An adaptive closed-loop hypnosis bed system

Abstract

The present application relates to the technical field of hypnosis bed, in particular to a self-adaptive closed-loop hypnosis bed system, comprising a bed body, a physiological signal acquisition module, a state evaluation module, a real-time adjustment module, a treatment optimization module and a stimulation execution module. The physiological signal acquisition module is used for collecting physiological parameters of a subject during hypnosis, the state evaluation module is used for generating a state evaluation result according to the physiological parameters, the real-time adjustment module is used for dynamically adjusting stimulation parameters according to the state evaluation result in a single hypnosis process, the treatment optimization module is used for generating a control strategy configuration for the next hypnosis process according to historical state evaluation results and historical stimulation parameters in the previous or previous several hypnosis processes, and the stimulation execution module is used for outputting hypnosis stimulation. The real-time adjustment module constitutes a fast loop, and the treatment optimization module constitutes a slow loop. The present application realizes the combination of real-time adjustment in a single hypnosis process and control strategy updating across treatment courses.

Description

Technical Field

[0001] This invention relates to the field of hypnosis bed technology, specifically an adaptive closed-loop hypnosis bed system. Background Technology

[0002] A hypnosis bed is a device that uses stimulation methods such as sound and vibration to induce relaxation, provide hypnotic intervention, or assist in sleep regulation. It is commonly used in relaxation training, psychological intervention, sleep assistance, and related health management scenarios. Existing hypnosis beds typically deliver preset stimuli to the subject through audio output devices, vibration devices, or other stimulation devices on the bed to induce a relaxed or hypnotic state.

[0003] In existing technologies, some hypnosis beds can collect physiological signals such as heart rate, respiration, and body movement, and adjust stimulation parameters to a certain extent based on the current detection results. However, their adjustment methods are mostly limited to immediate feedback adjustment within a single hypnosis session, usually only making local corrections to stimulation parameters based on changes in the current state. Because different subjects vary in their sensitivity to hypnotic stimuli, the speed of state transition, and the ability to maintain a state, and the response of the same subject in different treatment sessions also exhibits a certain continuity, existing hypnosis beds generally lack comprehensive utilization of historical data from previous hypnosis sessions. They cannot update the control strategy for the next hypnosis session based on changes in state and stimulus response in previous sessions, thus making it difficult to balance real-time adaptability within a single hypnosis session with continuous individualized optimization over multiple treatment sessions.

[0004] Therefore, it is necessary to provide an adaptive closed-loop hypnosis bed system to solve the problem that existing hypnosis beds mainly rely on real-time adjustments within a single hypnosis session and lack cross-treatment control strategy updates. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive closed-loop hypnosis bed system to solve the problems mentioned in the background art.

[0006] This invention is achieved through the following technical solution:

[0007] An adaptive closed-loop hypnosis bed system, comprising:

[0008] Bed frame;

[0009] The physiological signal acquisition module installed on the bed is used to collect the physiological parameters of the subject during hypnosis.

[0010] A state assessment module electrically connected to the physiological signal acquisition module is used to generate a state assessment result representing the current state of the subject based on the physiological parameters.

[0011] The real-time adjustment module, which is electrically connected to the state assessment module, is used to dynamically adjust the stimulation parameters based on the state assessment results during a single hypnosis session, so as to enable the subject's state to enter or remain in the target state range.

[0012] The treatment optimization module, which is electrically connected to the state assessment module and the real-time adjustment module, is used to generate a control strategy configuration for the next hypnosis process based on the historical state assessment results and historical stimulus parameters of the subject in the previous hypnosis process or several previous hypnosis processes.

[0013] The stimulation execution module, which is electrically connected to the real-time adjustment module, is used to output at least one hypnotic stimulus;

[0014] The real-time adjustment module constitutes a fast loop for real-time adjustment within a single hypnosis session, and the treatment optimization module constitutes a slow loop for updating the control strategy across multiple hypnosis sessions. The control strategy configuration output by the slow loop is used to set or constrain the real-time adjustment of the fast loop in the next hypnosis session.

[0015] Optionally, the control strategy configuration includes at least one of the following: target state range, parameter adjustment step size, parameter boundary, and initial stimulus parameters.

[0016] Optionally, the state assessment module includes a feature extraction unit, a feature fusion unit, and a state output unit. The feature extraction unit is used to extract state features corresponding to different physiological parameters respectively. The feature fusion unit is used to fuse different state features. The state output unit is used to output at least one of the following: state score value, state level, or state interval determination result. The state features include at least one of the following: EEG frequency band energy features, heart rate fluctuation features, skin conductance change features, respiratory rhythm stability features, and body movement frequency features.

[0017] Optionally, the real-time adjustment module includes a deviation calculation unit and a control quantity generation unit. The deviation calculation unit is used to generate a deviation value based on the deviation between the state evaluation result and the target state interval. The control quantity generation unit is used to generate a stimulus parameter adjustment amount based on the deviation value. The real-time adjustment module is used to adjust at least one stimulus parameter output by the stimulus execution module according to the stimulus parameter adjustment amount. The stimulus parameter includes at least one of audio beat, audio frequency, vibration frequency, and vibration amplitude.

[0018] Optionally, the treatment optimization module includes a response pattern analysis unit and a strategy generation unit. The response pattern analysis unit is used to determine the subject's response characteristics to different stimulus parameters based on the historical state assessment results and historical stimulus parameters of the previous one or several previous hypnosis processes. The strategy generation unit is used to generate a control strategy configuration for the next hypnosis process based on the response characteristics.

[0019] Optionally, the response pattern analysis unit is used to determine the response characteristics based on at least two of the following historical data: the time required to enter the target state interval, the duration of maintaining the target state interval, the amplitude of state fluctuation, the number of abnormal awakenings, the amplitude of state changes corresponding to each stimulus parameter, and the recovery time after the end of hypnosis.

[0020] Optionally, the update cycle of the slow loop spans at least one hypnosis session and is longer than the update cycle of the fast loop.

[0021] Optionally, the stimulation execution module includes an audio output unit and a bed vibration unit, wherein the beat period output by the audio output unit is the same as, an integer multiple of, or a preset ratio of the vibration period of the bed vibration unit.

[0022] Optionally, the system also includes a storage module for storing historical state assessment results and historical stimulus parameters during multiple hypnosis sessions of the subject.

[0023] Optionally, the system further includes a safety restriction module, which is used to limit the output intensity of the stimulation execution module or stop the output when the physiological parameters exceed a preset safety threshold, the amplitude of state fluctuation exceeds a preset upper limit, the number of abnormal awakenings exceeds a preset number, or the subject triggers a stop command.

[0024] Compared with the prior art, the present invention provides an adaptive closed-loop hypnosis bed system, which has the following beneficial effects:

[0025] 1. This invention enables the system to not only adjust the stimulation parameters in real time based on the current state evaluation results, but also update the control strategy configuration for the next hypnosis process based on the historical state evaluation results and historical stimulation parameters of the previous or several previous hypnosis processes, thereby achieving closed-loop control with dual time scales.

[0026] 2. The treatment optimization module in this invention can set or constrain the target state range, parameter adjustment step size, parameter boundary or initial stimulus parameters for the next hypnosis process based on the subject's historical response in previous hypnosis processes, thereby improving the individual adaptability and continuous optimization ability of the hypnosis control process.

[0027] 3. The real-time adjustment module in this invention can generate stimulation parameter adjustment amount based on the deviation between the state assessment result and the target state range, and dynamically adjust the stimulation parameters accordingly, thereby improving the targeting of adjustment within a single hypnosis session. Attached Figure Description

[0028] Figure 1 This is a block diagram of the overall structure of the adaptive closed-loop hypnosis bed system of the present invention;

[0029] Figure 2 This is a schematic diagram of the physical structure of the adaptive closed-loop hypnosis bed system of the present invention;

[0030] Figure 3 This is a control logic block diagram of the adaptive closed-loop hypnosis bed system of the present invention;

[0031] Figure 4 This is a control flowchart of the adaptive closed-loop hypnosis bed system of the present invention.

[0032] In the diagram: 1. Bed body; 2. Physiological signal acquisition module; 21. EEG acquisition unit; 22. Heart rate acquisition unit; 23. Skin conductance acquisition unit; 24. Respiratory acquisition unit; 25. Body movement acquisition unit; 3. Status assessment module; 31. Feature extraction unit; 32. Feature fusion unit; 33. Status output unit; 4. Real-time adjustment module; 41. Deviation calculation unit; 42. Control quantity generation unit; 5. Treatment course optimization module; 51. Response pattern analysis unit; 52. Strategy generation unit; 6. Stimulus execution module; 61. Audio output unit; 62. Bed vibration unit; 7. Storage module; 8. Safety restriction module; 9. Fast loop; 10. Slow loop. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Reference Figure 1 – Figure 4 As shown, this embodiment provides an adaptive closed-loop hypnosis bed system, including a bed body 1, a physiological signal acquisition module 2, a state assessment module 3, a real-time adjustment module 4, a treatment course optimization module 5, a stimulation execution module 6, a storage module 7, and a safety restriction module 8.

[0035] The system comprises the following modules: Physiological signal acquisition module 2, mounted on bed 1, for collecting physiological parameters of the subject during hypnosis; State assessment module 3, electrically connected to physiological signal acquisition module 2, for generating a state assessment result representing the subject's current state based on the collected physiological parameters; Real-time adjustment module 4, electrically connected to state assessment module 3, for dynamically adjusting stimulation parameters based on the state assessment result during a single hypnosis session; Treatment optimization module 5, electrically connected to state assessment module 3 and real-time adjustment module 4, for generating a control strategy configuration for the next hypnosis session based on the subject's historical state assessment results and historical stimulation parameters from previous hypnosis sessions; Stimulus execution module 6, electrically connected to real-time adjustment module 4, for outputting at least one hypnotic stimulus; Storage module 7 for storing historical state assessment results and historical stimulation parameters from multiple hypnosis sessions; and Safety restriction module 8 for limiting the output intensity of stimulus execution module 6 or stopping its output when an abnormal state occurs.

[0036] In this embodiment, the real-time adjustment module 4 constitutes a fast loop 9 for real-time adjustment within a single hypnosis process, and the treatment optimization module 5 constitutes a slow loop 10 for updating the control strategy across multiple hypnosis processes. The control strategy configuration output by the slow loop 10 is used to set or constrain the real-time adjustment of the fast loop 9 in the next hypnosis process.

[0037] I. System Composition

[0038] 1. Physiological signal acquisition module 2

[0039] In this embodiment, the physiological signal acquisition module 2 acquires at least two of the following physiological parameters: electroencephalogram (EEG) signals, heart rate or heart rate variability signals, skin conductance signals, respiratory signals, and body movement signals. These signals can be acquired via electrodes, contact sensors, breathing belts, pressure sensors, or accelerometers located at corresponding locations on the bed 1.

[0040] 2. Stimulus Execution Module 6

[0041] In this embodiment, the stimulation execution module 6 includes an audio output unit 61 and a bed vibration unit 1. The audio output unit 61 is used to output guiding sounds, rhythmic sounds, or soothing sounds, and the bed vibration unit 1 is used to output periodic vibration stimulation.

[0042] In this embodiment, the beat period output by the audio output unit 61 is the same as, an integer multiple of, or a preset ratio to the vibration period of the vibration unit of the bed 1. For example, the two periods are the same, or the vibration period is twice the audio beat period, or the two periods work in a preset ratio of 1:2, 2:3, or 3:4.

[0043] 3. Status Assessment Module 3

[0044] In this embodiment, the state assessment module 3 includes a feature extraction unit 31, a feature fusion unit 32, and a state output unit 33. The feature extraction unit 31 is used to extract state features corresponding to different physiological parameters; the feature fusion unit 32 is used to fuse different state features; and the state output unit 33 is used to output at least one of the following: state score value, state level, or state interval determination result.

[0045] 4. Real-time adjustment module 4

[0046] In this embodiment, the real-time adjustment module 4 includes a deviation calculation unit 41 and a control quantity generation unit 42. The deviation calculation unit 41 is used to generate a deviation value based on the deviation between the state evaluation result and the target state interval; the control quantity generation unit 42 is used to generate a stimulus parameter adjustment amount based on the deviation value, and adjust the stimulus parameters output by the stimulus execution module 6 according to the adjustment amount.

[0047] 5. Treatment Optimization Module 5

[0048] In this embodiment, the treatment optimization module 5 includes a response pattern analysis unit 51 and a strategy generation unit 52. The response pattern analysis unit 51 is used to determine the subject's response characteristics to different stimulus parameters based on the historical state assessment results and historical stimulus parameters of the previous one or several previous hypnosis processes; the strategy generation unit 52 is used to generate a control strategy configuration for the next hypnosis process based on the response characteristics.

[0049] In this embodiment, the response characteristics refer to the regularity of the subject's state change trend under different stimulus parameters, the speed at which it enters the target state range, the stability of maintaining the target state range, and the sensitivity to changes in stimulus parameters.

[0050] The control strategy configuration includes at least one of the following: target state range, parameter adjustment step size, parameter boundary, and initial stimulus parameters.

[0051] II. State Evaluation Algorithm

[0052] In this embodiment, the state assessment module 3 processes the multimodal physiological parameters to output state score values, state levels, or state interval determination results.

[0053] The feature extraction unit 31 extracts state features from different physiological parameters. The state features include at least: brainwave frequency band energy features, heart rate fluctuation features, skin conductance change features, respiratory rhythm stability features, and body movement frequency features.

[0054] Since the dimensions and value ranges of each state feature are different, this embodiment first performs normalization processing on each state feature. The normalized value Xi' of the i-th type of state feature can be calculated by the following formula:

[0055] Xi'=(Xi-Xi_min) / (Xi_max-Xi_min)

[0056] Where Xi is the currently extracted state feature value, Xi_min and Xi_max are the reference minimum and maximum values ​​of this state feature, respectively, and Xi' is the normalized state feature value. For features that are inversely related to the target state, inverse normalization can also be used.

[0057] Feature fusion unit 32 performs weighted fusion on the normalized state features to generate a state score value S, which can be calculated using the following formula:

[0058] S=w1E'+w2H'+w3G'+w4R'+w5M'

[0059] Wherein, E', H', G', R', and M' represent the normalized EEG characteristic value, heart rate fluctuation characteristic value, skin conductance change characteristic value, respiratory rhythm stability characteristic value, and body movement frequency characteristic value, respectively; w1 to w5 are the corresponding weights, and w1+w2+w3+w4+w5=1. The weights can be preset according to different usage scenarios, for example, w1=0.30, w2=0.15, w3=0.15, w4=0.25, and w5=0.15.

[0060] The status output unit 33 outputs the status determination result based on the status score value S. In this embodiment, two thresholds A and B are set, and A is less than B. When S is less than A, it is determined that the target status interval has not been entered; when S is greater than or equal to A and less than B, it is determined that the target status interval is approaching; when S is greater than or equal to B, it is determined that the target status interval has been entered. To improve the stability of the determination, a consistency verification rule can also be set, that is, the determination result of entering the target status interval is only output when at least two types of status features simultaneously meet the preset trend conditions.

[0061] III. Real-time Adjustment Algorithm for Fast Loop 9

[0062] In this embodiment, the real-time adjustment module 4 periodically receives the state evaluation results during a single hypnosis session and generates stimulation parameter adjustment amounts based on the deviation values ​​to adjust the audio output unit 61 and the vibration unit of the bed 1 in real time.

[0063] In one implementation, using the center value Sref of the target state interval as a reference value, and the current state score value St, the deviation value e is:

[0064] e=St-Sref

[0065] The control quantity generation unit 42 generates the stimulus parameter adjustment amount ΔP based on the deviation value e, which can be calculated using the following formula:

[0066] ΔP=k×e

[0067] Where k is the adjustment coefficient. Different adjustment coefficients can also be used to calculate the audio parameters and vibration parameters separately. The audio parameters include audio beat and audio frequency, and the vibration parameters include vibration frequency and vibration amplitude.

[0068] To improve control stability, a tiered adjustment rule is adopted in this embodiment. Two deviation thresholds, T1 and T2, are set, with T1 being less than T2. ​​When the absolute value of e is less than T1, the current stimulus parameter remains unchanged; when the absolute value of e is greater than or equal to T1 and less than T2, the stimulus parameter is slightly adjusted; and when the absolute value of e is greater than or equal to T2, the stimulus parameter is adjusted more significantly. The stimulus parameter includes at least one of audio beat, audio frequency, vibration frequency, and vibration amplitude.

[0069] In one implementation, when the state score is below the lower limit of the target state range, the audio beat and / or vibration amplitude are increased; when the state score is above the upper limit of the target state range, the audio beat and / or vibration amplitude are decreased; when the state score is within the target state range, the current stimulation parameters are maintained, or fine-tuned in small steps.

[0070] When the real-time adjustment module 4 adjusts the audio beat, it can simultaneously adjust the vibration cycle according to the preset cycle relationship so that the audio output unit 61 and the bed body 1 vibration unit maintain rhythmic coordination.

[0071] IV. Slow Loop 10-Course Treatment Optimization Algorithm

[0072] In this embodiment, the treatment optimization module 5 updates the control strategy configuration for the next hypnosis session based on the historical state assessment results and historical stimulus parameters from the previous hypnosis session or several previous hypnosis sessions. The update cycle of the slow loop 10 spans at least one hypnosis session and is longer than the update cycle of the fast loop 9.

[0073] Storage module 7 records at least two of the following historical data: time required to enter the target state range, duration of maintenance in the target state range, state fluctuation amplitude, number of abnormal awakenings, state change amplitude corresponding to each stimulus parameter, and recovery time after hypnosis. Treatment optimization module 5 reads the above historical data from storage module 7 and analyzes it in conjunction with the stimulus parameters used in the corresponding treatment. The historical state assessment results include the state score, state level, or state range determination results from the previous one or several hypnosis sessions.

[0074] In one implementation, if the time required to enter the target state range during the current treatment is long, it indicates that the current initial stimulation parameters are too weak or the current parameter settings are insufficient to enable the subject to enter the target state range quickly. The initial stimulation parameter Pn+1 for the next treatment can be corrected using the following formula:

[0075] Pn+1 = Pn + α × (Tin - Tref)

[0076] Where Pn is the initial stimulation parameter for the current treatment cycle, Pn+1 is the initial stimulation parameter for the next treatment cycle, Tin is the time required for the current treatment cycle to enter the target state range, Tref is the preset reference time, and α is the correction coefficient. When Tin is greater than Tref, the initial stimulation parameter for the next treatment cycle is increased; when Tin is less than Tref, the initial stimulation parameter for the next treatment cycle is decreased.

[0077] In one embodiment, when the number of abnormal awakenings in two or more consecutive treatment sessions exceeds a preset threshold, the parameter adjustment step size for the next treatment session is reduced, for example, the parameter adjustment step size for the next treatment session is set to 50% to 80% of the current step size.

[0078] In one embodiment, if the duration of maintaining the target state range in the previous treatment or previous treatments is lower than a preset threshold, the target state range for the next treatment is modified, for example, by adjusting the center value of the target state range, narrowing the range of the target state range, or changing the parameter maintenance strategy after entering the target state range.

[0079] In one embodiment, the strategy generation unit 52 can also comprehensively modify the control strategy configuration by considering the time required to enter the target state interval, the duration of maintaining the target state interval, and the number of abnormal wake-ups. A certain control parameter Cn+1 can be calculated by the following formula:

[0080] Cn+1=Cn+β1×(Tin-Tref)+β2×(Wref-W)+β3×(Nwake-Nref)

[0081] Where Cn represents the control parameters for the current treatment cycle, Cn+1 represents the control parameters for the next treatment cycle, W represents the duration of maintaining the target state interval, Wref represents the reference duration, Nwake represents the number of abnormal awakenings, Nref represents the reference number of abnormal awakenings, and β1, β2, and β3 are correction coefficients. Through the above correction rules, at least one of the following can be updated for the next treatment cycle: initial stimulation parameters, parameter adjustment step size, parameter boundaries, and target state interval.

[0082] V. Control Procedures and Safety Limitations

[0083] In this embodiment, the control flow of a single hypnosis session is as follows: the physiological signal acquisition module 2 acquires the subject's current physiological parameters; the state assessment module 3 extracts state features and outputs the state assessment results after normalization and weighted fusion; the real-time adjustment module 4 generates stimulation parameter adjustment amounts based on the deviation between the state assessment results and the target state interval; the stimulation execution module 6 performs linkage adjustment on the audio output unit 61 and the vibration unit of the bed 1 based on the adjustment amounts; the storage module 7 records the state assessment results and stimulation parameters in the current treatment session; after the current treatment session ends, the treatment optimization module 5 reads the historical data of the current and previous or several previous treatment sessions, analyzes the response characteristics, and generates the control strategy configuration for the next treatment session to set or constrain the real-time adjustment of the fast loop 9 in the next hypnosis session.

[0084] In this embodiment, the system also includes a safety restriction module 8. When physiological parameters exceed a preset safety threshold, the amplitude of state fluctuations exceeds a preset upper limit, the number of abnormal awakenings exceeds a preset number, or the subject triggers a stop command, the safety restriction module 8 limits the output intensity of the stimulus execution module 6 or stops the output.

[0085] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive closed-loop hypnosis bed system, characterized in that, include: Bed body (1); The physiological signal acquisition module (2) installed on the bed (1) is used to collect the physiological parameters of the subject during the hypnosis process; The state assessment module (3), which is electrically connected to the physiological signal acquisition module (2), is used to generate a state assessment result representing the current state of the subject based on the physiological parameters. The real-time adjustment module (4), which is electrically connected to the state assessment module (3), is used to dynamically adjust the stimulation parameters according to the state assessment results during a single hypnosis session, so that the subject's state enters or remains in the target state range. The treatment optimization module (5), which is electrically connected to the state assessment module (3) and the real-time adjustment module (4), is used to generate the control strategy configuration for the next hypnosis process based on the historical state assessment results and historical stimulation parameters of the subject in the previous hypnosis process or several previous hypnosis processes. The stimulation execution module (6), which is electrically connected to the real-time adjustment module (4), is used to output at least one hypnotic stimulus; The real-time adjustment module (4) constitutes a fast loop (9) for real-time adjustment within a single hypnosis process, and the treatment optimization module (5) constitutes a slow loop (10) for updating the control strategy across multiple hypnosis processes. The control strategy output by the slow loop (10) is configured to set or constrain the real-time adjustment of the fast loop (9) in the next hypnosis process.

2. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The control strategy configuration includes at least one of the following: target state range, parameter adjustment step size, parameter boundary, and initial stimulus parameters.

3. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The state assessment module (3) includes a feature extraction unit (31), a feature fusion unit (32), and a state output unit (33). The feature extraction unit (31) is used to extract state features corresponding to different physiological parameters. The feature fusion unit (32) is used to fuse different state features. The state output unit (33) is used to output at least one of the state score, state level, or state interval determination results. The state features include at least one of the following: brainwave frequency band energy features, heart rate fluctuation features, skin conductance change features, respiratory rhythm stability features, and body movement frequency features.

4. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The real-time adjustment module (4) includes a deviation calculation unit (41) and a control quantity generation unit (42). The deviation calculation unit (41) is used to generate a deviation value based on the deviation between the state evaluation result and the target state interval. The control quantity generation unit (42) is used to generate a stimulus parameter adjustment amount based on the deviation value. The real-time adjustment module (4) is used to adjust at least one stimulus parameter output by the stimulus execution module (6) according to the stimulus parameter adjustment amount. The stimulus parameter includes at least one of audio beat, audio frequency, vibration frequency and vibration amplitude.

5. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The treatment optimization module (5) includes a response pattern analysis unit (51) and a strategy generation unit (52). The response pattern analysis unit (51) is used to determine the subject's response characteristics to different stimulus parameters based on the historical state assessment results and historical stimulus parameters of the previous hypnosis process or several previous hypnosis processes. The strategy generation unit (52) is used to generate the control strategy configuration for the next hypnosis process based on the response characteristics.

6. The adaptive closed-loop hypnosis bed system according to claim 5, characterized in that, The response pattern analysis unit (51) is used to determine the response characteristics based on at least two historical data: the time required to enter the target state interval, the duration of maintaining the target state interval, the amplitude of state fluctuation, the number of abnormal awakenings, the amplitude of state change corresponding to each stimulus parameter, and the recovery time after the end of hypnosis.

7. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The update cycle of the slow loop (10) spans at least one hypnosis session and is longer than the update cycle of the fast loop (9).

8. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The stimulation execution module (6) includes an audio output unit (61) and a bed (1) vibration unit. The beat period output by the audio output unit (61) is the same as, an integer multiple of, or a preset ratio of the vibration period of the bed (1) vibration unit.

9. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The system also includes a storage module (7) for storing historical state assessment results and historical stimulus parameters during multiple hypnosis sessions of the subject.

10. The adaptive closed-loop hypnosis bed system according to claim 1, characterized in that, The system also includes a safety restriction module (8), which is used to limit the output intensity of the stimulation execution module (6) or stop the output when the physiological parameters exceed the preset safety threshold, the amplitude of the state fluctuation exceeds the preset upper limit, the number of abnormal awakenings exceeds the preset number, or the subject triggers a stop command.

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