Sleep cognitive enhancement system and method based on deep brain stimulation

Abstract

The application discloses a sleep cognitive enhancement system and method based on deep brain stimulation. The sleep cognitive enhancement system comprises a data acquisition unit, a sleep staging unit, a first detection unit, a second detection unit, a third detection unit and an electrical stimulation unit. The data acquisition unit is used to acquire real-time SEEG signals and scalp EEG data of a user, so as to perform real-time sleep staging through the sleep staging unit. Based on the real-time sleep staging result of the user, the three detection units are used to perform real-time detection on the slow wave rising stage, the spindle wave trough and the hippocampal sharp wave ripple of the user respectively, so as to output three detection results. It is judged whether the three detection results meet the first preset condition; if yes, the electrical stimulation unit is used to perform electrical stimulation on three preset regions respectively; if not, the user is detected again.

Description

TECHNICAL FIELD

[0001] The present application relates to a sleep cognitive enhancement system based on deep brain stimulation, and also relates to a corresponding sleep cognitive enhancement method, and belongs to the technical field of neural regulation. BACKGROUND

[0002] Sleep is one of the most basic physiological activities of human beings, and is also the "golden period" for cognitive map formation and memory consolidation. During this period, the brain will spontaneously perform "neural replay", and hippocampal sharp wave ripples (abbreviated as SWR) are the core biomarker driving this process. SWR is the highest degree of synchronization of neuronal cluster discharge pattern in the mammalian brain, and each occurrence of SWR is accompanied by sequential replay of daytime acquired information and abstract processing of experience, thereby converting short-term memory into long-term memory and ultimately constructing and optimizing the cognitive map.

[0003] Traditional memory consolidation theory further points out that short-term memory is initially formed in the hippocampus and then gradually "stored" in the neocortex during sleep to become long-term memory; during the non-rapid eye movement sleep (NREM) stage, hippocampal SWR, thalamic spindles, and cortical slow oscillations form a "three-wave coupling" under a specific phase relationship. This spatiotemporal coupling is considered to be the key mechanism of memory consolidation - the knowledge acquired during the day is precisely replayed through "three-wave coupling" at night, allowing the brain to organize and consolidate newly encoded memories.

[0004] Based on the above theoretical framework, existing research has confirmed that if a sound stimulus is precisely applied during the slow wave up-state or slow wave down-state, the picture memory paired with the sound can be enhanced or weakened, respectively; similarly, transcranial electrical / magnetic stimulation given at specific phases of slow waves or spindles can also have a regulatory effect on sleep structure and memory processing. These findings have for the first time suggested that it is feasible to improve sleep period memory consolidation with the aid of neural regulation means, and has become a frontier hotspot in the fields of brain-computer interface, cognitive psychology, and clinical diagnosis and treatment.

[0005] However, there are two major bottlenecks in existing research: first, the real-time decoding accuracy is insufficient: it is still not possible to simultaneously capture the wave trough of the spindles and the precise phase of the hippocampal SWR on a millisecond scale, resulting in a blurred regulatory window. Second, the stimulation means is limited: sound, transcranial electrical / magnetic stimulation are non-invasive techniques with low spatial resolution, making it difficult to precisely act on deep structures such as the entorhinal cortex and hippocampus. Therefore, there is still a lack of experimental evidence directly correlating "slow wave different states - three-wave coupling - memory consolidation", and even more lacking is a sleep cognitive enhancement system based on the "three-wave coupling" theory and capable of achieving precise stimulation of deep brain areas. SUMMARY

[0006] The primary technical problem to be solved by the present application is to provide a sleep cognitive enhancement system based on deep brain stimulation.

[0007] Another technical problem to be solved by the present application is to provide a sleep cognitive enhancement method based on deep brain stimulation.

[0008] To achieve the above technical purposes, the present application adopts the following technical solutions:

[0009] According to a first aspect of an embodiment of the present application, a sleep cognitive enhancement system based on deep brain stimulation is provided, comprising:

[0010] A data acquisition unit is configured to acquire real-time SEEG signals and scalp EEG data of a user;

[0011] A sleep staging unit is connected to the data acquisition unit and configured to receive the real-time SEEG signals and scalp EEG data of the user and perform real-time sleep staging, and output real-time sleep staging results;

[0012] A first detection unit is connected to the sleep staging unit and configured to detect a slow wave rising stage in real time based on the real-time sleep staging results, and output a first detection result;

[0013] A second detection unit is connected to the sleep staging unit and configured to perform real-time detection of a spindle wave trough based on the real-time sleep staging results, and output a second detection result;

[0014] A third detection unit is connected to the sleep staging unit and configured to perform real-time detection of a hippocampal sharp wave ripple based on the real-time sleep staging results, and output a third detection result;

[0015] An electrical stimulation unit is connected to the first detection unit, the second detection unit, and the third detection unit, and configured to perform electrical stimulation on a first preset region, a second preset region, and a third preset region when the first detection result, the second detection result, and the third detection result meet a first preset condition.

[0016] Preferably, the sleep staging unit comprises:

[0017] A preprocessing module is connected to the data acquisition unit and configured to receive the SEEG signals and scalp EEG data of the user and perform data preprocessing;

[0018] A feature extraction module is connected to the preprocessing module and configured to extract corresponding signal features in real time based on the preprocessed data; wherein the signal features at least include: time domain signal standard deviation, quartile difference, skewness, kurtosis, and power of multiple frequency band signals;

[0019] The sleep recognition module is connected with the feature extraction module and is preconfigured with a sleep staging model, so as to receive the signal features and predict a sleep stage at a predetermined time after a previous time period, thereby performing real-time sleep staging.

[0020] Preferably, the first detection unit comprises:

[0021] The slow wave descent stage detection module is connected with the sleep staging unit and is configured to perform real-time detection of a slow wave descent stage based on a preset first threshold after the sleep stage enters the N3 stage, and output a slow wave descent stage detection result.

[0022] The slow wave ascent stage detection module is connected with the slow wave descent stage detection module and is configured to perform real-time detection of a slow wave ascent stage based on a preset second threshold when the slow wave descent stage detection result is lower than the first threshold, and output a slow wave ascent stage detection result.

[0023] The slow wave length detection module is connected with the slow wave ascent stage detection module and is configured to perform length detection of a slow wave when the slow wave descent stage detection result is lower than the first threshold and the slow wave ascent stage detection result is higher than the second threshold, and output a slow wave length detection result.

[0024] Preferably, when the slow wave descent stage detection result is lower than the first threshold, the slow wave ascent stage detection result is higher than the second threshold, and the slow wave length detection result conforms to a frequency of the slow wave, the first detection result is that a slow wave is detected; otherwise, the first detection result is that a slow wave is not detected.

[0025] Preferably, the first detection unit further comprises:

[0026] The threshold updating module is connected with the slow wave ascent stage detection module and the slow wave descent stage detection module, and is configured to update the first threshold and the second threshold based on all slow wave events in the N3 sleep stage within a preset time period.

[0027] Preferably, the second detection unit comprises:

[0028] The RMS power detection module is connected with the sleep staging unit and is configured to perform RMS power detection based on a third threshold after the sleep stage enters the N2 and N3 stages, and output an RMS power detection result; when the RMS power detection result is higher than the third threshold and a duration exceeds a preset time length, it indicates that a center of a spindle wave is detected.

[0029] a power value detection module, connected with the RMS power detection module, to output a power value detection result; wherein when the time derivative of the RMS power becomes zero and the RMS power starts to decrease, the maximum spindle wave power is detected;

[0030] a spindle wave end detection module, connected with the RMS power detection module, to output a spindle wave end detection result; wherein when the RMS power detection result changes from being higher than the third threshold to being lower than the third threshold, the spindle wave end is detected;

[0031] a pause and resume detection module, connected with the RMS power detection module, the power value detection module and the spindle wave end detection module, to pause or resume detection based on the RMS power detection result, the power value detection result and the spindle wave end detection result;

[0032] a phase detection module, to perform real-time phase detection on the spindle wave based on a preset algorithm, to output a phase detection result;

[0033] wherein when the RMS power detection result, the power value detection result, the spindle wave end detection result and the phase detection result satisfy a second preset condition, the second detection result is that a trough of the spindle wave is detected; otherwise, the second detection result is that a trough of the spindle wave is not detected.

[0034] wherein preferably, the third detection unit comprises:

[0035] a filtering module, connected with the sleep staging unit, to perform sequence band-pass filtering on the SEEG signal of the hippocampus after the sleep stage enters the N3 stage;

[0036] a pulse conversion module, connected with the filtering module, to define a baseline amplitude that a sharp ripple event must exceed based on a preset length of time window, and convert the electrophysiological signal into upward pulse events / downward pulse events according to the amplitude intensity and its direction;

[0037] a double-layer pulse neural network, connected with the pulse conversion module, to perform real-time detection on the hippocampal sharp ripple based on the upward pulse events / downward pulse events, to output a third detection result;

[0038] wherein when a third preset condition is satisfied, the third detection result is that the hippocampal sharp ripple is detected; otherwise, the third detection result is that the hippocampal sharp ripple is not detected.

[0039] wherein preferably, the electrical stimulation unit comprises:

[0040] a first stimulation part, corresponding to a first preset area, to perform electrical stimulation on the first preset area based on preset stimulation parameters;

[0041] The second stimulation unit corresponds to the second preset area to implement electrical stimulation on the second preset area based on preset stimulation parameters.

[0042] The third stimulation unit corresponds to the third preset area to implement electrical stimulation on the third preset area based on preset stimulation parameters.

[0043] The first preset area corresponds to real-time detection of a slow wave rising stage, the second preset area corresponds to real-time detection of a spindle wave trough, and the third preset area corresponds to real-time detection of a hippocampal sharp wave ripple.

[0044] Preferably, the first preset area is a new cortex, the second preset area is an entorhinal cortex, and the third preset area is white matter adjacent to hippocampal CA1.

[0045] Preferably, the preset stimulation parameters are: 5 bidirectional pulse signals with a frequency of 100 Hz, a current intensity of 1.5 mA, a duration and interval time of 100 μs for two bidirectional pulses, and a duration of 50 ms for each stimulation.

[0046] According to a second aspect of the embodiment of the present application, a sleep cognitive enhancement method based on deep brain stimulation is provided, comprising the following steps:

[0047] Obtaining real-time SEEG signals and scalp EEG data of a user;

[0048] Based on the real-time SEEG signals and scalp EEG data of the user, real-time sleep staging is performed on the user, and a real-time sleep staging result is outputted;

[0049] Based on the real-time sleep staging result, a slow wave rising stage is detected in real time for the user, and a first detection result is outputted;

[0050] Based on the real-time sleep staging result, a spindle wave trough is detected in real time for the user, and a second detection result is outputted;

[0051] Based on the real-time sleep staging result, a hippocampal sharp wave ripple is detected in real time for the user, and a third detection result is outputted;

[0052] If the first detection result, the second detection result and the third detection result satisfy a first preset condition, then the first preset area, the second preset area and the third preset area are subjected to electrical stimulation through an electrical stimulation unit;

[0053] If any one of the first detection result, the second detection result and the third detection result does not satisfy the first preset condition, the user is re-sleep staged based on real-time SEEG signals and scalp EEG data of the user, and detection is performed again until the first detection result, the second detection result and the third detection result satisfy the first preset condition.

[0054] Compared with the prior art, the present application has the following technical effects:

[0055] (1) Three key physiological events in non-rapid eye movement sleep period can be captured in parallel and in real time: slow wave rising stage, spindle wave trough and hippocampal sharp wave ripple. With a millisecond-level sliding window and a special algorithm, it provides a "firing" precise time window for neural regulation, thereby improving the time resolution to an unprecedented height.

[0056] (2) The built-in electrical stimulation unit of the system directly acts on the neocortex, the entorhinal cortex and the hippocampal structure through the implanted deep electrode. Compared with non-invasive methods such as transcranial magnetic stimulation or transcranial electrical stimulation, the spatial positioning error of this targeted stimulation is less than 0.5mm, significantly improving the spatial resolution and ensuring effective intervention on deep circuits.

[0057] (3) By seamlessly combining the above "high-precision time window" and "high-precision spatial target", this system not only provides a new closed-loop research tool for the formation of cognitive maps and memory processing mechanisms in sleep, but also brings potential personalized treatment new solutions for patients with cognitive and memory disorders such as Alzheimer's disease. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 The overall schematic diagram of the sleep cognitive enhancement system based on deep brain stimulation provided by the first embodiment of the present application is provided;

[0059] Figure 2 The application scenario diagram of the sleep cognitive enhancement system based on deep brain stimulation provided by the first embodiment of the present application is provided;

[0060] Figure 3 The overall flowchart of the sleep cognitive enhancement method based on deep brain stimulation provided by the second embodiment of the present application is provided;

[0061] Figure 4 The detailed flowchart of the sleep cognitive enhancement method based on deep brain stimulation provided by the second embodiment of the present application is provided. DETAILED DESCRIPTION

[0062] The technical content of the present application will be described in detail below in combination with the drawings and specific embodiments.

[0063] The embodiment of the present application provides a sleep cognitive enhancement system based on deep brain stimulation, which is based on the mechanism of slow wave, spindle and sharp wave ripple in the hippocampus in the cognitive map formation and memory processing consolidation process during the non-rapid eye movement (Non-rapid Eye Movement) period, combines stereotactic electroencephalogram (SEEG) / scalp electroencephalogram (EEG) and neural image (nuclear magnetic resonance image), realizes real-time detection of the slow wave rising stage, the wave trough of the spindle and the sharp wave ripple in the hippocampus through various pattern recognition and neural network methods, and directly stimulates the neocortex, the entorhinal cortex and the hippocampus through deep brain stimulation technology, and finally builds a neural regulation brain-computer interface system based on the "three wave coupling" of sleep.

[0064] First embodiment

[0065] As shown in Figure 1 and Figure 2 The first embodiment of the present application provides a sleep cognitive enhancement system based on deep brain stimulation, which comprises a data acquisition unit 1, a sleep staging unit 2, a first detection unit 3, a second detection unit 4, a third detection unit 5 and an electrical stimulation unit 6. The data acquisition unit 1 is used to acquire real-time SEEG signals and scalp electroencephalogram data of a user, so as to process the data through the sleep staging unit 2 to perform real-time sleep staging on the user. After outputting the real-time sleep staging result of the user, the first detection unit 3, the second detection unit 4 and the third detection unit 5 are used to respectively detect the slow wave rising stage, the real-time detection of the wave trough of the spindle and the real-time detection of the sharp wave ripple in the hippocampus of the user, so as to output three detection results. Finally, it is judged whether the three detection results meet the first preset condition; if yes, the electrical stimulation unit 6 is used to perform electrical stimulation on the three preset regions respectively to improve the memory processing ability of the user; if not, the user is detected again until the three detection results meet the first preset condition.

[0066] Next, the composition structure and working process of each unit of the sleep cognitive enhancement system will be described in detail.

[0067] (I) Data acquisition unit

[0068] In this embodiment, the data acquisition unit 1 needs to acquire the SEEG signals and scalp electroencephalogram data of the user. The SEEG signals are acquired through a first mode, and the scalp electroencephalogram data are acquired through a second mode. Therefore, by combining the two data, the subsequent data decoding precision can be improved, and the data detection accuracy can be improved.

[0069] It can be understood that the two data acquisition methods adopted in the embodiment are conventional technical means in the art, and in other embodiments, appropriate data acquisition methods can be selected to achieve data acquisition according to needs, which are not limited here.

[0070] (ii) sleep staging unit

[0071] Referring to Figure 1 As shown in the figure, in the embodiment, the sleep staging unit 2 includes a preprocessing module 21, a feature extraction module 22, and a sleep recognition module 23. Specifically, the preprocessing module 21 is connected with the data acquisition unit 1 to receive the SEEG signal and scalp EEG data of the user, and performs data preprocessing (for example, filtering, denoising, and standardization of the collected EEG signal, and other preprocessing operations) to improve the quality and reliability of the signal. It can be understood that these preprocessing steps help to reduce interference and artifacts, making subsequent analysis more accurate. However, after data preprocessing is completed, feature extraction needs to be performed through the feature extraction module 22.

[0072] The feature extraction module 22 is connected with the preprocessing module 21 to extract corresponding signal features in real time based on the preprocessed data. In the embodiment, the signal features at least include: time domain signal standard deviation, quartile difference, skewness, kurtosis, and power of multiple frequency band signals. When the feature extraction module 22 completes the feature extraction, the sleep recognition module 23 is needed to perform real-time staging of the sleep stage.

[0073] The sleep recognition module 23 is connected with the feature extraction module 22 and is pre-set with a sleep staging model. The training of the sleep staging model uses the EEG data C3-M2 (C4-M1) and one eye electrode of the user the night before. The label of the training data comes from the current mainstream Yasa sleep staging algorithm. The core of the Yasa sleep staging algorithm is the Light GBM classifier, which is a classifier based on gradient boosting machine, using a decision tree based on learning algorithm as the basic model, reducing the use of memory through sparse optimization and optimal segmentation of class feature values.

[0074] After the feature extraction module 22 completes feature extraction, the sleep recognition module 23 receives the extracted signal features and uses the signal features from the previous period (e.g., 30s or 60s, but not limited to these) to predict the sleep stage at a predetermined time (e.g., 3s or 5s, but not limited to these), thereby performing real-time sleep staging. Furthermore, in this embodiment, when performing sleep staging using the sleep staging model, a random forest algorithm is first used to perform real-time sleep staging on the scalp EEG, and type A random undersampling is used to address the issue that stages N3 and N1 are shorter than the wakefulness period. It is understood that the random forest algorithm and type A random undersampling are common knowledge in the art and will not be specifically described here.

[0075] (III) First Detection Unit

[0076] like Figure 1 As shown, in this embodiment, the first detection unit 3 is connected to the sleep staging unit 2 to detect the slow-wave rising phase in real time based on the real-time sleep staging results and output a first detection result. Specifically, the first detection unit 3 includes a slow-wave falling phase detection module 31, a slow-wave rising phase detection module 32, and a slow-wave length detection module 33. The slow-wave falling phase detection module 31 is used to detect the slow-wave falling phase in real time, the slow-wave rising phase detection module 32 is used to detect the slow-wave rising phase in real time, and the slow-wave length detection module 33 is used to detect the length of the slow wave.

[0077] The slow wave (0.16–1.25 Hz slow wave oscillation) consists of periodically alternating slow wave rising phases and slow wave falling phases: the slow wave rising phase is characterized by an increase in EEG amplitude. The slow wave falling phase is characterized by a decrease in EEG amplitude below the baseline. Each slow wave falling phase lasts approximately 100–300 milliseconds and alternates with the slow wave rising phase, forming the slow wave oscillation.

[0078] In this embodiment, based on the user's sleep data of the neocortex and hippocampus the previous night, all slow waves in the N3 sleep stage are extracted, and 75% of the slow wave descent stage is set as the first threshold, and 75% of the slow wave descent stage is set as the second threshold. At the same time, the length of all slow waves is calculated.

[0079] Then, the SEEG signal is real-time sequence band-pass filtered (0.16-1.25 Hz) in the current night, and then data buffers with lengths of 2 s and 400 s are defined respectively. Based on the real-time sleep staging result of the sleep staging unit 2, when the user's sleep enters the N3 stage, the first threshold is used for real-time detection of the slow wave descending stage, and when the SEEG signal is lower than the first threshold of the slow wave descending stage, the 2 s data buffer is detected by the slow wave ascending stage threshold. When the detection result of the slow wave ascending stage is higher than the second threshold, the length of the slow wave is detected by the slow wave length detection module 33 to ensure that the length of the slow wave meets the frequency of the slow wave.

[0080] Finally, when the slow wave descending stage detection result is lower than the first threshold, the slow wave ascending stage detection result is higher than the second threshold, and the length detection result of the slow wave meets the frequency of the slow wave, the first detection unit 3 outputs the first detection result as detecting a slow wave; otherwise, the first detection unit 3 outputs the first detection result as not detecting a slow wave.

[0081] In addition, in the present embodiment, the first detection unit 3 further comprises a threshold updating module 34. The threshold updating module is connected with the slow wave descending stage detection module 31 and the slow wave ascending stage detection module 32 to update the first threshold and the second threshold based on all slow wave events in the N3 sleep data (i.e. the above-mentioned 400 s data buffer) in a preset time period.

[0082] (Four) Second detection unit

[0083] As shown in Figure 1 In the present embodiment, the second detection unit 4 is connected with the sleep staging unit 2 to perform real-time detection of the spindle wave trough based on the real-time sleep staging result and output the second detection result. Specifically, the second detection unit 4 comprises an RMS (Root Mean Square) power detection module 41, a power value detection module 42, a spindle wave end detection module 43, a pause and resume detection module 44, and a phase detection module 45.

[0084] The RMS power detection module 41 is connected with the sleep staging unit 2, and is used to perform RMS power detection based on a third threshold after the sleep stage enters the N2 and N3 stages, and output the RMS power detection result. When the RMS power detection result is higher than the third threshold (the mean value of the EEG signal power of the user's previous night data + 1.15 standard deviations) and the duration exceeds a preset time (in the present embodiment, 250 milliseconds, corresponding to half of the shortest spindle wave duration 500 milliseconds), it indicates that the center of the spindle wave is detected. Otherwise, it indicates that the center of the spindle wave is not detected.

[0085] The power value detection module 42 is connected with the RMS power detection module 41 to output the power value detection result. When the time derivative of the RMS power becomes zero and the RMS power begins to decrease, the maximum spindle wave power is detected; otherwise, the maximum spindle wave power is not detected.

[0086] The spindle wave end detection module 43 is connected with the RMS power detection module 42 to output the spindle wave end detection result. When the RMS power detection result changes from being higher than the third threshold value to being lower than the third threshold value, the spindle wave end is detected; otherwise, the spindle wave end is not detected.

[0087] The pause and resume detection module 44 is connected with the RMS power detection module 41, the power value detection module 42 and the spindle wave end detection module 43 to pause or resume the detection based on the RMS power detection result, the power value detection result and the spindle wave end detection result. Specifically, in the embodiment, when the RMS power signal remains above the third threshold value for more than 2s, the detection is paused until at least three of the four signal features exceed their respective thresholds again.

[0088] The phase detection module 45 performs real-time phase detection on the spindle wave based on a preset algorithm to output the phase detection result.

[0089] In the specific detection, first, based on the sleep data of the new cortex and hippocampus of the user the night before, all spindle waves in the sleep N2 stage are extracted, and 75% of the peak value of the spindle wave is defined as the initial threshold value, and the length of all spindle waves is calculated. Then, the SEEG signal is sequentially band-pass filtered (12-16 Hz) in the current night, and data buffers with lengths of 4s and 400s are defined respectively. Based on the real-time sleep staging result of the sleep staging module 2, when the sleep of the user enters the N2 and N3 stages, when at least three of the following four signal features meet the respective standards, and the duration of the spindle wave meets the requirements (i.e., when the second preset condition is met), it can be determined that a spindle wave is detected:

[0090] ①RMS power signal detection: After the RMS power signal of the SEEG exceeds the "entry threshold value" (mean value + 1.15 standard deviation), if the duration exceeds 250 milliseconds (corresponding to half of the shortest spindle wave duration of 500 milliseconds), it indicates that the center of the spindle wave is detected.

[0091] ②Maximum power signal: When the time derivative of the RMS power of the SEEG becomes zero and the RMS power begins to decrease, the maximum spindle wave power is detected, which is assumed to be the center of the spindle wave.

[0092] ③Spindle wave end: When the signal is lower than the "entry threshold value" again, the end of the spindle wave is detected.

[0093] (4) Pause and resume detection: If the RMS power signal remains above the "entry threshold" for more than 2s, detection will pause until at least three of the four signal features exceed their respective thresholds again.

[0094] In addition to detecting the spindle event, it is also necessary to estimate its oscillation phase in real time. In this embodiment, the open-source real-time phase estimation algorithm phastimate is used to realize real-time detection and phase estimation of the spindle, providing accurate and real-time spindle trough detection results.

[0095] (Five) Third detection unit

[0096] As shown in Figure 1 , in this embodiment, the third detection unit 5 is connected with the sleep staging unit 2 to perform real-time detection of the hippocampal sharp ripple based on the real-time sleep staging result, and output the third detection result. Specifically, the third detection unit 5 includes a filtering module 51, a pulse conversion module 52, and a double-layer pulse neural network 53. Among them, the filtering module 51 is connected with the sleep staging unit 2, used to perform sequence band-pass filtering (70-180Hz) on the SEEG signal of the hippocampus after the sleep stage enters the N3 stage, and then enters the signal pulse conversion stage.

[0097] The pulse conversion module 52 is connected with the filtering module 51 to define a baseline amplitude that a sharp ripple event must exceed based on a time window of a preset length, and convert the electro-physiological signal into an upward pulse event / downward pulse event according to the amplitude intensity and its direction. Specifically, in this embodiment, a baseline amplitude that a sharp ripple event must exceed is defined in a time window of a specific length (the time window length in this embodiment is selected as 0.1s) in advance (in this embodiment, the maximum signal amplitude of 0.05s continuous non-overlapping signal in the time window is selected, and the average value at the lower four quartiles is taken as the baseline amplitude), and the electro-physiological signal is converted into an upward pulse event / downward pulse event using an asynchronous modulator according to the amplitude intensity and its direction.

[0098] The double-layer pulse neural network 53 is connected with the pulse conversion module 52 to perform real-time detection of the hippocampal sharp ripple based on the upward pulse event / downward pulse event, and output the third detection result. Specifically, after obtaining the pulse event, it is input into the double-layer pulse neural network integrating the dynamic synapse and neuron discharge, and finally, by extracting the key features of the electro-physiological signal in this period, the previously trained, best-performing classifier is used to perform real-time detection of the hippocampal sharp ripple.

[0099] It can be understood that in this embodiment, when the preset condition is met, the third detection result is that the hippocampal sharp ripple is detected; otherwise, the third detection result is that the hippocampal sharp ripple is not detected.

[0100] (VI) an electrical stimulation unit

[0101] As shown in Figure 1 the embodiment, the electrical stimulation unit 6 is connected with the first detection unit 3, the second detection unit 4 and the third detection unit 5, and is used to perform electrical stimulation on the first preset region, the second preset region and the third preset region when the first detection result, the second detection result and the third detection result meet the first preset condition. The electrical stimulation unit 6 includes a first stimulation part 61, a second stimulation part 62 and a third stimulation part 63. The three stimulation parts correspond to the three preset regions respectively, so as to perform electrical stimulation on the respective corresponding preset regions based on preset stimulation parameters.

[0102] Specifically, in the embodiment, the first preset region is the neocortex, which corresponds to the real-time detection of the slow wave rising stage, and the first stimulation part 61 is used to perform electrical stimulation on the first preset region. The second preset region is the entorhinal cortex, which corresponds to the real-time detection of the spindle wave trough, and the second stimulation part 62 is used to perform electrical stimulation on the second preset region. The third preset region is the white matter adjacent to the hippocampal CAI, which corresponds to the real-time detection of the hippocampal sharp wave ripple, and the third stimulation part 63 is used to perform electrical stimulation on the third preset region. The first stimulation part 61, the second stimulation part 62 and the third stimulation part 63 perform electrical stimulation based on the same preset stimulation parameters, and the preset stimulation parameters are: 5 bidirectional pulse signals with a frequency of 100 Hz, a current intensity of 1.5 mA, a duration and interval time of 100 μs for two bidirectional pulses, and a duration of 50 ms for each stimulation. In addition, in the embodiment, the first preset condition is that the first detection result is the detection of the slow wave, the second detection result is the detection of the trough of the spindle wave, and the third detection result is the detection of the hippocampal sharp wave ripple. Thus, based on the sleep "three wave coupling", the deep brain stimulation technology is used to directly stimulate the neocortex, the entorhinal cortex and the hippocampus, and finally a sleep cognitive enhancement system based on deep brain stimulation is built.

[0103] Second embodiment

[0104] As shown in Figure 3 and Figure 4 on the basis of the first embodiment, the second embodiment of the present application provides a sleep cognitive enhancement method based on deep brain stimulation, which specifically includes the following steps:

[0105] S1: acquiring real-time SEEG signals and scalp EEG data of a user.

[0106] Specifically, in the embodiment, the SEEG signals and the scalp EEG data of the user are collected in real time by the data acquisition unit 1. The SEEG signals are collected by a first mode, and the scalp EEG data are collected by a second mode.

[0107] S2: based on the real-time SEEG signal and scalp EEG data of the user, real-time sleep staging is performed on the user, thereby outputting a real-time sleep staging result.

[0108] Specifically, after obtaining the SEEG signal and scalp EEG data of the user based on step S1, first, signal features are extracted after data preprocessing; then, based on a pre-set sleep staging model, the signal features of the previous period (for example, 30s or 60s, but not limited to) are used to predict the sleep stage in the subsequent predetermined time (for example, 3s or 5s, but not limited to), thereby performing real-time sleep staging.

[0109] S3: based on the real-time sleep staging result, sleep detection is performed on the user.

[0110] Specifically, this step is divided into three aspects of detection, which are real-time detection of slow wave rising stage, real-time detection of spindle wave trough and real-time detection of hippocampal sharp wave ripple.

[0111] In this embodiment, when the user is in the N3 stage of sleep, the first detection unit 3 detects the slow wave rising stage of the user in real time and outputs the first detection result. The specific detection logic of the first detection unit 3 is described with reference to the first embodiment, which will not be repeated here. In addition, when the user is in the N3 stage of sleep, the third detection unit 5 also detects the hippocampal sharp wave ripple of the user in real time and outputs the third detection result. The specific detection logic of the third detection unit 5 is described with reference to the first embodiment, which will not be repeated here.

[0112] In addition, when the user is in the N2 stage of sleep, the second detection unit 4 detects the spindle wave trough of the user in real time and outputs the second detection result. The specific detection logic of the second detection unit 4 is described with reference to the first embodiment, which will not be repeated here.

[0113] It should be noted that in this embodiment, the above three aspects of detection are detected in parallel, thereby respectively detecting the sharp wave ripple, slow wave and spindle wave, and the most core detection is the detection of the sharp wave ripple.

[0114] S4: performing electrical stimulation on the pre-set region.

[0115] After obtaining the first detection result, the second detection result and the third detection result based on step S3, it is necessary to determine whether the three detection results satisfy the first preset condition. If it is satisfied, the electrical stimulation is implemented, and if it is not satisfied, the sleep staging is performed again and detected again.

[0116] Specifically, if the first detection result is detection of a slow wave, the second detection result is detection of a spindle wave, and the third detection result is detection of a hippocampal sharp wave ripple, the first stimulation unit 61, the second stimulation unit 62, and the third stimulation unit 63 are used to perform electrical stimulation on the neocortex, the entorhinal cortex, and the white matter near the hippocampal CA1, respectively. Conversely, if any one of the detection results is not detected, no electrical stimulation is performed.

[0117] In summary, the sleep cognitive enhancement system and method based on deep brain stimulation provided by the embodiments of the present application have the following beneficial effects:

[0118] First, in the time dimension, the system adopts a "three-stage parallel detection" mechanism: at the same time, the slow wave rising stage of N3, the spindle wave trough of N2, and the hippocampal sharp wave ripple of N3 are detected in real time and synchronously. With a millisecond-level sliding time window, the system can respond within a very short time after the event. The spindle wave phase is completed by a real-time phase estimation algorithm phastimate, with a phase estimation error of only ±10 ms; the hippocampal sharp wave ripple is processed by a double-layer pulse neural network to process high-frequency signals, and compared with the traditional FFT method, the time resolution is significantly improved. In addition, the slow wave detection threshold is not fixed, but is updated based on the data of the previous 400 seconds, which can adapt to individual differences and significantly reduce the false positive rate.

[0119] Second, in the spatial dimension, the system directly transmits electrical stimulation of preset parameters to the neocortex, the entorhinal cortex, and the hippocampus through stereotactic deep electrodes. Compared with traditional non-invasive neuroregulation (transcranial magnetic or electrical stimulation positioning error > 10 mm), the positioning error of stereotactic electrodes is < 0.5 mm, significantly improving the spatial resolution and greatly improving the success rate of entorhinal cortex stimulation. More importantly, the system simultaneously stimulates the three targets of "neocortex-entorhinal cortex-hippocampus", forming a closed memory loop regulation, further enhancing the stimulation effect.

[0120] Finally, by organically combining the above high time resolution and high spatial resolution, the system not only provides a new closed-loop research tool for complex scientific problems such as the formation of cognitive maps and memory processing mechanisms during sleep, but also provides a practical solution for improving the cognitive function of patients with cognitive and memory disorders such as Alzheimer's disease.

[0121] Therefore, the embodiments of the present application solve the core problem of "spatiotemporal resolution imbalance" in traditional neuroregulation technology (non-invasive means lack spatial resolution, deep stimulation lacks temporal precision), providing a new closed-loop intervention paradigm for cognitive disorder treatment.

[0122] It should be noted that the above embodiments are only illustrative. The technical solutions of each embodiment can be combined, and all are within the protection scope of the present application.

[0123] Furthermore, the terms "first", "second", etc. are used only for descriptive purposes and are not to be construed as indicating or implying relative importance or an ordered ranking of indicated technical features. Thus, features defined with "first", "second" etc. can include, explicitly or implicitly, one or more of such features. In the description of the application, the meaning of "a plurality" is two or more, unless explicitly specified otherwise.

[0124] The deep brain stimulation based sleep cognitive enhancement system and method provided by the present application are described in detail above. Any obvious modification made to the present application by those skilled in the art without departing from the essential content of the present application shall constitute an infringement of the patent right of the present application and shall bear the corresponding legal responsibility.

Claims

1. A sleep cognitive enhancement system based on deep brain stimulation, characterized in that The system comprises: a data acquisition unit configured to acquire real-time SEEG signals and scalp EEG data of a user; a sleep staging unit connected to the data acquisition unit, configured to receive the real-time SEEG signals and scalp EEG data of the user and perform real-time sleep staging, and output real-time sleep staging results; a first detection unit connected to the sleep staging unit, configured to detect a slow wave rising phase in real time based on the real-time sleep staging results, and output a first detection result; a second detection unit connected to the sleep staging unit, configured to detect a spindle wave trough in real time based on the real-time sleep staging results, and output a second detection result; a third detection unit connected to the sleep staging unit, configured to detect a hippocampal sharp wave ripple in real time based on the real-time sleep staging results, and output a third detection result; an electrical stimulation unit connected to the first detection unit, the second detection unit, and the third detection unit, configured to perform electrical stimulation on a first preset region, a second preset region, and a third preset region when the first detection result, the second detection result, and the third detection result satisfy a first preset condition; wherein the first preset condition is that the first detection result is detection of a slow wave, the second detection result is detection of a spindle wave trough, and the third detection result is detection of a hippocampal sharp wave ripple; the electrical stimulation unit comprises a first stimulation part corresponding to the first preset region, configured to perform electrical stimulation on the first preset region based on preset stimulation parameters; a second stimulation part corresponding to the second preset region, configured to perform electrical stimulation on the second preset region based on the preset stimulation parameters; and a third stimulation part corresponding to the third preset region, configured to perform electrical stimulation on the third preset region based on the preset stimulation parameters; the first preset region is a neocortex corresponding to real-time detection of a slow wave rising phase; the second preset region is an entorhinal cortex corresponding to real-time detection of a spindle wave trough; and the third preset region is white matter adjacent to a hippocampal CAI corresponding to real-time detection of a hippocampal sharp wave ripple.

2. The sleep cognitive enhancement system of claim 1, wherein The sleep staging unit comprises: a preprocessing module connected to the data acquisition unit, configured to receive the SEEG signals and scalp EEG data of the user and perform data preprocessing; a feature extraction module connected to the preprocessing module, configured to extract corresponding signal features in real time based on the preprocessed data; wherein the signal features at least include time domain signal standard deviation, quartile deviation, skewness, kurtosis, and power of multiple frequency band signals; a sleep recognition module connected to the feature extraction module and preconfigured with a sleep staging model, configured to receive the signal features, and predict a sleep stage at a predetermined time after a previous time period using the signal features of the previous time period, thereby performing real-time sleep staging.

3. The sleep cognitive enhancement system of claim 1, wherein The first detection unit comprises: a slow wave descending phase detection module connected to the sleep staging unit, configured to detect a slow wave descending phase in real time based on a preset first threshold after a sleep stage enters an N3 stage, and output a slow wave descending phase detection result; The slow wave rising phase detection module is connected with the slow wave falling phase detection module, and is configured to, when the slow wave falling phase detection result is lower than the first threshold value, detect a slow wave rising phase in real time based on a preset second threshold value, and output a slow wave rising phase detection result; The slow wave length detection module is connected with the slow wave rising phase detection module, and is configured to, when the slow wave falling phase detection result is lower than the first threshold value and the slow wave rising phase detection result is higher than the second threshold value, detect a length of the slow wave, and output a slow wave length detection result; When the slow wave falling phase detection result is lower than the first threshold value, the slow wave rising phase detection result is higher than the second threshold value, and the slow wave length detection result is consistent with the frequency of the slow wave, the first detection result is that the slow wave is detected; otherwise, the first detection result is that the slow wave is not detected.

4. The sleep cognitive enhancement system of claim 3, wherein The first detection unit further includes: The threshold value updating module is connected with the slow wave rising phase detection module and the slow wave falling phase detection module, and is configured to update the first threshold value and the second threshold value based on all slow wave events in the N3 sleep stage in a preset time period.

5. The sleep cognitive enhancement system of claim 1, wherein The second detection unit includes: The RMS power detection module is connected with the sleep staging unit, and is configured to, after the sleep stage enters the N2 and N3 stages, perform RMS power detection based on a third threshold value, and output an RMS power detection result; when the RMS power detection result is higher than the third threshold value and the duration exceeds a preset time length, it is indicated that the center of the spindle wave is detected; The power value detection module is connected with the RMS power detection module, and is configured to output a power value detection result; when the time derivative of the RMS power becomes zero and the RMS power starts to decrease, the maximum spindle wave power is detected; The spindle wave end detection module is connected with the RMS power detection module, and is configured to output a spindle wave end detection result; when the RMS power detection result changes from being higher than the third threshold value to being lower than the third threshold value, the end of the spindle wave is detected; The pause and resume detection module is connected with the RMS power detection module, the power value detection module and the spindle wave end detection module, and is configured to pause or resume detection based on the RMS power detection result, the power value detection result and the spindle wave end detection result; The phase detection module is configured to perform real-time phase detection on the spindle wave based on a preset algorithm, and output a phase detection result; When the RMS power detection result, the power value detection result, the spindle wave end detection result and the phase detection result satisfy a second preset condition, the second detection result is that the trough of the spindle wave is detected; otherwise, the second detection result is that the trough of the spindle wave is not detected.

6. The sleep cognitive enhancement system of claim 1, wherein The third detection unit includes: The filtering module is connected with the sleep staging unit, and is configured to, after the sleep stage enters the N3 stage, perform sequence band-pass filtering on the SEEG signal of the hippocampus; a pulse conversion module, connected with the filter module, to define a baseline amplitude that a sharp wave ripples event must exceed based on a preset length of time window, and to convert the electrophysiological signal into an up pulse event / down pulse event according to the amplitude intensity and its direction; a double-layer pulse neural network, connected with the pulse conversion module, to perform real-time detection on the hippocampal sharp wave ripples based on the up pulse event / down pulse event, and output a third detection result; wherein, when a third preset condition is met, the third detection result is a detection of the hippocampal sharp wave ripples; otherwise, the third detection result is a non-detection of the hippocampal sharp wave ripples.

7. The sleep cognitive enhancement system of claim 1, wherein: the first preset region is the neocortex, the second preset region is the entorhinal cortex, and the third preset region is the white matter adjacent to the CAI of the hippocampus.

8. The sleep cognitive enhancement system of claim 1, wherein: the preset stimulation parameters are: 5 bidirectional pulse signals with a frequency of 100 Hz, a current intensity of 1.5 mA, a duration and interval time of 100 μs for two bidirectional pulses, and a stimulation duration of 50 ms for each.

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