Physiological signal long-distance two-way transmission processing system and method

Abstract

The present invention proposes a physiological signal long-distance two-way transmission processing system and method for a closed-loop system, comprising: generating a signal at a user end of the closed-loop system, compressing the signal to generate a compressed signal; transmitting the compressed signal at the user end to a computing end of the closed-loop system; receiving and comparing the compressed signal with a database at the computing end to generate a comparison result and a feedback signal; and transmitting the feedback signal from the computing end to the user end. The time interval between the user end generating the signal and receiving the feedback signal is less than a threshold value.

Description

Technical Field

[0001] The present invention relates to a physiological signal long-distance transmission method, in particular to a physiological signal long-distance two-way transmission processing system and method for a closed-loop system. Background Art

[0002] Existing biofeedback training is mainly done through wireless devices at the input end, such as using a pair of electrode patches to compare brain wave changes before and after training in three areas of the parietal lobe, using a pair of electrode patches to detect the impact of neurophysiological feedback on sensorimotor rhythm (SMR), or collecting physiological signals, and uploading physiological data to a cloud platform for analysis via a wired or wireless transmission module. The user needs to open an APP or related application to read the physiological device during sleep in a retrospective manner. However, existing technologies usually prevent users from immediately obtaining physiological information such as brain waves or heartbeat variations, and they need to wait for several hours to days for interpretation.

[0003] Therefore, there is a need to propose improved methods and systems that can provide remote real-time feedback so that users can immediately understand their own conditions and adjust their physiological signals back to normal through visual or auditory feedback. Summary of the invention

[0004] In order to effectively solve the above-mentioned problems, the present invention proposes a physiological signal long-distance two-way transmission processing system, comprising: a user end, comprising: an electroencephalogram cap, a processing unit, an output unit and a long-distance transmission module, wherein the processing unit receives the electroencephalogram cap to detect the user's brain electroencephalogram physiological signal, and selects one of a plurality of signal transmission modes to process the electroencephalogram physiological signal, and transmits the electroencephalogram physiological signal to a cloud network through the long-distance transmission module, and the processing unit receives a comparison feedback signal through the long-distance transmission module, and the comparison feedback signal is output by the output unit as a comparison result; and a computing end, comprising: a cloud server and a brain wave physiological database, wherein the cloud server receives the brain wave physiological signal from the cloud network, and selects one of a plurality of signal comparison modes to process the brain wave physiological signal, generates the comparison feedback signal according to the brain wave physiological database, and transmits the comparison feedback signal to the user end; wherein the user end selects one of a plurality of signal transmission modes to process and transmit the brain wave physiological signal and the computing end selects one of a plurality of signal comparison modes to process the brain wave physiological signal and transmit the comparison feedback signal are processed simultaneously.

[0005] According to an embodiment of the present invention, the user end uses prediction (probability) calculation compression to generate a combination result through the correspondence between the waveforms of the brain wave physiological signal to compress the brain wave physiological signal.

[0006] According to an embodiment of the present invention, the user end uses numerical compression to compress the brain wave physiological signal through the difference vector angle and difference vector size between consecutive values ​​on the waveform curve of the brain wave physiological signal in the same channel.

[0007] According to an embodiment of the present invention, the user terminal uses block compression to compress the brain wave physiological signals having the same characteristics as the same encoding data.

[0008] According to an embodiment of the present invention, the user end uses shape compression to compress the brain wave physiological signal through the image static base value and image displacement of the difference between the waveforms of the brain wave physiological signal in different channels.

[0009] In order to effectively solve the above problems, the present invention further proposes a closed-loop system, which includes a user end and a computing end. The user end generates a signal and compresses the signal to generate and transmit a compressed signal. The computing end receives and compares the compressed signal to generate and transmit a feedback signal to the user end. The time interval between the user end generating the signal and receiving the feedback signal is less than a threshold value.

[0010] By using the physiological signal long-distance two-way transmission processing system of the present invention, the evaluation efficiency can be improved, and remote real-time feedback can be achieved in the biofeedback training system, so that the user can immediately understand his or her own condition, and through feedback, the user can adjust his or her own physiological signals to return to normal. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram showing a closed loop system according to an embodiment of the present invention; and

[0012] Figures 2 to 5 Schematic diagrams showing compression methods according to different embodiments of the present invention.

[0013] Description of reference numerals:

[0014] 1- Physiological signal long-distance two-way transmission and processing system of closed-loop system;

[0015] 10- User end;

[0016] 11-Computing end. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] Please refer to Figure 1 , Figure 1 A schematic diagram of a physiological signal long-distance bidirectional transmission processing system 1 of a closed-loop system according to an embodiment of the present invention is shown. Figure 1As shown, the system 1 includes a user end 10 and a computing end 11. The user end 10 compresses and transmits a compressed signal to the computing end 11 through a compression mode M1 / M2 / M3 / M4, and then the computing end 11 transmits a feedback signal Sf back to the user end 10. The method for long-distance transmission of physiological signals used includes the following steps: generating a signal at the user end 10 of the closed-loop system 1, compressing the signal to generate a compressed signal; transmitting the compressed signal to the computing end 11 of the system 1 at the user end 10; receiving and comparing the compressed signal with a database at the computing end 11, so as to generate a comparison result and the feedback signal Sf through machine learning and artificial intelligence methods, so as to reduce the data transmission between the computing end 11 and the user end 10. For example, when the system 1 is used for biofeedback training, the biological indicator of the signal is compared with a detection database containing brain wave and heart rate variability data to generate a comparison result and the feedback signal Sf; and the computing end 11 transmits the feedback signal Sf to the user end 10. The time interval between the user end 10 generating the signal and receiving the feedback signal Sf is less than the threshold value, which is usually within 3 seconds, but can also be set to 5 seconds, 10 seconds, 20 seconds, 30 seconds depending on the situation, and no more than 30 seconds is used as a benchmark. It is a schematic diagram of the compression method according to the embodiment of the present invention corresponding to the compression mode M1 / M2 / M3 / M4. Figure 2 As shown, in this embodiment, the user terminal 10 uses compression mode M1, which uses a prediction (probability) calculation compression to compress the signal by generating a combination result through the corresponding relationship between the waveforms of the signal. Corresponding similar waveforms b1, waveform b2, waveform b3, etc. will appear in channel Channel-A and channel Channel-B. Corresponding similar waveforms c1, waveform c2, waveform c3, etc. will appear in channel Channel-B and channel Channel-C. Figure 2The compression method is mainly to compare the feedback of the user end 10 and the computing end 11, with the intention of reducing the data transmission between the computing end 11 and the user end 10, wherein the comparison of the feedback signal Sf is through machine learning and artificial intelligence methods to find the most suitable mode and then give feedback. Because it is limited by the physical limitations of the human body (nerve conduction, muscle operation, etc. are all within a certain range and affect each other), the difference comparison compression method between channels requires a long-term analysis of the database. The above waveform is a waveform with a higher probability of occurrence. If a waveform different from the above appears, the different waveform will be defined as noise or ignored. Therefore, when the signal transmitted in this mode is transmitted and compared, the waveform with the highest probability of occurrence will be predicted and further calculated to generate the feedback signal Sf. For example, taking the pinball machine as an example, after a marble is released, it will go left or right when it encounters the first nail, but the force of the initial ejection can predict the probability of encountering the first nail, and then the probability of the second nail will be predicted. Finally, the marble will definitely fall into the groove at the bottom. The probability of exceeding the groove is very small and is affected by other irresistible factors. After receiving the brain wave signal from the user, there are different sites on the brain wave cap. For example, if the 19-channel is used, the brain wave of channel Channel-A may produce different probability distributions such as b1, b2, b3...bx after spectrum analysis. If the brain wave signal with probability b2 from channel Channel-A is received in channel Channel-B, then the brain wave signal with probability c5 from channel Channel-B may be received in channel Channel-C, and so on. Such a pattern will fall into the style of a certain type of behavior or mental characteristics in the database.

[0019] like Figure 3As shown, in this embodiment, the user end 10 uses compression mode M2, which uses numerical compression, and compresses the signal by the difference vector angle and difference vector size between the continuous values ​​on the waveform curve of the signal in the same channel. The difference comparison object refers to the comparison between the sampled values ​​in the continuous time points, the comparison between the sampled value at the time point t(n+k) and the sampled value at the time point t(n). The larger the k value, the larger the difference between the two sampling times. When the waveform does not change much, the larger the k value will help to improve the compression ratio, improve the compression rate and reduce the transmission value. Here, the waveform is predicted by positioning the reference value and using the vector angle and size values ​​to depict the waveform. Channel Channel-A defines vector 1, channel Channel-B defines vector 2, and channel Channel-C defines vector 3... and so on. Taking binary as an example, 23=8, there are eight changes in total, "000", "001", "010", "100", "011", "101", "110", and "111". At the time point of brain wave physiological signal acquisition, a specific bit count is given as a baseline value, and subsequent brain waves can be described by difference vectors. Therefore, when the user's physiological signal or brain wave signal is collected, after the factor characteristics are detected, the subsequent physiological signal characteristics are compared with the vector angle change of the subsequent data and the positive and negative value changes. Figure 3 The compression mode M2 ​​mainly uses the difference between each sample in each channel, and each channel can use the compression mode M2. Figure 2 The compression mode M1 is for comparison between different channels.

[0020] like Figure 4 As shown, in this embodiment, the user end 10 uses compression mode M3, which is block compression, and compresses the signal with the same characteristics as the same coded data. Here, the morphological characteristics are defined for the signal, a large number of signals are divided into blocks, and template characteristics are defined for the blocks, such as template feature Pattern-A. Blocks with the same template characteristics in different channels will be compressed into coded data, so that the same template characteristics can be transmitted as the same coded data during the transmission and calculation process. Figure 4 The blocks that meet the template feature Pattern-A will be represented by the same encoded data, and the signal content that originally occupied four large areas will only become one image, achieving a compression ratio of 4:1.

[0021] like Figure 5As shown, in this embodiment, the user end 10 uses compression mode M4, which uses four-point three-segment compression, and uses a specific sampling frequency to mark the signal waveforms on different channels Channel-A~Channel-D. In this embodiment, one mark is given to every four points, and every four points will produce three intervals. Three line segment features on the three intervals can be extracted, and the original waveform can be depicted through the three line segment features corresponding to the four points P1~P4 and the horizontal segments L1~L3. In this way, by transmitting the four points and three line segment features, the computing end 11 can be compared with the database.

[0022] In the present invention, after the user end 10 transmits the signal, it needs to be compared with the database. The comparison can be performed at the computing end 11. Then, the feedback signal Sf needs to be sent back to the user end 10. The user end 10 also continues to generate brain waves or other physiological signals at the same time, forming a closed-loop feedback mechanism. The user end 10 is actually comparing the signal while generating the signal, and is also receiving the feedback signal Sf for adjustment. For example, when the brain waves of the subject are compared with the database, if the ratio of a certain feature type meets a certain standard, the subject is given sound or visual feedback through a mobile phone, TV or computer screen, so that the subject can understand the current brain wave status and reflect the real-time status of the brain waves of the subject converted into sound or visual feedback signals, so as to achieve the purpose of brain training. Therefore, in terms of transmission and calculation comparison technology, the "transmission efficiency" is higher and the "comparison efficiency" is faster. The present invention uses the above-mentioned M1 / M2 / M3 / M4 modes for signal transmission and comparison, which can quickly provide feedback on the user's physiological signals, especially the comparison and calculation characteristics of brain waves. The brain areas and morphological arrangements and combinations that may be included in the signal can be represented by up to tens of millions to hundreds of millions of possible waveforms. For example, the present invention can be used to select the most suitable comparison strategy for the M1 / M2 / M3 / M4 modes according to different ethnic groups (demographic variables such as gender, age, and education level), different behavioral performances or mental functions. For example, for subjects with memory degeneration problems, the best comparison order may be mode M4, mode M1, mode M2, mode M3; for subjects with attention deficit hyperactivity disorder symptoms, the best comparison order may be mode M2, mode M1, mode M4, mode M3; for subjects who want to improve their motor reaction ability, they may only need to use the comparison of mode M1.

[0023] By using the method for long-distance transmission of physiological signals for a closed-loop system of the present invention, in addition to preventing distortion of the long-distance transmitted signals, the signals can also be compared and then transmitted back. Generally, complex physiological signals (such as EEG brain waves) usually take a while to calculate the results, but the method of the present invention can transmit, compare and feedback more quickly within an allowable range (the goal of the present invention is to maintain the delay within 3 seconds, but 30 seconds is also an allowable range), so the evaluation efficiency can be improved, and remote real-time feedback can be achieved in the biofeedback training system, so that the user can immediately understand his or her own condition, and through feedback, the user can adjust his or her own physiological signals to return to normal.

[0024] The present invention is not limited to the above-described embodiments, and it is obvious to those skilled in the art that various modifications and variations may be made to the present invention without departing from the spirit or scope of the present invention.

[0025] Thus, it is intended that the present invention covers the modifications and variations of the present invention or that come within the scope of the appended claims and their equivalents.

Claims

1. A physiological signal long-distance two-way transmission and processing system, characterized in that: Include: The user end comprises: an electroencephalogram cap, a processing unit, an output unit and a remote transmission module, wherein the processing unit receives an electroencephalogram physiological signal detected by the electroencephalogram cap from the user's brain, selects one of a plurality of signal transmission modes to process the electroencephalogram physiological signal, transmits the electroencephalogram physiological signal to a cloud network through the remote transmission module, and the processing unit receives a comparison feedback signal through the remote transmission module, and the output unit outputs a comparison result of the comparison feedback signal; and A computing end, comprising: a cloud server and a brain wave physiological database, wherein the cloud server receives the brain wave physiological signal from the cloud network, selects one of a plurality of signal comparison modes to process the brain wave physiological signal, generates the comparison feedback signal according to the brain wave physiological database, and transmits the comparison feedback signal to the user end; The user end selects one of a plurality of signal transmission modes to process and transmit the brain wave physiological signal, and the calculation end selects one of a plurality of signal comparison modes to process the brain wave physiological signal and transmit the comparison feedback signal, and the processing is performed simultaneously; The plurality of signal transmission modes and the plurality of signal comparison modes both include: A predictive calculation compression mode compresses the brain wave physiological signal by generating a combination result based on the correspondence between the waveforms of the brain wave physiological signal between different channels; Numerical compression mode, compressing the brain wave physiological signal by the difference vector angle and difference vector size between consecutive numerical values ​​on the waveform curve of the brain wave physiological signal in the same channel; The block compression mode compresses the brain wave physiological signals with the same template characteristics in different channels as the same coded data; The four-point three-segment compression mode uses a mark for every four points of the brain wave physiological signal of different channels. Every four points will produce three intervals, and the three line segment features on the three intervals can be extracted to compress the brain wave physiological signal.

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