System for evaluating treatment effect of alcohol use disorder patient by using HRV index

Abstract

The invention provides a system for evaluating the treatment effect of a patient suffering from alcohol use disorder by using an HRV (heart rate variability) index, the system monitors and analyzes physiological and neural responses of the patient by using a heart rate variability HRV index, measures the heart rate variability HRV through a heart rate monitoring sensor, and then calculates time domain, frequency domain, nonlinearity and time-frequency HRV indexes through a data processing unit. These indicators are compared to preset reference values or indicator patterns to identify psychological state and physiological response characteristics of the patient. Finally, the system judges the physiological state, the neural activity state and the treatment effect of the alcohol use disorder patient according to the analysis result of the HRV index, and the analysis and judgment result is displayed through an output unit. The system can accurately evaluate the treatment effect of the patient with the alcohol use disorder, so that accurate treatment and prevention strategies are provided for the patient with the alcohol use disorder.

Description

Technical Field

[0001] The invention relates to a system for evaluating the treatment effect of patients with alcohol use disorder (AUD). Background Art

[0002] Alcohol use disorders (AUD) are characterized by compulsive heavy drinking and loss of control over alcohol intake. They are common and socially harmful chronic diseases. AUD is associated with a variety of physical and psychiatric sequelae and is one of the most common mental disorders in the world. Alcohol relapse is the main clinical problem of AUD. Studies have shown that the relapse rate within one year after discharge is as high as 60-80%, and in a 3-year follow-up, the relapse rate is as high as 45%. The craving for alcohol is the key factor in the relapse of AUD patients. The craving for alcohol is the main feature of AUD patients and one of the important items for diagnosing AUD. Craving can be defined as an urgent and irresistible desire or impulse to drink, which, if not satisfied, will lead to psychological and physiological symptoms. Therefore, reducing craving is the key to treating AUD. Obviously, alcohol use disorder requires a multidisciplinary approach that combines drug treatment, social support and psychological intervention. In addition, to achieve abstinence and prevent relapse, detection methods that can accurately assess psychological craving are also needed.

[0003] How to accurately evaluate the treatment effect of alcohol addiction is currently facing a great challenge. Heart rate variability (HRV) refers to the changes in the difference between successive heartbeats or the changes in the speed of the heartbeat. In the pathophysiology of addictive diseases, HRV is an indicator of the individual's overall ability to regulate psychological responses. HRV is mainly regulated by the autonomic nervous system, and is also affected by multiple factors such as respiration, blood pressure, skin temperature, and renin-angiotensin. The autonomic nervous system regulates heart rate and HRV through the sympathetic and parasympathetic nerves. These two branches usually work in an antagonistic manner. Changes in any branch of the system will change HRV to meet internal and external needs. Increased sympathetic nerve activity and decreased parasympathetic nerve activity reflect the impairment of the system's ability to respond to various environmental stimuli. HRV can be used to measure the activity of the autonomic nervous system in patients with AUD, but it is not clear whether it can be used to evaluate the effectiveness of treatment.

[0004] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0005] The main purpose of the present invention is to overcome the defects of the above-mentioned background technology and provide a system for evaluating the treatment effect of patients with alcohol use disorder AUD using HRV indicators. The present invention uses the research and discovery of the neural mechanism characteristics of subconscious memory extinction applied to AUD patients. Through the system of the present invention, heart and brain detection and analysis are performed, which can accurately evaluate the treatment effect of the new treatment method of subconscious memory extinction on AUD patients, and provide more accurate treatment and detection prevention strategies for AUD patients, which has a good guiding significance.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A system for evaluating the treatment effect of patients with alcohol use disorder (AUD) using HRV indicators, comprising:

[0008] Heart rate monitoring sensor, used to measure the user's heart rate variability HRV;

[0009] A data processing unit is connected to the heart rate monitoring sensor and is used to perform the following operations:

[0010] a. Obtain the user's heart rate variability HRV by measuring the ECG signal in real time through the heart rate monitoring sensor, and calculate the time domain, frequency domain and nonlinear and time-frequency HRV indicators;

[0011] b. Comparing the calculated HRV with a preset baseline value or an indicator pattern under different psychological states to identify the physiological response characteristics of the patient under a specific psychological state; the preset baseline value or indicator pattern is based on the classification of the subconscious and conscious responses of AUD patients under different exposure conditions and is analyzed and verified using statistical methods;

[0012] c. Analyze and judge the activity of the sympathetic and parasympathetic nerves when the patient subconsciously processes alcohol stimulation based on the comparison results of the HRV frequency domain index with the preset baseline value or index pattern; analyze and evaluate the patient's response to subconscious memory extinction treatment and the overall activity of the autonomic nervous system based on the comparison results of the HRV time domain index with the preset baseline value or index pattern; analyze and evaluate the patient's autonomic nervous system response to different treatment conditions based on the comparison results of the HRV nonlinearity and time frequency index with the preset baseline value or index pattern;

[0013] d. Comprehensively judge the physiological state and treatment effect of AUD patients based on the analysis results of HRV indicators;

[0014] The output unit is used to output the HRV index analysis and judgment results.

[0015] The present invention has the following beneficial effects:

[0016] The system of the present invention provides a new and effective method for evaluating the therapeutic effect of subconscious memory extinction on patients with alcohol use disorder (AUD) by monitoring and analyzing HRV indicators. The system can accurately capture the physiological and neural responses of patients during treatment, thereby providing clinicians with accurate evaluation of the therapeutic effect. Using statistical methods, the system of the present invention can verify the significance and relevance of preset benchmark values ​​or indicator patterns, thereby enhancing the reliability of the evaluation results. The system uses HRV indicators to provide a comprehensive treatment effect evaluation tool that can more comprehensively reflect the physiological and psychological state of patients. The system can also detect abnormal changes in the patient's state in a timely manner by continuously monitoring the trend of indicator changes, so as to achieve early intervention and prevent deterioration. In addition, the real-time feedback provided by the system can improve the patient's treatment compliance and participation. Using the personalized monitoring and analysis results generated by the system, the treatment plan can be adjusted according to the patient's specific response to achieve personalized treatment and improve the effectiveness of treatment, thereby providing strong technical support for the rehabilitation of AUD patients. The system implements a non-invasive, low-cost treatment effect evaluation, and the design of the system allows it to be integrated with existing medical equipment and workflows, which is convenient for implementation and use in different medical environments, while helping to reduce medical costs and improve the efficiency of medical resource use.

[0017] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is the result of noise reduction and reconstruction of a partial ECG signal of a subject in an embodiment of the present invention.

[0019] Figure 2 This is an IBI sequence after preprocessing of a subject in an embodiment of the present invention.

[0020] Figure 3 FIG. 4 is an IBI sequence histogram of a subject according to an embodiment of the present invention.

[0021] Figure 4 4 is a power spectrum density diagram of a subject according to an embodiment of the present invention.

[0022] Figure 5 These are pictures related to the nonlinear HRV analysis results of a subject according to an embodiment of the present invention, (a) Poincare map, (b) detrended fluctuation analysis double logarithmic map.

[0023] Figure 6 Schematic diagram of the position of the cap electrode according to an embodiment of the present invention.

[0024] Figure 7 Schematic diagram of a recursive graph matrix according to an embodiment of the present invention.

[0025] Figure 8Schematic diagram of the system structure for evaluating the treatment effect of patients with alcohol use disorder using HRV indicators according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0027] Key terms and concepts:

[0028] HRV related concepts

[0029] Heart rate variability (HRV) refers to the variation in the difference between successive heartbeat cycles or the variation in the speed of the heartbeat. It is determined by the length of two adjacent RR intervals, that is, the small difference from the first cardiac cycle to the next cardiac cycle. Because the R wave is not the only time marker of the beat position, such as the QRS complex wave, the term IBI is also used as a generalization to represent any beat interval. The RR interval can also be called the NN (normal to normal) interval.

[15] .

[0030] The HRV time-domain and frequency-domain indices commonly calculated in research mainly include: (1) standard deviation of the NN interval sequence (SDNN, ms); (2) root mean square of the successive differences of the IBI sequence (RMSSD, ms); (3) percentage of the total intervals with a successive phase difference of more than 50 milliseconds (PNN50, %); (4) very low frequency power (frequency band range 0.01–0.04 Hz, VLF, ms); 2 ); (5) low frequency power (frequency band 0.04–0.15 Hz, LF, ms 2 ); (6) high frequency power (frequency band 0.15–0.4 Hz, HF, ms 2 ), etc. In addition to time domain and frequency domain indicators, it also includes nonlinear indicators and time-frequency analysis indicators.

[0031] HRV is an indicator of an individual's overall ability to regulate psychological responses. HRV is primarily regulated by the autonomic nervous system, but is also affected by multiple factors, including respiration, blood pressure, skin temperature, and renin-angiotensin. The autonomic nervous system regulates heart rate and HRV through the sympathetic and parasympathetic nerves. These two branches usually work in an antagonistic manner. Changes in any branch of the system will change HRV to meet internal and external demands. Increased sympathetic nerve activity and decreased parasympathetic nerve activity reflect impairment of the system's ability to respond to various environmental stimuli. SDNN is a marker of the total power (standard deviation) of HRV. SDNN and SDANN simultaneously reflect the regulation of heart rate by the sympathetic and parasympathetic nerves, that is, they reflect the overall activity of the autonomic nervous system and can be used to evaluate the regulatory ability of the autonomic nervous system. PNN50 and RMSSD are measurement indicators of parasympathetic nerve activity. HF is a marker of parasympathetic nerve activity, LF is related to sympathetic nerve activity, and the LF / HF ratio is an indicator of sympathetic-parasympathetic balance. There are fewer studies on VLF. Some studies have shown that fluctuations in body temperature regulation cycles and plasma renin activity may lead to changes in VLF. In fact, VLF is often ignored in research due to the use of IBI detrending technology that may destroy information in the VLF frequency band.

[0032] Previous studies applying advanced data mining tools to collate consistent trends or associations in large data sets have identified the normalized LF index (nLF) and LF peak (peakLF) as reliable descriptors of sympathetic / parasympathetic function. The peakLF describes the sympathetic / parasympathetic interaction mediated by the baroreflex and has been associated with changes in arousal and emotional responses. In studies of other types of disorders, peakHF has been found to be sensitive to PTSD status and may be a potential biofeedback parameter for future PTSD clinical trials. In fact, peak frequencies within the VLF, LF, and HF bands are less frequently mentioned in many studies.

[0033] like Figure 8 As shown, an embodiment of the present invention provides a system for evaluating the treatment effect of patients with alcohol use disorder (AUD) using HRV indicators, including:

[0034] Heart rate monitoring sensor, used to measure the user's heart rate variability HRV;

[0035] A data processing unit is connected to the heart rate monitoring sensor and is used to perform the following operations:

[0036] a. Obtain the user's heart rate variability HRV by measuring the ECG signal in real time through the heart rate monitoring sensor, and calculate the time domain, frequency domain and nonlinear and time-frequency HRV indicators;

[0037] b. Comparing the calculated HRV with a preset baseline value or an indicator pattern under different psychological states to identify the physiological response characteristics of the patient under a specific psychological state; the preset baseline value or indicator pattern is based on the classification of the subconscious and conscious responses of AUD patients under different exposure conditions and is analyzed and verified using statistical methods;

[0038] c. Analyze and judge the activity of the sympathetic and parasympathetic nerves when the patient subconsciously processes alcohol stimulation based on the comparison results of the HRV frequency domain index with the preset baseline value or index pattern; analyze and evaluate the patient's response to subconscious memory extinction treatment and the overall activity of the autonomic nervous system based on the comparison results of the HRV time domain index with the preset baseline value or index pattern; analyze and evaluate the patient's response to subconscious memory extinction treatment, including the response of the autonomic nervous system when subconsciously processing alcohol stimulation, based on the comparison results of the HRV nonlinearity and time frequency index with the preset baseline value or index pattern;

[0039] d. Comprehensively judge the physiological state and treatment effect of AUD patients based on the analysis results of HRV indicators;

[0040] The output unit, such as a display screen, is used to output (eg, display) the HRV index analysis and judgment results.

[0041] In some embodiments, the time domain HRV indicators include one or more of SDNN, SDANN, RMSSD, PNN50, HRVTi and TINN; the frequency domain HRV indicators include one or more of VLF, LF, HF, nLF, nHF, LF / HF ratio, peakVLF, peakLF and peakHF; the nonlinear indicators include one or more of SD1, SD2, sampen, α, α1 and α2; the time-frequency indicators include one or more of LF / HFTF and rLF / HF.

[0042] In some embodiments, the baseline value or index pattern is established based on the following method: classifying the subconscious and conscious reactions of AUD patients under different exposure conditions; establishing HRV index patterns corresponding to subconscious processing of alcohol stimuli, subconscious processing of neutral stimuli and conscious processing of alcohol stimuli based on the classification results; and using statistical methods, such as one-way analysis of variance and Pearson correlation analysis, to verify the significance and correlation of the index pattern.

[0043] The system of the present invention accurately evaluates the treatment effect of AUD patients by detecting the influence of the heart rate variability (HRV) index. The present invention uses multi-dimensional neurophysiological indicators such as HRV time domain, frequency domain, nonlinear and time-frequency indicators to quantify and evaluate the treatment effect. The present invention can reveal the changes in autonomic nervous system activity during the treatment process of AUD patients, and provide an accurate basis for the evaluation of clinical treatment effects. In summary, the new method of evaluating the treatment effect of the present invention provides an effective solution for the evaluation of the clinical treatment effect of AUD by detecting and analyzing comprehensive neurophysiological indicators.

[0044] Experimental Example 1

[0045] Processing and extraction of HRV related indicators

[0046] The collected and recorded ECG signals are processed offline by self-programming in MATLAB. First, the ECG signals are preprocessed: (1); Filtering: Hanning filtering, removing 50Hz power frequency interference, minimum mean square polynomial difference, removing baseline drift and wavelet denoising reconstruction are performed in sequence. For example, see Figure 1 ; (2) Extract R feature points to calculate the RR interval and calculate the heart rate in real time; (3) Save the IBI sequence. Figure 1 The figure shows the result of noise reduction and reconstruction of part of the ECG signal of a certain subject. In the figure, the horizontal axis is the 5000 intercepted data sampling points, and the vertical axis is the relative amplitude position of the data points.

[0047] An IBI sequence is a time series containing beat-to-beat intervals extracted from the ECG signal. The temporal position of the beats is usually based on the R wave, as it is usually the easiest wave to distinguish. The R wave usually has the largest amplitude compared to the surrounding P, Q, S, and T waveforms. Therefore, the beat-to-beat interval can be defined as the time difference between consecutive R peaks (RR interval).

[0048] The IBI sequence was preprocessed using the MATLAB-based software analysis package HRVAS: (1) ectopic interval detection: intervals with a change of more than 20% compared to the previous interval were marked by a percentage filter, and outliers were marked as intervals with a standard deviation of more than 3 by a standard deviation filter; (2) ectopic interval correction: marked intervals were removed for correction; (3) IBI detrending: wavelet packet detrending. An example of an IBI sequence after preprocessing for a subject is shown in Figure 2 ,In the figure, the horizontal axis is the total duration of the IBI time series, and the vertical axis is the duration of each beat-to-beat interval, that is, the length of each IBI.

[0049] Extraction of HRV related indicators

[0050] The MATLAB-based software analysis package HRVAS was used to extract HRV-related indicators, including time domain indicators, frequency domain indicators, nonlinear indicators and time-frequency indicators.

[0051] Time domain HRV indicators mainly include the following:

[0052] (1) Calculate the standard deviation of the NN interval sequence (SDNN). Two variants of SDNN can be applied to such long data sets. The first step is to divide the IBI sequence into non-overlapping segments. For human IBI sequences, the segment length is usually 5 minutes. The first is SDNNi, which represents the SDNN value of the i-th IBI segment. It is calculated by finding the standard deviation of each IBI segment and then returning the average of the standard deviations. SDANN calculates the average IBI of each segment and then returns the standard deviation of all averages.

[0053] (2) The root mean square of the successive differences (RMSSD) of the IBI sequence.

[0054] (3) The percentage of total intervals with consecutive phase differences exceeding 50 ms (PNN50).

[0055] (4) The geometric HRV index is calculated based on the geometric pattern of the IBI sequence. The most commonly used geometric pattern is the histogram of the IBI, see Figure 3 Two indices based on the IBI histogram are used, one is the HRV triangular index (HRVti) and the other is the triangular interpolation of NN interval histogram (TINN).

[0056] (5) Standard deviation of heart rate (sdHR).

[0057] Frequency domain HRV indicators mainly include the following:

[0058] (1) Typical HRV indicators obtained from frequency domain analysis are power and power ratio within the frequency band, including the absolute power of the spectrum (aVLF, aLF, aHF), the sum of aLF and aHF normalized to the total power (normalized low frequency power nLF, normalized high frequency power nHF), see Figure 4 , which shows the power spectral density diagram of a subject.

[0059] (2) The ratio of low-frequency to high-frequency power: LF / HF.

[0060] (3) The peak frequency peakVLF in the VLF band, the peak frequency peakLF in the LF band, and the peak frequency peakHF in the HF band.

[0061] Nonlinear HRV indicators mainly include the following:

[0062] (1) The Poincare map is a nonlinear analysis used to quantify self-similarity, where each IBI is affected by the previous IBI. Therefore, pairs of consecutive IBIs form attractors in the Poincare map. Ellipses are fitted to the plotted data, and the standard deviations along the marker line (SD2) and perpendicular to the marker line (SD1) represent the size of the major and minor axes of the ellipse, respectively. SD1 represents the SD of instantaneous beat-to-beat variability or short-term variability. SD2 represents the SD of continuous or long-term variability. See Figure 5 (a) in .

[0063] (2) Sample entropy (sampen), which can quantify the complexity of the signal or the rate at which new information is generated. If the sample entropy is zero, the consecutive sequences are identical, and the larger the sample entropy value, the higher the complexity of the consecutive sequence.

[0064] (3) Detrended fluctuation analysis (DFA) attempts to quantify the fractal-like or self-similar properties of non-stationary time series. This technique is a modification of the random walk RMS analysis applied to non-stationary signals. The RMS fluctuations of the integrated and detrended time series are measured at different scales and plotted against the size of the scale on a double logarithmic plot. The linear relationship between the double logarithms represents the scaling exponent α of the IBI series. The short-term scale α1 and the long-term scale α2 are usually described by two different linear regions on the double logarithmic plot, separated by a breakpoint located around 12-16 beats. See Figure 5 (b) in .

[0065] Time-frequency HRV indicators mainly include the following:

[0066] (1) Use short-time Fourier transform time-frequency analysis to calculate the LF / HF value of HRV, denoted as LF / HF TF .

[0067] (2) From the LF / HF instantaneous time series, another index can be extracted, called the LF / HF ratio (rLFHF). This index represents the "global" sympathetic-parasympathetic balance. On the instantaneous LF / HF graph, a line is drawn through LF / HF = 1. Above this line (LF / HF>1) there is sympathetic dominance, and below this line (LF / HF<1) there is parasympathetic dominance. The rLF / HF ratio is obtained by calculating the ratio of the bounded area above the line LF / HF = 1 to the bounded area below.

[0068] Results Analysis

[0069] The obtained HRV time domain indicators, frequency domain indicators, nonlinear indicators and time-frequency indicators were analyzed for differences.

[0070] Table 1 Multiple comparisons of HRV time domain indicators

[0071]

[0072] The results showed that in the first five minutes of the experimental task, the overall activity of the autonomic nervous system of each group of AUD patients when they consciously processed alcohol stimuli was greater than the overall activity of the autonomic nervous system when they subconsciously processed alcohol, and both activities were greater than the overall activity of the autonomic nervous system when they subconsciously processed neutral stimuli.

[0073] Table 2 Multiple comparisons of HRV frequency domain indicators

[0074]

[0075]

[0076] Table 3 Multiple comparisons of HRV nonlinearity and time-frequency indicators

[0077]

[0078] The results showed that when AUD patients subconsciously processed neutral stimuli, the long-term scale of the IBI sequence was greater than that when they consciously processed alcohol stimuli. Compared with conscious processing of alcohol stimuli, the subconscious processing of alcohol stimuli in AUD patients had a greater impact on the sympathetic-parasympathetic balance, which was consistent with the results of the difference analysis of HRV frequency domain indicators. From the perspective of the "global" sympathetic-parasympathetic balance, the advantage of sympathetic nerve dominance decreased in turn when AUD patients subconsciously processed alcohol stimuli, subconsciously processed neutral stimuli, and consciously processed alcohol stimuli.

[0079] According to the analysis of the results, it can be found that there are significant differences in time domain, frequency domain, nonlinearity and time-frequency indicators among the VBE group, VBN group and CVE group. The specific significant differences in the indicators are mainly reflected in the VBE group and the CVE group. By comparing nLF, nHF, LF / HF, peakLF, LF / HF TFThe analysis of the differences in the results of these indicators reflects that when AUD patients subconsciously process alcohol stimulation compared with when they consciously process alcohol stimulation, the former has greater sympathetic nerve activity and less parasympathetic nerve activity, and the impact on the sympathetic-parasympathetic balance is greater. From the perspective of the "global" sympathetic-parasympathetic nerve balance, the advantage of sympathetic nerve dominance is also in line with the above trend. Other significant difference indicators include the time domain indicator SDNN1 and the nonlinear indicator α2. Through the difference analysis of SDNN1, it can be found that its value is CVE group>VBE group>VBN group among the three groups, reflecting the contrast law of the overall activity of the autonomic nervous system under the three exposure conditions. It is the largest when the alcohol stimulation is processed consciously, in the middle when the alcohol stimulation is processed subconsciously, and the smallest when the neutral stimulation is processed subconsciously, indicating that the VBE group is indeed at the subconscious level, and alcohol stimulation and neutral stimulation do have different effects on the autonomic nervous system at the subconscious level. SDNN1 is the SDNN value of the first 5-minute IBI segment, indicating that the overall activity difference of the autonomic nervous system of the three groups of AUD patients is the largest in the first five minutes after receiving the stimulation. From the perspective of the overall SDNN values ​​of the three groups, there is no significant difference in the end and they tend to be consistent; through the difference analysis of α2, it can be found that the trend of the difference between the groups in the long-term scale of the IBI sequence is consistent with the time domain indicator SDNN1, and the results confirm each other. Furthermore, in order to accurately evaluate the treatment effect of patients with alcohol use disorder (AUD), a benchmark or reference point is set, and the calculated HRV index is compared with the preset benchmark or index pattern to measure the changes before and after treatment, confirm the effectiveness of the treatment and the improvement of the patient. Through statistical verification and classification of the response of AUD patients under different exposure conditions, a benchmark value or index pattern can be established to ensure the accuracy and reliability of the treatment effect evaluation.

[0080] The embodiment of the present invention provides an innovative technical solution, which can effectively evaluate the treatment effect of patients with alcohol use disorder AUD by setting a benchmark or reference point, performing statistical verification, and performing classification analysis. Specifically, the benchmark or reference point can be set by classifying the responses of AUD patients under different exposure conditions, and combined with statistical verification and classification analysis, an accurate evaluation of the treatment effect of patients with alcohol use disorder AUD can be achieved. This process can use known statistical methods such as one-way analysis of variance and Pearson correlation analysis to determine the baseline value or indicator pattern of HRV index or EEG index, and use this as a reference to confirm the effectiveness of treatment and the improvement of patients by comparing the changes before and after treatment. That is, the detected heart rate variability HRV index is used to measure the changes before and after treatment by comparing with the preset baseline value or indicator pattern. In addition, by applying statistical methods, such as one-way analysis of variance and Pearson correlation analysis, the significance and correlation of the indicator pattern can be verified, thereby ensuring the accuracy and reliability of the treatment effect evaluation. Through this systematic solution, the present invention can effectively evaluate and judge the physiological state and treatment effect of AUD patients, so as to provide more accurate treatment and prevention strategies for clinicians.

[0081] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A system for evaluating the treatment effect of patients with alcohol use disorder (AUD) using HRV indicators, characterized in that: include: Heart rate monitoring sensor, used to measure the user's heart rate variability HRV; A data processing unit is connected to the heart rate monitoring sensor and is used to perform the following operations: a. Obtain the user's heart rate variability HRV by measuring the ECG signal in real time through the heart rate monitoring sensor, and calculate the time domain, frequency domain and nonlinear and time-frequency HRV indicators; b. Compare the calculated HRV with a preset baseline value or with the indicator pattern under different psychological states to identify the physiological response characteristics of the patient under a specific psychological state; The preset baseline value or index pattern is based on the classification of subconscious and conscious reactions of AUD patients under different exposure conditions, and is analyzed and verified using statistical methods; c. Analyze and judge the activity of the sympathetic and parasympathetic nerves when the patient subconsciously processes alcohol stimulation based on the comparison results of the HRV frequency domain index with the preset baseline value or index pattern; analyze and evaluate the patient's response to subconscious memory extinction treatment and the overall activity of the autonomic nervous system based on the comparison results of the HRV time domain index with the preset baseline value or index pattern; analyze and evaluate the patient's response to subconscious memory extinction treatment, including the response of the autonomic nervous system when subconsciously processing alcohol stimulation, based on the comparison results of the HRV nonlinearity and time frequency index with the preset baseline value or index pattern; d. Comprehensively judge the physiological state and treatment effect of AUD patients based on the analysis results of HRV indicators; The output unit is used to output the HRV index analysis and judgment results.

2. The system according to claim 1, characterized in that The time domain HRV index includes one or more of SDNN, SDANN, RMSSD, PNN50, HRVTi and TINN; The frequency domain HRV index includes one or more of VLF, LF, HF, nLF, nHF, LF / HF ratio, peakVLF, peakLF and peakHF; The nonlinear index includes one or more of SD1, SD2, sampen, α, α1 and α2; The time-frequency index includes one or more of LF / HFTF and rLF / HF.

3. The system according to claim 1, characterized in that The benchmark value or indicator model is established based on the following method: Classify the responses of AUD patients to different treatment conditions; According to the classification results, HRV index patterns corresponding to subconscious processing of alcohol stimuli (VBE), subconscious processing of neutral stimuli (VBN) and conscious processing of alcohol stimuli (CVE) were established; Statistical methods, such as one-way analysis of variance and Pearson correlation analysis, were used to verify the significance and relevance of the indicator patterns.

4. The system according to claim 1, characterized in that The processing of the data processing unit includes: Preprocessing of ECG signal data, including filtering, noise removal and baseline drift correction; Extract the heartbeat interval IBI sequence from the preprocessed ECG signal; Calculate the heart rate variability (HRV) index of the IBI sequence.

5. The system according to any one of claims 1 to 4, characterized in that: The output unit comprises: The display screen is used to display the analysis and judgment results of the heart rate variability HRV index.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, data processing of the system according to any one of claims 1 to 5 is realized.

7. A computer program product, characterized in that When the computer program product is executed by a processor, the computer program product implements data processing of the system according to any one of claims 1 to 5.

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