Method and apparatus for determining addiction using heart rate variability

Abstract

A method for determining addiction may comprise the steps in which: an analysis apparatus acquires heart rate variability data of a subject; the analysis apparatus performs a spectral analysis of the heart rate variability data; the analysis apparatus determines an addiction level of the subject on the basis of the result of the spectral analysis; and the analysis apparatus applies an electrical stimulation to the subject on the basis of the determined addiction level.

Description

Method and device for determining addiction using heart rate variability

[0001] The technique described below describes a method for determining addiction using heart rate variability.

[0002] Addiction can be divided into physical addiction and mental addiction. Physical addiction refers to the physical harm caused by exposure to toxic substances. Mental addiction refers to psychological dependence, resulting in a persistent craving for a substance or behavior, which can harm both physical and mental health.

[0003] Addiction isn't simply a matter of an individual's deviant habits or tendencies. It's a type of brain disease that requires treatment. Addiction occurs when neurotransmitters lose their normal regulatory function, leading to a pathological state. Therefore, a systematic, comprehensive treatment system for addiction treatment is necessary.

[0004] [Prior Art Literature]

[0005] [Patent Document]

[0006] Patent Registration No. 10-0553516

[0007] Before treating addiction, the severity of the addiction must be assessed. The severity of addiction is assessed based on psychological, behavioral, and physiological symptoms. For example, the severity of addiction can be assessed using self-diagnosis checklists, clinical assessments, and biological tests. However, these questionnaire-based addiction assessment methods can be subject to response distortion due to the respondent's subjective attitudes, memory errors, and social needs. Furthermore, questionnaire-based addiction assessment methods cannot adequately explain the questions, thus failing to reflect individual characteristics. Furthermore, questionnaire-based addiction assessment methods struggle to understand the user's social processes in a natural setting, hindering in-depth research. Furthermore, because questionnaire-based addiction assessment methods cannot capture the user's nonverbal behavior, they can miss crucial information for addiction diagnosis.

[0008] The technology described below aims to objectively assess the degree of addiction by utilizing the degree of imbalance in the autonomic nervous system, and to provide a method to alleviate the degree of addiction by applying stimulation to the user based on the assessment results.

[0009] The addiction determination method may include a step in which an analysis device acquires heart rate variability data of a subject; a step in which the analysis device performs a spectral analysis on the heart rate variability data; a step in which the analysis device determines the addiction level of the subject based on the result of the frequency analysis; and a step in which the analysis device provides an electrical stimulus to the subject based on the determined addiction level.

[0010] The technology described below can be used to measure the degree of imbalance in the autonomic nervous system based on heart rate variability, and based on this, the level of addiction of the subject can be objectively determined. Using the technology described below, the intensity of electrical stimulation can be determined based on the subject's level of addiction, enabling appropriate treatment for the addiction. The technology described below can implement a closed-loop concept of electrical stimulation by applying electrical stimulation based on an individual's level of intoxication, rather than an open-loop concept of electrical stimulation. This can enhance the effectiveness of addiction treatment.

[0011] Figure 1 is one of the embodiments in which the analysis device (100) performs an addiction determination method.

[0012] Figure 2 shows one embodiment (200) of an addiction determination method.

[0013] FIG. 3 shows one embodiment (300) of generating a time-frequency image based on heart rate variability data.

[0014] Figure 4 shows one embodiment of determining the level of addiction based on heart rate variability and providing stimulation based on the level of addiction.

[0015] Figures 5 to 7 show experimental results of constructing an analysis model as one of the examples and evaluating the performance of the constructed model.

[0016] Figure 5 shows the results of organizing the characteristics of the subjects for the experiment.

[0017] Figure 6 is a confusion matrix that evaluates the performance of the deep learning model.

[0018] Figure 7 shows the results of analyzing the output value of the deep learning model using (Class Activation Mapping, CAM).

[0019] Figure 8 is a configuration of one embodiment of an analysis device (400).

[0020] The technology described below is susceptible to various modifications and embodiments. Specific embodiments of the technology described below may be illustrated in the drawings of the specification. However, these are intended to illustrate the technology described below and are not intended to limit the technology described below to any specific embodiments. Therefore, it should be understood that all modifications, equivalents, or alternatives that fall within the spirit and scope of the technology described below are encompassed by the technology described below.

[0021] In the terms used hereinafter, singular expressions should be understood to include plural expressions unless the context clearly dictates otherwise, and terms such as "comprises" should be understood to mean the presence of a described feature, number, step, operation, component, part, or combination thereof, but not to exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0022] Before going into a detailed description of the drawings, it should be made clear that the division of components in this specification is merely a division based on the main function of each component. That is, two or more components described below may be combined into one component, or one component may be further divided into two or more components with more detailed functions. In addition to its own main function, each component described below may additionally perform some or all of the functions of other components, and of course, some of the main functions of each component may be exclusively performed by other components.

[0023] Additionally, in performing a method or method of operation, each process constituting the method may occur in a different order than the stated order, unless the context clearly indicates a specific order. That is, each process may occur in the same order as the stated order, may be performed substantially simultaneously, or may be performed in the opposite order.

[0024] Figure 1 shows one embodiment in which an analysis device (100) performs an addiction determination method.

[0025] The analysis device (100) may be a device that performs an addiction determination method. The analysis device (100) may acquire heart rate variability data of the subject. The analysis device (100) may determine the subject's addiction level based on the heart rate variability data. The analysis device (100) may apply an electrical stimulus to the subject based on the determined addiction level of the subject.

[0026] The analysis device (100) can be physically implemented in various forms. For example, the analysis device (100) can take the form of a PC, laptop, smart device, server, or data processing-dedicated chipset.

[0027] There may be at least one analysis device (100). That is, the addiction determination method may be performed by one analysis device or may be performed separately by at least one device.

[0028] Figure 2 is one of the embodiments of the addiction determination process (200).

[0029] The analysis device can obtain heart rate variability data of the subject (210).

[0030] Heart rate variability (HRV) can refer to the variability in the time intervals between heartbeats. HRV can be defined as the variation in minute time intervals from one heartbeat to the next. HRV data can include measurements of the RR interval, the time interval between heartbeats.

[0031] The analysis device can process the subject's heart rate data to obtain the subject's heart rate variability data. The analysis device can obtain the heart rate data. The analysis device can store the subject's heart rate data in a buffer. If a preset amount of heart rate data is stored in the buffer, the analysis device can process the heart rate data stored in the buffer to generate heart rate variability data. If necessary, the analysis device can detect and remove ectopic beats from the heart rate data. Alternatively, the analysis device can perform resampling.

[0032] The analysis device can perform frequency analysis (spectral analysis) of heart rate variability data (220).

[0033] Frequency analysis can be a method of transforming heart rate variability data into the frequency domain and then analyzing its frequency components. For example, frequency analysis can include the Fourier transform (FT).

[0034] The analysis device can determine the level of addiction of the subject based on the frequency analysis results (230).

[0035] The frequency analysis results may include data that can be obtained through frequency analysis.

[0036] The frequency analysis result may include heart rate variability data converted into a time-frequency image. The time-frequency image may be data generated by performing spectral analysis on the heart rate variability data. The time-frequency image may be one of the images that shows how frequencies change over time. For example, the time-frequency image may be an image that visually represents the intensity of each frequency by placing time on the x-axis and frequency on the y-axis. The time-frequency image may be one of the 2D images that shows how the intensity of each frequency changes over time.

[0037] Frequency analysis results may include analysis of the ratio of high-frequency and low-frequency regions of heart rate variability data. For example, frequency analysis results may include the ratio of LF (Low Frequency, 0.04 to 0.15 Hz) to HF (High Frequency, 0.15 to 0.4 Hz).

[0038] A subject's addiction level may indicate the severity of their current addiction. For example, a subject's addiction level could fall into one of four categories: Normal, Mild, Moderate, and Severe. Alternatively, a subject's addiction level could be expressed numerically.

[0039] The analytical device can use the analytical model to determine the level of addiction of the subject.

[0040] The analysis model may be one that analyzes a user's addiction level based on frequency analysis results. The analysis model may also be one that receives time-frequency analysis results as input and outputs the user's addiction level.

[0041] The analysis model may be an AI-based model. An AI-based model may be a model created by simulating artificial intelligence.

[0042] The analysis model may be a trained model trained on training data. The analysis model may be a trained model capable of inferring a user's addiction level from time-frequency images, based on training data including time-frequency images and the user's addiction level. The training data may be built on a pre-stored database.

[0043] The analysis model can be a machine learning (ML)-based model. Machine learning models can be of various types. For example, machine learning models can be decision trees, random forests (RF), k-nearest neighbors (KNN), naive Bayes, support vector machines (SVM), and artificial neural networks (ANN). ANNs can be deep neural networks (DNNs), which can include convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), generative adversarial networks (GANs), and relational networks (RLs).

[0044] The analysis device can provide electrical stimulation to the subject based on the determined level of intoxication of the subject (240).

[0045] Electrical stimulation may be intended to reduce the subject's level of addiction.

[0046] If the subject's level of intoxication exceeds a preset threshold, the analysis device may administer an electrical stimulus to the subject. Conversely, if the subject's level of intoxication falls below the preset threshold, the analysis device may not administer an electrical stimulus to the subject.

[0047] The analytical device can electrically stimulate the subject's auricularis oculi. The analytical device can stimulate the subject's vagus nerve. The analytical device can electrically stimulate the subject's auricularis oculi, thereby stimulating the subject's vagus nerve. Through this, the analytical device can alleviate the subject's level of addiction. In addition, the analytical device can increase the activity of the subject's parasympathetic nervous system.

[0048] The analysis device can adjust the intensity of the electrical stimulation based on the subject's level of addiction. For example, if the subject's level of addiction is high, the intensity of the electrical stimulation can be increased. Conversely, if the subject's level of addiction is low, the intensity of the electrical stimulation can be decreased.

[0049] The analysis device can adjust the intensity of the electric stimulation by adjusting the pulse width. The analysis device can adjust the intensity of the electric stimulation according to the size of a preset threshold value. For example, when the frequency analysis result is LF / HF Ratio, the pulse width can be adjusted according to the following mathematical expression 1. Mathematical expression 1 may be an expression used to calculate the pulse width of the electric signal by multiplying (1 / (LF / HR)), which is the inverse of the LF / HR Ratio, which is the frequency analysis result, by a preset threshold value.

[0050]

[0051] In Equation 1, STIMweight can be any weight value. In Equation 1, Threshold can be a threshold value.

[0052] The analysis device can continue to provide electrical stimulation to the subject until the subject's level of intoxication decreases.

[0053] For example, the analysis device can re-acquire the heart rate variability data of the subject who has been electrically stimulated and then perform the previous process again. That is, the analysis device can re-acquire the heart rate variability data of the subject who has been electrically stimulated, convert the acquired heart rate variability into a time-frequency image, determine the intoxication level of the subject who has been electrically stimulated based on the converted time-frequency image, and if the target intoxication level is not reached, continue to provide the electrical stimulation, and if the target intoxication level is reached, stop providing the electrical stimulation. For example, the electrical stimulation can continue to be provided until the subject's intoxication level becomes Normal. Alternatively, the electrical stimulation can continue to be provided until the subject's autonomic nervous system maintains homeostasis.

[0054] Figure 3 shows one embodiment (300) of generating a time-frequency image.

[0055] The analysis device can acquire heart rate data (310). The analysis device can store the heart rate data in a buffer for a preset period of time (320). For example, the analysis device can store heart rate data for 5 minutes in the buffer. The analysis device can preprocess the heart rate data stored in the buffer (330). For example, the analysis device can detect an ectopic beat in the heart rate data and perform 4 Hz resampling. The analysis device can also generate heart rate variability data based on the preprocessed heart rate data. The analysis device can perform a spectral analysis on the preprocessed heart rate data (340). The analysis device can generate a time-frequency image based on the frequency analysis result (350).

[0056] Figure 4 shows one embodiment of determining an addiction level based on heart rate variability data and providing electrical stimulation based on the addiction level.

[0057] The analysis device can monitor heart rate data. The analysis device can generate a heart rate variability (HRV) time-frequency image as input data from the monitored heart rate data. The analysis device can input the generated HRV time-frequency image into an analysis model. The analysis model can include a convolutional network and a fully connected layer. The analysis device can classify the addiction level into one of four levels (Normal, Mild, Moderate, and Severe) based on the output value of the analysis model. If the user's addiction level is higher than a preset level (Mild, Moderate, and Severe), the analysis device can apply a stimulus to the user. The analysis device can reacquire the user's heart rate data to which the stimulus has been applied and repeat the previous process.

[0058] Figures 5 to 7 show experimental results of constructing an analysis model as one of the examples and evaluating the performance of the constructed model.

[0059] The study included 70 subjects. All subjects were male, considering the prevalence of Internet gaming disorder in males. The average age of the subjects was 22.0 ± 2.8 years. The subjects primarily played the online game League of Legends. They possessed a certain level of gaming skill. The absence of mental disorders was confirmed through testing. To confirm the absence of mental disorders, the Korean version of the Structured Clinical Interview for DSM-IV-TR (SCID-IV) was used. Males with an IQ below 80, as assessed by the Wechsler Adult Intelligence Scale IV (WAIS-IV), were excluded. The Internet Addiction Test (IAT) was used to assess the severity of Internet Gaming Disorder (IGD). Based on the severity of IGD, the subjects were divided into four groups: Normal, Mild, Moderate, and Severe. The Normal group included 15 subjects who scored between 0 and 30. The Mild group included 30 subjects who scored between 31 and 50. The Moderate group included 23 subjects who scored between 51 and 80. The Severe group included 2 subjects who scored between 81 and 100.

[0060] Figure 5 shows the results of organizing the characteristics of the subjects for the experiment.

[0061] As shown in Figure 5, the Internet Addiction Test scores for the four groups increase in the following order: Normal, Mild, Moderate, and Severe. Similarly, the Barratt impulsiveness scale scores for the Moderate and Severe groups are higher than those for the Normal and Mild groups.

[0062] As can be seen in Figure 5, there were no significant differences in the Full Scale Intelligence Quotient (FSIQ), Beck Depression Inventory (BDI) scores, Beck Anxiety Inventory scores, and Alcohol Use Disorder Identification Text (AUDIT) scores between the four groups. In addition, there were no significant differences in the Inattention / memory problems, Hyperactive / restlessness, Impulsive / emotional lability, and Problems with self-concept scores included in the Conners ADHD rating scale.

[0063] To secure training and test data, subjects were asked to play a game. Electrocardiogram (ECG) data were obtained from both the subjects at rest and while playing the game. The acquired ECG data was preprocessed to obtain HRV data. The acquired HRV data was analyzed in the time-frequency domain. Based on the analysis results, 2D images in the time-frequency domain were generated.

[0064] A deep learning model was trained based on the generated 2D images. The deep learning model used was VGG16. The deep learning model was trained to calculate the severity of internet game impairment based on input data.

[0065] Figure 6 is a confusion matrix that evaluates the performance of the deep learning model.

[0066] As shown in Figure 6, the analysis results confirmed that the deep learning model achieved an accuracy of 95.1%. Furthermore, the area under the ROC curve (AUC) was 0.9951 in the Normal group, 0.9941 in the Mild group, 0.9957 in the Moderate group, and 0.9992 in the Severe group. Therefore, the deep learning model can predict the severity of Internet gaming addiction with high accuracy.

[0067] Figure 7 shows the results of analyzing the output value of the deep learning model using (Class Activation Mapping, CAM).

[0068] As shown in Figure 7, the deep learning model primarily references the high-frequency (HF) region for analysis. Furthermore, for the Severe group, the deep learning model primarily references the HF region and the low-frequency (LF) region.

[0069] Figure 8 is a configuration of one embodiment of an analysis device (400).

[0070] The analysis device (400) may correspond to the analysis device (100) described above in FIG. 1. That is, the analysis device (400) may be a device that performs the aforementioned addiction determination method.

[0071] The analysis device (400) may include at least one input device (410), storage device (420), calculation device (430), output device (440), interface device (450), and communication device (460).

[0072] The input device (410) can receive data, information, or models necessary for performing the aforementioned addiction determination method. The input device (410) can receive heart rate variability data, heart rate data, time-frequency images, and the addiction level of the subject. The input device (410) can receive an analysis model. The input device (410) can receive training data necessary for training the analysis model. The input device (410) may include a device for inputting a certain command or data (such as a keyboard, mouse, touch screen, joystick, trackball, touchpad, scanner, or webcam). The input device (410) may also include a configuration for receiving data through a separate storage device (such as a USB, CD, or hard disk). The input device (410) may also receive data through a separate measuring device or a separate database. The input device (410) may also receive data through a communication device (460) in a wired or wireless manner. The input device (410) may also receive a control signal for controlling the analysis device (400).

[0073] The storage device (420) can store data, information, models, etc. required to perform the aforementioned addiction determination method. The storage device (420) can store heart rate variability data, heart rate data, time-frequency images, and the addiction level of the subject. The storage device (420) can store an analysis model. The storage device (420) can store training data required to train the analysis model. The storage device (420) can also be a device that stores certain data, information, models, etc. The storage device (420) can store data, information, models, etc. input through the input device (410). The storage device (420) can store commands that cause the operation device (430) to perform operations required for the addiction determination method. The storage device (420) can store information generated during the operation of the operation device (430). That is, the storage device (420) can include a memory. For example, storage devices may include hard disk drives (HDDs), solid state drives (SSDs), ROMs, RAMs, and CD-ROM magnetic tapes or floppy disks.

[0074] The computing device (430) can perform the calculations necessary to perform the aforementioned addiction determination method. The computing device (430) can perform a spectral analysis on heart rate variability data. The computing device (430) can determine the addiction level of the subject based on the frequency analysis results. The computing device (430) can process heart rate data stored in a buffer to generate heart rate variability data. The computing device (430) can determine the addiction level of the subject using an analysis model. The computing device (430) can generate a control signal to control an electrical stimulation device (470) to provide an electrical stimulation to the subject. The computing device (430) can generate a control signal to adjust the intensity of the electrical stimulation of the electrical stimulation device (470). The computing device (430) can be a device such as a processor, an application processor (AP), or a chip embedded with a program that processes data and performs certain calculations. For example, the computing device (430) may include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an NPU (Neural Processing Unit). The computing device (430) may generate a control signal that controls the analysis device (400). The computing device (430) may generate a control signal that controls the input device (410), storage device (420), output device (440), interface device (450), and communication device (460) included in the analysis device (400).

[0075] The output device (440) may be a device that outputs certain data, information, and models. The output device (440) may be a device that outputs certain data, information, and models to the outside of the analysis device (400). The output device (440) may output interfaces, input data, analysis results, etc. required for the data processing process. The output device (440) may include a device that outputs data, etc. through tactile, visual, auditory, gustatory, and olfactory methods. The output device (440) may be physically implemented in various forms, such as a display, a speaker, a vibration motor, or a document output device. The output device (440) may output data, information, or models stored in the storage device (420). The output device (440) may output data, information, and models generated during the operation of the operation device (430). The output device (440) may output the results of the operation of the operation device (430).

[0076] The interface device (450) may be a device that receives certain commands and data from the outside. The interface device (450) may receive a control signal for controlling the analysis device (400). The interface device (450) may output the results analyzed by the analysis device (400). The interface device (450) may receive information necessary for performing the aforementioned addiction determination method from a physically connected input device or an external storage device.

[0077] The communication device (460) can receive information necessary for performing the aforementioned addiction determination method. The communication device (460) can receive a model necessary for performing the aforementioned addiction determination method. The communication device (460) can transmit and receive heart rate variability data, heart rate data, time-frequency images, and the addiction level of the subject. The communication device (460) can transmit and receive an analysis model. The communication device (460) can receive a control signal necessary for controlling the analysis device (400). The communication device (460) can transmit the results analyzed by the analysis device (400). The communication device (460) can refer to a configuration that receives and transmits certain data, information, models, etc. through a wired or wireless network. The communication device (460) can perform network communication such as Wi-Fi (Wireless Fidelity), Wi-Fi Direct, Bluetooth, UWB (Ultra-Wide Band), NFC (Near Field Communication), USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), LAN (Local Area Network), etc.

[0078] The electrical stimulation device (470) may be a device that applies electrical stimulation to a subject. The electrical stimulation device (470) may apply electrical stimulation to the subject based on the level of intoxication. The electrical stimulation device (470) may apply different intensities of electrical stimulation depending on the subject's level of intoxication. The electrical stimulation device (470) may apply electrical stimulation to the subject's auricle.

[0079] The above-described addiction determination method can be implemented as a program (or application) including an executable algorithm that can be run on a computer.

[0080] The above program may be provided stored on a non-transitory computer readable medium.

[0081] The above-mentioned temporarily readable medium refers to various RAMs such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (Synclink DRAM, SLDRAM), and direct Rambus RAM (DRRAM).

[0082] The above non-transitory readable medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, the various applications or programs described above may be stored and provided in a non-transitory readable medium, such as a CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM (read-only memory), PROM (programmable read only memory), EPROM (Erasable PROM, EPROM), EEPROM (Electrically EPROM), or flash memory.

[0083] The present embodiment and the drawings attached to the present specification only clearly illustrate a part of the technical idea included in the above-described technology, and it will be obvious that all modified examples and specific embodiments that can be easily inferred by a person skilled in the art within the scope of the technical idea included in the specification and drawings of the above-described technology are included in the scope of the rights of the above-described technology.

Claims

1. A step in which the analysis device acquires the subject's heart rate variability (HRV) data; A step in which the above analysis device performs frequency analysis (spectral analysis) on the heart rate variability data; and A step of determining the level of addiction of the subject based on the frequency analysis result by the above analysis device; including; How to determine addiction.

2. In paragraph 1, The step of acquiring the above heart rate variability data is A step in which the analysis device acquires heart rate data of the subject; A step in which the analysis device stores the subject's heart rate data in a buffer; and A step of generating heart rate variability data by processing the heart rate data stored in the buffer by the analysis device when a preset amount of heart rate data is stored in the buffer; How to determine addiction.

3. In paragraph 1, The above frequency analysis result includes the heart rate variability data converted into a time-frequency image. How to determine addiction.

4. In paragraph 1, The above frequency analysis results include the results of analyzing the ratio of high-frequency and low-frequency regions of the heart rate variability data. How to determine addiction.

5. In paragraph 1, The step of determining the level of addiction includes determining the level of addiction using an analysis model, The above analysis model is a model that analyzes the user's addiction level based on the frequency analysis results. How to determine addiction.

6. In paragraph 1, The above analysis device includes a step of providing an electrical stimulus to the test subject based on the determined addiction level. How to determine addiction.

7. In paragraph 6, The step of applying electrical stimulation to the subject includes the step of applying electrical stimulation to the subject's auricle. How to determine addiction.

8. In paragraph 6, The step of applying electrical stimulation to the subject further includes a step of applying different strengths of electrical stimulation according to the level of addiction of the subject. How to determine addiction.

9. In paragraph 8, Controlling the intensity of the above electric stimulation includes controlling the intensity of the electric stimulation by controlling the pulse width. How to determine addiction.

10. In paragraph 8, Controlling the intensity of the electrical stimulation includes controlling the intensity of the electrical stimulation according to the size of a preset threshold value. How to determine addiction.

11. Input device for receiving the subject's heart rate variability (HRV) data; A computing device that performs a spectral analysis on the above heart rate variability data and determines the subject's addiction level based on the results of the frequency analysis; and A storage device for storing the above heart rate variability data; including; Analysis device.

12. In paragraph 11, The above input device receives the heart rate data of the subject, The above storage device stores the heart rate data of the subject, When a preset amount of heart rate data is stored in the storage device, the computing device processes the heart rate data stored in the storage device to generate heart rate variability data. Analysis device.

13. In paragraph 11, The above frequency analysis result includes the heart rate variability data converted into a time-frequency image. Analysis device 14. In paragraph 11, The above frequency analysis results include the results of analyzing the ratio of high-frequency and low-frequency regions of the heart rate variability data. Analysis device.

15. In paragraph 11, Determining the above addiction level includes determining the addiction level using an analysis model, The above analysis model is a model that analyzes the level of addiction of the subject based on the frequency analysis results. Analysis device.

16. In paragraph 11, An electrical stimulation device that provides electrical stimulation to the subject based on the determined addiction level of the subject; further comprising; Analysis device.

17. In paragraph 16, Providing electrical stimulation to the subject includes providing electrical stimulation to the subject's auricle. Analysis device.

18. In paragraph 16, Providing electrical stimulation to the subject includes providing different strengths of electrical stimulation according to the subject's level of addiction. Analysis device.

19. In paragraph 18, Controlling the intensity of the above electric stimulation includes controlling the intensity of the electric stimulation by controlling the pulse width. Analysis device.

20. In paragraph 18, Controlling the intensity of the electrical stimulation includes controlling the intensity of the electrical stimulation according to the size of a preset threshold value. Analysis device.

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