiency: deficiency-heat syndrome, low vagus nerve function; lack of body fluid and qi; replenishing Yin;

[0097] 6. Yin excess: syndrome of excess cold, parasympathetic overexcitation; exogenous cold and dampness; dissipation of cold;

[0098] 7. Yang deficiency: deficiency-cold syndrome, weak sympathetic nerves; long-term fear of cold; tonifying Yang.

[0099] Reporting and Intervention Prescription Module

[0100] The report and intervention prescription module 214 is used to standardize the reports and intervention prescriptions collected by the expert diagnosis collection module 112 to obtain a standardized subject report, which includes information on TCM syndrome differentiation and intervention prescriptions.

[0101] Specifically, the test subject report includes at least one of the following:

[0102] The subject's lung resonance index series, physical and mental health indicators, disease risk coefficient, and the TCM syndrome differentiation type and intervention prescription corresponding to the lung resonance index series, physical and mental health indicators, and disease risk coefficient.

[0103] In addition, the subject's report can also include syndrome-related knowledge, intervention prescriptions, daily precautions, and dietary therapy generated by AI tools such as DeepSeek.

[0104] Machine Learning Subsystem

[0105] The machine learning subsystem 310 is established based on the machine learning model 311 and trains the machine learning model 311 based on the normalized data processed by the expert case subsystem.

[0106] During training, the machine learning model 311 uses the characteristic vector composed of the cardiopulmonary resonance index series, physical and mental health indicators, and disease risk coefficients as input data, and the Yin-Yang deficiency and excess syndrome types calibrated by traditional Chinese medicine experts as label data (output data). Through its machine learning training algorithm, the corresponding relationship between the cardiopulmonary resonance index series, physical and mental health indicators, and disease risk coefficients and syndrome types is established. In this embodiment, the cardiopulmonary resonance index series is 9 indicators of three respiratory stages, forming a 27-dimensional characteristic vector. Together with the physical and mental health indicators and 7 risk factors, the total number of input vector dimensions is 34. There are 7 Yin-Yang deficiency and excess syndrome types. This embodiment uses a conventional support vector machine (SVM) learning method. In other embodiments, other machine learning models or algorithms, such as neural network models, can also be used.

[0107] Figure 3 This is a module architecture diagram of the second embodiment of the intelligent Chinese medicine Yin-Yang deficiency and excess syndrome differentiation system based on autonomic nerve assessment. This second embodiment is the architecture of the system when it is in AI diagnosis mode. Figure 3 As shown, the system includes a data acquisition subsystem 120 , a digital case subsystem 220 and a machine dialectics subsystem 320 .

[0108] like Figure 3 As shown, in this embodiment, the data acquisition subsystem 120 only includes the cardiopulmonary resonance measurement module 121. In other words, this second embodiment only collects data used to calculate the cardiopulmonary resonance index, including ECG, respiratory data, and PPG data, but does not collect expert diagnosis information. This is because this embodiment uses the AI ​​diagnosis of the machine dialectics subsystem 320 to determine the TCM Yin-Yang deficiency-excess syndrome type.

[0109] The digital case subsystem 220 has a similar structure to the first embodiment. The cardiopulmonary resonance index module 221 and the physical and mental health assessment module 222 are identical. They perform the same processing on the data from the cardiopulmonary resonance measurement module 121 to obtain the cardiopulmonary resonance index series for the current subject and calculate physical and mental health indicators and disease risk factors.

[0110] The Yin-Yang Deficiency and Excess Dialectical Information Module 223 and the Reporting and Intervention Prescription Module 224 are similar in structure to those of the first embodiment. However, the Yin-Yang Deficiency and Excess Dialectical Information and the Traditional Chinese Medicine (TCM) information used in the reports and intervention prescriptions received and stored do not come from TCM experts. Instead, they utilize the machine dialectical model within the machine dialectical subsystem 320 to automatically derive the Yin-Yang Deficiency and Excess syndrome types based on a series of cardiopulmonary resonance indicators, physical and mental health indicators, and disease risk factors. This is the most fundamental difference compared to the first embodiment.

[0111] The intelligence and accuracy of the machine dialectic model 321 of the machine dialectic subsystem 320 of the second embodiment depends on the number and quality of cases in the expert case subsystem 210 of the first embodiment. As the number of high-quality expert cases increases, the intelligence and accuracy of the machine dialectic model 321 will also increase through the learning function of the machine learning model.

[0112] Based on the cardiopulmonary resonance index series, physical and mental health indicators, and disease risk factors from the digital case subsystem 220, the machine dialectical model 320 automatically generates a Yin-Yang deficiency-excess syndrome type. Specifically, using the 34-dimensional vector consisting of the subject's cardiopulmonary resonance index series, physical and mental health indicators, and disease risk factors as input, the machine dialectical model 320 calculates the probabilities of the seven Yin-Yang deficiency-excess syndrome types and selects the syndrome with the highest probability as the Yin-Yang deficiency-excess syndrome type for the current subject.

[0113] The report and intervention prescription module S224 of the second embodiment, as with the first embodiment, also includes intervention prescriptions corresponding to the cardiopulmonary resonance index series, physical and mental health indicators, and disease risk factors, as well as syndrome-related knowledge, intervention prescriptions, daily precautions, and diet therapy corresponding to the Yin-Yang deficiency and excess syndrome types, such as those generated by AI tools such as DeepSeek. Thus, the report and intervention prescription module 224 can find an intervention prescription suitable for the subject based on the cardiopulmonary resonance index series, physical and mental health indicators, and disease risk factors obtained by the cardiopulmonary resonance index module 221 and the physical and mental health assessment module 222. At the same time, based on the Yin-Yang deficiency and excess syndrome types obtained by the machine dialectical model of the machine dialectical subsystem 320, the syndrome-related knowledge, intervention prescriptions, daily precautions, and diet therapy corresponding to the subject are found.

[0114] As a preferred embodiment, the report and intervention prescription module 224 can also use case reasoning methods to compare cases in the same Yin-Yang deficiency and excess syndrome type, implement the effects of intervention prescriptions, and thus optimize the intervention prescriptions to achieve personalized and optimized goals.

[0115] In summary, the present invention not only standardizes the cardiopulmonary resonance series indicators and the physical and mental health assessment indicator system, but also standardizes the process and data of cardiopulmonary resonance measurement, and solves the problem that traditional Chinese medicine is based on the subjective judgment of experts' "looking, listening, asking and feeling", lacks standardization and consistency, and is difficult to pass on.

[0116] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent Chinese medicine dialectical system based on autonomic nervous system assessment, comprising a data acquisition subsystem (110), an expert case subsystem (210) and a machine learning subsystem (310), characterized in that: The data acquisition subsystem (110) is used to measure the signal reflecting the heart and lung resonance of the subject, obtain the measurement signal, and collect the TCM syndrome differentiation type and intervention prescription of the subject by the TCM expert; The expert case subsystem (210) is used to store the measurement signals, TCM syndrome differentiation types and intervention prescriptions, and process and analyze the measurement signals to obtain a series of cardiopulmonary resonance indexes, physical and mental health indicators and disease risk coefficients; The machine learning subsystem (310) is used to establish a machine learning model, taking the cardiopulmonary resonance index series, physical and mental health indicators and disease risk coefficients stored in the expert case subsystem (210) as input, and taking the TCM syndrome differentiation type as calibration output, and training the machine learning model to determine the parameters of the machine learning model.

2. The intelligent TCM dialectical system based on autonomic nervous system evaluation according to claim 1, characterized in that: The measurement signals are electrocardiogram and respiratory signals, or PPG signals; the process of measuring to obtain the measurement signals includes measuring a free breathing phase and at least two guided rhythmic breathing phases of the subject.

3. The intelligent TCM dialectical system based on autonomic nerve evaluation as claimed in claim 2, characterized in that: The expert case subsystem (210) is used to extract the NN sequence of the electrocardiogram signal and the corresponding respiratory signal from the measurement signal, and calculate the cardiopulmonary resonance index series based on the NN sequence of the electrocardiogram signal and the corresponding respiratory signal; the cardiopulmonary resonance index series includes at least one of the following: respiratory stability RS, cardiopulmonary resonance amplitude CRA, cardiopulmonary resonance factor CRF, mean heart rate MHR, NN sequence standard deviation SDNN, NN difference sequence standard deviation RMSSD, NN sequence Fourier power spectrum low frequency 0.04 to 0.15 Hz relative value LF, NN sequence Fourier power spectrum high frequency 0.15 to 0.4 Hz relative value HF.

4. The intelligent TCM dialectical system based on autonomic nervous system evaluation as claimed in claim 3, characterized in that: The physical and mental health indicators and disease risk factors include at least one of the following: Oxygen metabolism capacity MetA, mental health status MenH, cell lifespan Clife, stress resistance StrC, heart rate recovery HRec, mental health risk Mrisk, and cardiopulmonary metabolic disease risk CPrisk.

5. The intelligent TCM dialectical system based on autonomic nervous system assessment according to claim 1, characterized in that: The TCM syndrome differentiation type is at least one of the following: Yin-Yang deficiency and excess syndrome differentiation type, Eight Principles syndrome differentiation type, Zang-Fu syndrome differentiation type, Qi-blood-body-fluid syndrome differentiation type; The Yin-Yang deficiency and excess syndrome type consists of 7 syndrome types: Yin-Yang balance, low-level Yin-Yang balance, Yin-Yang deficiency, Yang excess, Yin deficiency, Yin excess, and oxygen deficiency.

6. The intelligent TCM dialectical system based on autonomic nervous system evaluation according to any one of claims 1 to 5, characterized in that: The expert case subsystem (210) is further used to generate a subject report, which includes at least one of the following: a cardiopulmonary resonance index series, a physical and mental health index, a disease risk coefficient, a Chinese medicine syndrome type and an intervention prescription.

7. The intelligent TCM dialectical system based on autonomic nervous system evaluation according to any one of claims 1 to 5, characterized in that: The machine learning model adopts a support vector machine or a multi-layer neural network learning algorithm.

8. An intelligent TCM dialectical system based on autonomic nerve assessment, comprising a data acquisition subsystem (120), a digital case subsystem (220) and a machine dialectical subsystem (320), characterized in that: The data acquisition subsystem (120) is used to measure a signal reflecting the heart-lung resonance of the subject to obtain a measurement signal; The digital case subsystem (220) is used to store the measurement signal, and process and analyze the measurement signal to obtain a cardiopulmonary resonance index series, physical and mental health indicators and disease risk coefficients; the machine dialectic subsystem (320) includes a trained machine learning model, which is used to automatically generate a Chinese medicine dialectical syndrome type based on the cardiopulmonary resonance index series, physical and mental health indicators and disease risk coefficients.

9. The intelligent TCM dialectical system based on autonomic nervous system assessment according to claim 8, characterized in that: The measurement signals are electrocardiogram and respiratory signals, or PPG signals; the process of measuring to obtain the measurement signals includes measuring a free breathing phase and at least two guided rhythmic breathing phases of the subject.

10. The intelligent TCM dialectical system based on autonomic nervous system assessment according to claim 9, characterized in that: The digital case subsystem (220) is used to extract the NN sequence of the electrocardiogram signal and the corresponding respiratory signal from the measurement signal, and calculate the cardiopulmonary resonance index series based on the NN sequence of the electrocardiogram signal and the corresponding respiratory signal; The cardiopulmonary resonance index series includes at least one of the following: respiratory smoothness RS, cardiopulmonary resonance amplitude CRA, cardiopulmonary resonance factor CRF, mean heart rate MHR, NN sequence standard deviation SDNN, NN difference sequence standard deviation RMSSD, NN sequence Fourier power spectrum low frequency 0.04~0.15Hz relative value LF, NN sequence Fourier power spectrum high frequency 0.15~0.4Hz relative value HF.

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