Sound-light-electricity multi-physics field stimulation sleep modulator based on resonant frequency

Abstract

The invention discloses an acoustic-optical-electric multi-physics field stimulation sleep modulator based on resonant frequency. The acoustic-optical-electric multi-physics field stimulation sleep modulator comprises a system control module, a transcutaneous electrical stimulation module, an acoustic stimulation module, a near-infrared light stimulation module and an electroencephalogram data acquisition module, the system control module is in control connection with the transcutaneous electrical stimulation module, the sound stimulation module, the near-infrared light stimulation module and the electroencephalogram data acquisition module, the transcutaneous electrical stimulation module is located on the neck, and electrical stimulation is conducted through weak current with sine wave superposition waveforms of all frequencies within 135-155 Hz. The sound stimulation module uses two same miniature loudspeakers to be arranged on the two ear sides respectively and plays sound waves of sine wave superposition waveforms with the frequencies ranging from 135 Hz to 155 Hz, the light stimulation module is located on the forehead and uses near-infrared light of sine wave superposition waveforms with the frequencies ranging from 135 Hz to 155 Hz to conduct stimulation, and a red LED is configured to be used for indicating lamps. The electroencephalogram data acquisition module is provided with five electrode plates which are transversely arranged side by side and are connected with the forehead. The method can assist in regulating the brain region activity of the user, improves the sleep quality of the user, and can provide a theoretical basis for selection of stimulation parameters of sleep modulation.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep health care, and particularly relates to an acoustic-optical multi-physical field stimulation sleep modulator based on resonance frequency. Background Art

[0002] For sleep disorder problems, clinically, most treatments adopt drug methods including using sedatives, hypnotics, etc. and cognitive behavioral therapy methods such as hypnosis. These treatment methods have limitations and cause side effects and dependencies. Therefore, the physical field modulation method is considered a new modulation method for improving sleep disorder problems due to its advantages such as safety, long-term applicability, and obvious modulation effect. Currently, the commonly used physical field modulation methods include light stimulation method, transcutaneous electrical stimulation method, and sound stimulation method.

[0003] Currently, it has been found that sound stimulation, light stimulation, and transcutaneous electrical stimulation all have effective regulatory effects on sleep, but there are few studies on the comprehensive application of two or more multi-physical field modulation methods. At the same time, there is no generally recognized unified standard for the stimulation methods of physical field modulation of sleep, and the stimulation parameters lack theoretical basis support. Therefore, an acoustic-optical multi-physical field stimulation sleep modulator designed based on the resonance frequency theory is needed. Summary of the Invention

[0004] Object of the Invention: The present invention provides an acoustic-optical multi-physical field stimulation sleep modulator based on resonance frequency, which can assist in regulating the brain region activities of users, improve the sleep quality of users, and provide a theoretical basis for the selection of stimulation parameters for sleep modulation.

[0005] Technical Solution: An acoustic-optical multi-physical field stimulation sleep modulator based on resonance frequency according to the present invention includes: a system control module, a transcutaneous electrical stimulation module, a sound stimulation module, a near-infrared light stimulation module, and an electroencephalogram data acquisition module; the system control module is respectively connected to the transcutaneous electrical stimulation module, the sound stimulation module, the near-infrared light stimulation module, and the electroencephalogram data acquisition module in a control manner. The transcutaneous electrical stimulation module is located at the neck and performs electrical stimulation through a weak current of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz. The sound stimulation module uses two identical miniature speakers respectively placed on both sides of the ears to play sound waves of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz. The light stimulation module is located at the forehead and uses near-infrared light of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz for stimulation, and is configured with a red LED for indicating. The electroencephalogram data acquisition module is designed with a total of five electrode patches arranged side by side horizontally and connected to the forehead.

[0006] Further, the system control module includes a main body housing, a power module, an EEG data storage module, a Bluetooth connection module, a stimulation control module, and a central control module; the power module, the EEG data storage module, the Bluetooth connection module, the stimulation control module, and the central control module are all arranged inside the main body housing, and the central control module is respectively connected to the power module, the EEG data storage module, the Bluetooth connection module, and the stimulation control module.

[0007] Further, in the EEG data acquisition module, the central electrode patch is used to control the start and stop of the acquisition, the two adjacent electrode patches are used for grounding, and the two outermost electrode patches are used for acquiring EEG. The acquired EEG points are FP1 and FP2; the acquired EEG signals are the signals of FP1 and FP2 at the electrode positions of the international 10-20 standard.

[0008] Further, the electrode area of the transcutaneous electrical stimulation module uses a reticulated conductive sponge sheet, and the inner side of the conductive sponge sheet is connected to the electrode patch.

[0009] Further, the transcutaneous electrical stimulation module is located at the neck and performs electrical stimulation through a weak current of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz. The specific steps are as follows:

[0010] Step 1: Use a 4th-order Butterworth filter to perform band-pass filtering on high-frequency signals of 120 - 180 Hz;

[0011] Step 2: Apply independent component analysis technology to remove EOG artifacts;

[0012] Step 3: Standardize the preprocessed data to ensure that the data in each time window has the same scale and unit, thereby improving the accuracy and consistency of the results;

[0013] Step 4: Divide the preprocessed data into several equal-length time windows, and the data division process uses a sliding window technique;

[0014] Step 5: Perform Fourier transform on x(t) in each time window, use short-time Fourier transform to analyze the signal, obtain time-frequency distribution information, and perform peak identification;

[0015] Step 6: Further analyze the peak spectrum through Morlet wavelet transform, and then enhance the accuracy of peak detection through moving average;

[0016] Step 7: In each time window, select the frequency with the highest energy from the temporarily stored peaks as the resonant frequency of this time window;

[0017] Step 8: Summarize the resonant frequencies of all windows to obtain a set of resonant frequencies, then find the maximum resonant frequency and the minimum resonant frequency in the set, and calculate the bandwidth Δf;

[0018] Step 9: Determine the sub-intervals of the frequency bandwidth to which each resonant frequency belongs;

[0019] Step 10: Count the number of resonant frequencies that appear in each sub-interval, and then select the frequency bandwidth sub-interval with the highest number of occurrences - 135 - 155 Hz as the resonant frequency to be used.

[0020] Furthermore, in Step 1, a 4th-order Butterworth filter is used for band-pass filtering of the high-frequency signal of 120 - 180 Hz. Assuming the original EEG signal is x(t), the band-pass filtered signal is:

[0021] y(t) = H bp (x(t))

[0022] where H bp represents the transfer function of the band-pass filter, expressed as:

[0023]

[0024] where f low is the lower cut-off frequency of the filter, f high is the upper cut-off frequency of the filter, n is the order of the filter, and f is the frequency of the signal.

[0025] Furthermore, in Step 5, the Fourier transform is performed on x(t) in each time window:

[0026]

[0027] PSD(f) = |X(f)| 2

[0028] where X(f) is the result of the Fourier transform of the signal x(t);

[0029] The short-time Fourier transform is used to analyze the signal to obtain the time-frequency distribution information:

[0030]

[0031] where ω(τ - t) is used to limit the time period of the signal, x(τ) is the time-domain representation of the signal, X(t, f) is the result of the short-time Fourier transform at time t and frequency f, and then the calculation of PSD can be performed:

[0032] PSD(t, f) = |X(t, f)| 2

[0033] If for any frequency f i , the following mathematical expression can be achieved, it is regarded as a peak:

[0034] P(f i ) > P(f i"3 ) and P(f i ) > P(f i+3 )

[0035] where P(f i ) represents the PSD value corresponding to the frequency f i .

[0036] Furthermore, in step 6, the peak spectrum is further analyzed by Morlet wavelet transform, and then the accuracy of peak detection is enhanced by moving average:

[0037]

[0038] where MA(t) is the moving average value at time t, and n is the length of the moving window.

[0039] Furthermore, in step 8, the resonant frequencies of all windows are summarized to obtain a set of resonant frequencies. Then, the maximum and minimum resonant frequencies in the set are found, and the bandwidth Δf is calculated. Δf is divided into g equally wide sub - intervals, and the width of each sub - interval is

[0040]

[0041] Furthermore, in step 9, the frequency bandwidth sub - interval to which each resonant frequency belongs is determined. For the i - th resonant frequency the index of the frequency bandwidth sub - interval to which it belongs is:

[0042]

[0043] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: The present invention can adjust the discharge of the sleep - related nuclei in the user's brain and the secretion of hormones in the body, etc., adjust the brain activity frequency of users with poor sleep quality to the range of resonant frequencies, induce brain synchronous rhythm signals, modulate the brain activity during the user's sleep, improve the user's sleep quality, and at the same time collect the electrophysiological activity signal data of the user during sleep for further analysis; The present invention can effectively regulate the brain region activity of users, improve the user's sleep quality, and provide a theoretical basis for the selection of stimulation parameters for sleep modulation. Description of the Drawings

[0044] Figure 1 is a physical diagram of the sleep modulation instrument based on resonant frequency of the present invention.

[0045] Figure 2 is a framework diagram of the modulation instrument of the present invention.

[0046] Figure 3 This is a schematic structural diagram of the modulation instrument control module of the present invention.

[0047] Figure 4 This is a schematic hardware structure diagram of the present invention. Specific embodiments

[0048] As Figures 1-4 shown, an acousto-opto-electro multi-physical field stimulation sleep modulation instrument based on resonance frequency includes: a system control module, a transcutaneous electrical stimulation module, an acoustic stimulation module, a near-infrared light stimulation module, and an electroencephalogram data acquisition module; the system control module is respectively connected to the transcutaneous electrical stimulation module, the acoustic stimulation module, the near-infrared light stimulation module, and the electroencephalogram data acquisition module for control connection. The transcutaneous electrical stimulation module is located at the neck and performs electrical stimulation through a weak current of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz. The acoustic stimulation module uses two identical miniature speakers, which are respectively placed on both sides of the ears, to play sound waves of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz. The light stimulation module is located on the forehead and uses near-infrared light of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz for stimulation, and is configured with a red LED for indicating. The electroencephalogram data acquisition module is designed with a total of five horizontally arranged electrode patches connected to the forehead.

[0049] The system control module includes a main machine housing, a power supply module, an electroencephalogram data storage module, a Bluetooth connection module, a stimulation control module, and a central control module; the power supply module, the electroencephalogram data storage module, the Bluetooth connection module, the stimulation control module, and the central control module are all arranged inside the main machine housing, and the central control module is respectively connected to the power supply module, the electroencephalogram data storage module, the Bluetooth connection module, and the stimulation control module.

[0050] In the electroencephalogram data acquisition module, the central electrode patch is used to control the start and stop of acquisition, the two adjacent electrode patches are used for grounding, and the two outermost electrode patches are used for collecting electroencephalogram. The collected electroencephalogram point is the average value of the signal at two points FP1 and FP2; the collected electroencephalogram signal is the average value of the signal at two points FP1 and FP2 in the international 10 - 20 standard electrode positions.

[0051] The electrode area of the transcutaneous electrical stimulation module uses a reticulated conductive sponge sheet, and the inner side of the conductive sponge sheet is connected to the electrode patch.

[0052] The transcutaneous electrical stimulation module is located at the neck and performs electrical stimulation through a weak current of a superimposed waveform of sine waves at various frequencies within 135 - 155 Hz, and specifically includes the following steps:

[0053] Step 1: Use a 4th-order Butterworth filter to perform band-pass filtering on the 120 - 180 Hz high-frequency signal. Assuming the original electroencephalogram signal is x(t), the signal after band-pass filtering is:

[0054] y(t) = H bp (x(t))

[0055] where H bp represents the transfer function of the band - pass filter, expressed as:

[0056]

[0057] where f low is the lower cut - off frequency of the filter, f high is the upper cut - off frequency of the filter, n is the order of the filter, and f is the frequency of the signal.

[0058] Step 2: Apply independent component analysis technology to remove EOG artifacts to ensure that the signal mainly reflects the brain's endogenous neural activities:

[0059] Z = A^- 1 y(t)

[0060] where A is the mixing matrix and Z is the independent component.

[0061] Step 3: Normalize the pre - processed data to ensure that the data in each time window has the same scale and unit, thereby improving the accuracy and consistency of the results.

[0062]

[0063] where μ(t) is the average value of the signal within the time window, σ(t) is the standard deviation of the signal within the time window, and s(t) is the normalization of the signal.

[0064] Step 4: Segment the pre - processed data into several equal - length time windows. The data segmentation process uses the sliding window technique:

[0065] x(t) = x[nT : + kΔt]

[0066] where the data of each time window is represented as x(t), T : is the length of the time window, Δt is the step size of the sliding window, and k is the window number.

[0067] Step 5: Perform Fourier transform on x(t) in each time window:

[0068]

[0069] PSD(f) = |X(f)|^ 2

[0070] where X(f) is the result of the Fourier transform of the signal x(t).

[0071] Analyze the signal using the short-time Fourier transform to obtain time-frequency distribution information:

[0072]

[0073] Among them, ω(τ - t) is used to limit the time period of the signal, x(τ) is the time-domain representation of the signal, and X(t, f) is the result of the short-time Fourier transform at time t and frequency f. Furthermore, the calculation of PSD can be performed:

[0074] PSD(t, f) = |X(t, f)| 2

[0075] Peak identification:

[0076] If for any frequency f i , the following mathematical expression can be achieved, it is regarded as a peak:

[0077] P(f i ) > P(f i"3 ) and P(f i ) > P(f i+3 )

[0078] Among them, P(f i ) represents the PSD value corresponding to frequency f i .

[0079] Step 6: Further analyze the peak spectrum through Morlet wavelet transform, and then enhance the accuracy of peak detection through moving average:

[0080]

[0081] Among them, MA(t) is the moving average value at time t, and n is the length of the moving window.

[0082] Step 7: In each time window, select the frequency with the highest energy from the temporarily stored peaks as the resonant frequency of this time window.

[0083] Step 8: Summarize the resonant frequencies of all windows to obtain a set of resonant frequencies. Then find the maximum and minimum resonant frequencies in the set, and calculate the bandwidth Δf. Divide Δf into g equally wide sub-intervals, and the width of each sub-interval is

[0084]

[0085] Step 9: Determine the frequency bandwidth sub-interval to which each resonant frequency belongs. For the i-th resonant frequency The index of the frequency bandwidth sub-interval to which it belongs is:

[0086]

[0087] Step 10: Count the number of resonant frequencies that appear in each sub-interval, and then select the frequency bandwidth sub-interval with the most occurrences - 135 - 155 Hz as the resonant frequency to be used.

Claims

1. An acoustic, optical and electrical multi-physical field stimulation sleep modulator based on resonant frequency, characterized in that: include: System control module, transcutaneous electrical stimulation module, acoustic stimulation module, near-infrared light stimulation module and EEG data acquisition module; the system control module is controlled and connected with the transcutaneous electrical stimulation module, acoustic stimulation module, near-infrared light stimulation module and EEG data acquisition module respectively; the transcutaneous electrical stimulation module is located at the neck, and electrical stimulation is performed through a weak current of a superimposed waveform of sine waves of various frequencies within 135-155 Hz; the acoustic stimulation module uses two identical micro-speakers respectively placed on both ear sides to play sound waves of a superimposed waveform of sine waves of various frequencies within 135-155 Hz; the light stimulation module is located at the forehead, and near-infrared light of a superimposed waveform of sine waves of various frequencies within 135-155 Hz is used for stimulation, and a red LED is configured for indicator light; the EEG data acquisition module is designed with a total of five horizontally arranged electrode sheets connected to the forehead.

2. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency according to claim 1, characterized in that: The system control module includes a host shell, a power module, an EEG data storage module, a Bluetooth connection module, a stimulation control module and a central control module; the power module, the EEG data storage module, the Bluetooth connection module, the stimulation control module and the central control module are all arranged in the host shell, and the central control module is respectively connected to the power module, the EEG data storage module, the Bluetooth connection module and the stimulation control module.

3. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency as claimed in claim 1, characterized in that: In the EEG data acquisition module, the central electrode is used to control the opening and closing of the acquisition, the two adjacent electrodes are used for grounding, and the two outermost electrodes are used to collect EEG. The collected EEG points are FP1 and FP2 point signals respectively; the collected EEG signals are FP1 and FP2 point signals in the international 10-20 standard electrode positions.

4. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency as claimed in claim 1, characterized in that: The electrode area of ​​the transcutaneous electrical stimulation module uses a mesh conductive sponge sheet, and the inner side of the conductive sponge sheet is connected to the electrode sheet.

5. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency as claimed in claim 1, characterized in that: The transcutaneous electrical stimulation module is located in the neck and uses a weak current of a superimposed waveform of sine waves of various frequencies within 135-155Hz for electrical stimulation. The specific steps include the following: Step 1: Use a 4th-order Butterworth filter to bandpass filter the 120-180 Hz high frequency signal; Step 2: Apply independent component analysis to remove EOG artifacts; Step 3: Standardize the preprocessed data to ensure that the data in each time window has the same scale and unit, thereby improving the accuracy and consistency of the results; Step 4: Divide the preprocessed data into several time windows of equal length, and use the sliding window technology in the data segmentation process; Step 5: Perform Fourier transform on x(t) in each time window, use short-time Fourier transform to analyze the signal, obtain time-frequency distribution information, and perform peak recognition; Step 6: further analyze the peak spectrum through Morlet wavelet transform, and then enhance the accuracy of peak detection through moving average; Step 7: In each time window, select the frequency with the highest energy from the temporarily stored peak values ​​as the resonant frequency of the time window; Step 8: Summarize the resonant frequencies of all windows to obtain a set of resonant frequencies, then find the maximum resonant frequency and the minimum resonant frequency in the set, and calculate the bandwidth Δf; Step 9, determining the frequency bandwidth sub-interval to which each resonant frequency belongs; Step 10: Count the number of resonant frequencies in each sub-interval, and then select the frequency bandwidth sub-interval with the largest number of resonant frequencies, 135-155 Hz, as the resonant frequency to be used.

6. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency as claimed in claim 1, characterized in that: In step 1, a 4th-order Butterworth filter is used to bandpass filter the 120-180 Hz high-frequency signal. Assuming that the original EEG signal is x(t), the signal after bandpass filtering is: y(t)=H bp (x(t)) Among them, H bp represents the transfer function of the bandpass filter, expressed as: Among them, f low is the lower cutoff frequency of the filter, f high is the upper cutoff frequency of the filter, n is the order of the filter, and f is the frequency of the signal.

7. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency as claimed in claim 1, characterized in that: In step 5, Fourier transform is performed on x(t) in each time window: PSD(f)=|X(f)| 2 Where X(f) is the Fourier transform result of the signal x(t); Use short-time Fourier transform to analyze the signal and obtain time-frequency distribution information: Among them, ω(τ-t) is used to limit the time period of the signal, x(τ) is the time domain representation of the signal, and X(t,f) is the short-time Fourier transform result at time t and frequency f, which can then be used to calculate the PSD: PSD(t,f)=|X(t,f)| 2 If for any frequency f i , it is considered as a peak value if the following mathematical expression can be realized: P(f i )>P(f i"3 ) and P(f i )>P(f i+3 ) Among them, P(f i ) represents the frequency f i The corresponding PSD value.

8. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency as claimed in claim 1, characterized in that: In step 6, the peak spectrum is further analyzed by Morlet wavelet transform, and then the accuracy of peak detection is enhanced by moving average: Among them, MA(t) is the moving average at time t, and n is the length of the moving window.

9. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency as claimed in claim 1, characterized in that: In step 8, the resonant frequencies of all windows are summarized to obtain a set of resonant frequencies. Then, the maximum and minimum resonant frequencies in the set are found, and the bandwidth Δf is calculated. Δf is divided into g sub-intervals of equal width, and the width of each sub-interval is 10. The acoustic-optical-electrical multi-physical field stimulation sleep modulator based on resonance frequency according to claim 1, characterized in that: In step 9, the frequency bandwidth sub-interval to which each resonant frequency belongs is determined. For the i-th resonant frequency The frequency bandwidth subinterval index to which it belongs is:

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