A neurovascular coupling feature extraction method and device and a storage medium

Abstract

The application relates to a neurovascular coupling feature extraction method and device and a storage medium, comprising the following steps: obtaining a TOI signal and an EEG signal of a frontal lobe; preprocessing the TOI signal and the EEG signal to obtain a TOI signal and an aEEG signal respectively; calculating time-frequency spectra of the TOI signal and the aEEG signal; extracting a marked region of the TOI signal and the aEEG signal; and extracting an NVC evaluation index of the TOI signal and the aEEG signal according to the marked region data. The CWT time-frequency spectrum of the aEEG signal is used to mark a time-frequency band with active neural activity, so that the wavelet consistency region participating in the calculation is a region with active neural activity, the contact degree between the NVC index value and the NVC physiological principle is improved, the average wavelet consistency and the average phase difference feature of each frequency band are extracted, the former mainly reflects the significance of the hemodynamic response of neural activity, and the latter mainly reflects the timeliness of the hemodynamic response of neural activity, and the effective consistency percentage index is more diverse and comprehensive.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of neurovascular coupling research, and particularly relates to a neurovascular coupling feature extraction method and device and a storage medium. BACKGROUND

[0002] Neurovascular coupling (NVC) mainly studies the coupling relationship between neural activity and cerebral blood flow (CBF), and is one of the most important and most studied fields in the study of neurovascular units (NVUs). The human brain is a complex and energy-consuming organ, accounting for only 2% of the body mass, but consuming 20% of the total body energy. However, the brain itself does not store any energy material (mainly oxygen and glucose), and completely relies on the timely supply of blood. Due to the time and regional differences in brain neural activity, the energy demand of each brain region is dynamic, and the energy demand of multiple brain regions is regionally different. Therefore, blood flow needs to reach the designated area at the right time to meet the demand of neural activity. If the blood flow of a specific brain region decreases, it cannot meet the energy demand of the local tissue, causing some subtle changes in the brain structure and NVU, leading to chronic brain injury and further causing cognitive dysfunction. If the blood supply to the brain is completely interrupted for a few minutes, it will cause irreversible brain damage and death.

[0003] NVC is an important mechanism to ensure the blood supply of local brain regions, which meets the metabolic demand of the brain by timely delivering oxygen and glucose to the activated brain regions. In simple terms, the CBF of high-energy utilization regions is higher, and the CBF of low-energy utilization regions is lower. There are two theoretical models of NVC mechanism, feedback model and feedforward model, to explain why the CBF increases when the local brain region is active. The feedback model believes that the increase of CBF is driven by the metabolic and removal needs of the brain. Brain neural activity consumes oxygen and glucose and produces some toxic byproducts, such as lactic acid, carbon dioxide and beta amyloid protein, etc., at the same time, the temperature of the local brain tissue is increased. The increase of CBF can take away the toxic byproducts produced by brain tissue metabolism, and also can regulate the temperature of the local brain tissue. At the same time, the metabolic byproducts produced by brain activity also include some potent vasodilators, such as adenosine, carbon dioxide, H+ and lactic acid, etc., which can act as potential initiators of CBF increase. The feedforward model does not believe that the increase of CBF is driven by the metabolic state of the blood vessels. Fox and Raichle believe that the increase of CBF will be greater than the oxygen demand of the brain tissue, resulting in the excess delivery of oxygen. In the case of excess delivery of oxygen and glucose, the increase of CBF also occurs. It is indicated that the consumption of energy substances does not drive the increase of CBF flow. Therefore, the feedforward model believes that the CBF delivery is affected by the neurovascular signaling pathway, resulting in synaptic activity and release of vasoactive byproducts, such as K+, nitric oxide (NO) and prostaglandin.

[0004] In fact, the feedforward model and the feedback model are not considered to be mutually exclusive at present. Microvascular studies have shown that the decrease of oxygen and glucose will trigger the function of metabolic factors in NVC before the start of neural activity and the increase of CBF. Moreover, the difference in basal oxygen content of different brain regions is very large, which may lead to local brain tissue hypoxia according to the local vascular topography and the activation intensity of neural stimulation, and then promote the expansion of local blood vessels. Moreover, the decrease of oxygen in the blood vessels at the beginning of activation will lead to the increase of red blood cell deformability, thereby improving the microvascular rheology and increasing the blood flow in the capillaries.

[0005] The above contents jointly prove that the feedforward mechanism may trigger an excessive blood flow response driven by neurovascular signals, and at the same time may adjust the delivery of CBF of the brain through the feedback mechanism, so that the supply is closer to the metabolic demand of the tissue. There are multiple documents supporting this hypothesis, which found that during continuous neural activity, the CBF often reaches a peak at the beginning and is then gradually adjusted to a lower level. Therefore, both the feedback mechanism dependent on metabolism and the independent feedforward mechanism may be involved in the functional hyperemia process during neural activity, depending on the time, intensity and duration of neural activity, as well as the brain region and the development stage of the brain.

[0006] Although the details of the mechanism of NVC are still not fully understood, existing evidence suggests that the main function of NVC is to maintain the stability of the brain microenvironment by providing the energy substances required for the initiation and maintenance of neural activity, while removing potential toxic byproducts of brain metabolism and carrying away the heat generated by neural activity. The schematic diagram of the feedforward and feedback model in the mechanism of NVC is shown in Figure 1 .

[0007] At present, a large number of literatures have shown that the change of NVU function will lead to brain dysfunction and brain injury, as shown in Figure 2 , among which the NVC function of patients with Alzheimer's disease (AD) and Amyotrophic Lateral Sclerosis (ALS) will decrease significantly. AD is the most common cause of dementia in the elderly, mainly due to the accumulation of Aβ in extracellular amyloid plaques and the over-phosphorylation of tau protein in intracellular neuronal fibrils to induce neuronal dysfunction and injury. A large number of studies have shown that Aβ is an important cause of neurovascular dysfunction. Thomas et al. first reported that Aβ not only causes nerve dysfunction, but also damages the ability of endothelial cells to relax systemic blood vessels in vitro. Through animal experiments, it is determined that Aβ damages the NVC, endothelial function and the ability of cerebral circulation regulation mechanism such as cerebral vascular autoregulation. Kisler et al. found that there were symptoms such as decrease of CBF at rest and weakening of hemodynamic response to neural activation in early AD patients, indicating that the NVC function has decreased in the early stage of AD disease. ALS mainly refers to the weakness and paralysis when walking due to the dysfunction and degeneration of upper and lower motor neurons. Based on the increase of plasma proteins in cerebrospinal fluid and the changes of spinal cord microvessels in cadaveric autopsy samples, it is shown that the changes of blood-brain barrier and blood-spinal cord barrier promote the development of the disease. Miyazaki et al. found that there was decoupling between spinal cord blood flow and glucose metabolism in ALS animal experiments, and the increase of spinal cord capillary permeability was accompanied by microhemorrhage, which all appeared before the degeneration of neurons. In addition, studies have shown that neurovascular factors play a role in the process of neuronal degeneration, especially in cognitive ability. There is a decrease in cortical perfusion in the early stage of the disease, including non-motor areas. In ALS patients with frontal lobe cognitive dysfunction, the increase of CBF caused by the activation task of the associated areas of the frontal lobe is reduced.

[0008] There are also documents that found that due to the incomplete development of the cerebral vascular system in newborns, the NVC of newborns and premature infants is quite different from that of adults. The hemodynamic response after neural activity may be late and less obvious, and even lead to significant hypoxia. Nourhashemi et al. found that the NVC function of premature infants is related to age, indicating that when the NVC is not fully developed, the cerebral vascular network does not adopt a unified strategy to respond to brain cortical neural activation. Roche-Labarbe et al. compared healthy premature infants and premature infants with intracranial hemorrhage and found that healthy premature infants showed fluctuations in CBF within 3-4 s after neural activity and gradually returned to the baseline level before the point activity within 20 s, while premature infants with intracranial hemorrhage showed fluctuations in CBF within 2-5 s after neural activity, but the fluctuations disappeared quickly, which may indicate the dysfunction of NVC function in brain injury newborns. Chalak and Tian et al. analyzed the NVC function of moderate to severe HIE patients and healthy newborns within 72 hours after birth and found that the NVC function of moderate to severe HIE patients decreased significantly in the ultra-low frequency range. These studies show that the study of NVC function in newborns and premature infants has very important clinical significance.

[0009] In clinical practice, aEEG or EEG is used to monitor brain function, which has become the standard of care for neonatal hypoxic-ischemic encephalopathy (HIE). The tissue oxygenation index (TOI) or tissue oxygen saturation (rSO2, SctO2) in the NIRS device can evaluate the balance of cerebral oxygen supply and consumption, and is widely used in clinical practice. It can find early brain hypoxia or imbalance of cerebral oxygen supply and consumption, and is often used as a substitute for CBF. EEG devices can monitor the neural activity of local brain tissue, and NIRS can monitor the balance of cerebral oxygen supply and consumption. There are many documents that calculate NVC function based on EEG and NIRS signals. The calculation process mainly has the following steps:

[0010] The aEEG signal or processed EEG signal is calculated to obtain the active trajectory of the original EEG signal. The standard aEEG signal calculation process mainly includes 2Hz-15Hz band-pass filtering, rectification, envelope extraction, segmentation, upper terminal position extraction (UTP) and lower terminal position extraction (LTP), edge calculation and logarithmic scaling. The calculation process of the processed EEG signal is relatively simple, including 2Hz-15Hz band-pass filtering, global rectification and spline interpolation. In order to correspond to the sampling frequency of the TOI signal, the aEEG signal or the processed EEG signal will be down-sampled to 0.209Hz (4.78s). The detailed calculation process is shown in Figure 3 .

[0011] The wavelet coherence transform time-frequency spectrum of TOI signal and aEEG signal or processed EEG signal is calculated. First, the wavelet transform software package of MATLAB is used to obtain the cross wavelet coherence (R 2 ) and the phase difference (φ) of TOI with respect to aEEG signal or processed EEG signal. In Figure 4 , the wavelet coherence transform time-frequency spectrum of a newborn with anencephaly 10 hours after birth is shown.

[0012] Based on the cross wavelet coherence (R 2 ) and the phase difference (φ), the NVC features are extracted for quantitative analysis. As shown in Figure 5 , the extracted features mainly include the percentage of effective coherence in the frequency band of 0.00025Hz-0.001Hz to the total time. It is found that the percentage of effective coherence in the frequency band of 0.00025Hz-0.001Hz of the newborns with anencephaly is significantly higher than that of the newborns with encephalopathy.

[0013] In addition, Das et al. found that although different aEEG algorithms can derive different aEEG waveforms, the calculated percentage of effective coherence features have good consistency, indicating that different aEEG algorithms have little effect on the wavelet coherence-based NVC feature analysis. In the follow-up study of Das et al., it was also found that the percentage of effective coherence features calculated by processed EEG and aEEG also have good consistency, providing a flexible method for calculating NVC for traditional EEG equipment.

[0014] The method for evaluating the NVC function of newborns based on aEEG signal and NIRS signal and wavelet coherence algorithm is a novel bedside real-time monitoring method for evaluating the NVC function of newborns, but there are several shortcomings at present:

[0015] The frequency band used is relatively low, and there is a certain requirement for the length of real-time monitoring data. The frequency band mainly used by this method is 0.00025Hz-0.001Hz, corresponding to a period of 17min-66min, so the EEG data and NIRS data must be collected simultaneously for at least 132min. However, under complex clinical conditions, it may be difficult to continuously collect data for 132min.

[0016] The NVC evaluation index has low relevance to the physiological principle of NVC. The NVC index extracted by the method is the percentage of effective coherence, which refers to the cross wavelet coherence (R 2The percentage of valid pixels verified by the Monte Carlo method accounts for the percentage of total pixels, and the percentage of valid consistency is related to the correlation between the aEEG signal and the TOI signal verified by the Monte Carlo method, so the percentage of valid consistency can to some extent reflect the time-frequency domain correlation between the aEEG signal and the TOI signal. However, this index can only measure whether the aEEG signal and the TOI signal exist correlation, and cannot measure the specific correlation value of the aEEG signal and the TOI signal. In addition, according to the physiological principle of NVC, it is necessary to accurately locate and analyze the time point of each neural activity and evaluate the NVC function, and to exclude the time point without neural activity. The valid consistency percentage index uses all time points, which may introduce some noise information.

[0017] The NVC index is single. This method only extracts the consistency percentage feature of a single frequency band, and can only obtain the time percentage of the aEEG signal and the TOI signal having a certain correlation in the same phase. However, the NVC function evaluation not only needs to measure whether there is coupling between neural activity and CBF, but also needs to evaluate the specific relative delay of neural activity and CBF in the coupling case. SUMMARY

[0018] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a neural vascular coupling feature extraction method, device and storage medium.

[0019] In a first aspect, the present application provides a neural vascular coupling feature extraction method, the method comprising the steps of:

[0020] obtaining a TOI signal and an EEG signal of the frontal lobe;

[0021] preprocessing the TOI signal and the EEG signal and respectively obtaining a TOI signal and an aEEG signal;

[0022] calculating the time-frequency spectrum of the TOI signal and the aEEG signal;

[0023] extracting a marked region of the TOI signal and the aEEG signal;

[0024] extracting an NVC evaluation index of the TOI signal and the aEEG signal according to the marked region data.

[0025] Preferably, the preprocessing of the TOI signal and the EEG signal and the corresponding obtaining of the TOI signal and the aEEG signal comprises the steps of:

[0026] obtaining a first plurality of files of the TOI signal and a second plurality of files of the EEG signal;

[0027] performing multi-file merging and timeline synchronization on the first multi-file and the second multi-file;

[0028] converting the EEG signal into a raw aEEG signal;

[0029] merging the raw aEEG signal and obtaining an aEEG signal;

[0030] optimizing the aEEG signal and the TOI signal.

[0031] Preferably, the merging the raw aEEG signal and obtaining an aEEG signal comprises the steps of:

[0032] obtaining an Fp1 channel signal and an Fp2 channel signal in the EEG signal;

[0033] converting the Fp1 channel signal into a corresponding aEEG-Fp1 signal;

[0034] converting the Fp2 channel signal into a corresponding aEEG-Fp2 signal;

[0035] judging whether the TOI signal has one channel;

[0036] if yes, merging the aEEG-Fp1 signal and the aEEG-Fp2 signal into an aEEG signal;

[0037] if no, keeping the aEEG-Fp1 signal and the aEEG-Fp2 signal.

[0038] Preferably, the optimizing the aEEG signal and the TOI signal comprises the steps of:

[0039] performing low-pass filtering on the aEEG signal and the TOI signal;

[0040] performing value range scaling on the aEEG signal and the TOI signal.

[0041] Preferably, the calculating the time-frequency spectrum of the TOI signal and the aEEG signal comprises the steps of:

[0042] calculating the CWT time-frequency spectrum of the aEEG signal and the TOI signal respectively;

[0043] calculating the cross spectrum by using the CWT time-frequency spectrum of the aEEG signal and the TOI signal;

[0044] performing square and normalization processing on the cross spectrum and obtaining a wavelet consistency map of the aEEG signal and the TOI signal.

[0045] Preferably, the step of extracting the marked region of the TOI signal and the aEEG signal comprises the steps of:

[0046] obtaining a preset threshold and a time-frequency spectrum of the aEEG signal;

[0047] labeling the CWT time-frequency spectrum in the time-frequency spectrum according to the threshold;

[0048] extracting a corresponding region of the wavelet consistency map of the aEEG signal and the TOI signal according to the label.

[0049] Preferably, the step of extracting the NVC evaluation index of the TOI signal and the aEEG signal according to the labeled region data comprises the steps of:

[0050] obtaining an ultra-low frequency band, a low frequency band and a high frequency band;

[0051] calculating the average wavelet consistency of the TOI signal and the aEEG signal in the ultra-low frequency band, the low frequency band and the high frequency band;

[0052] calculating the average phase difference of the TOI signal and the aEEG signal in the ultra-low frequency band, the low frequency band and the high frequency band;

[0053] calculating the effective consistency percentage of the TOI signal and the aEEG signal.

[0054] In a second aspect, the present application provides a neurovascular coupling feature extraction device, comprising:

[0055] a signal acquisition module for acquiring a TOI signal and an EEG signal of the frontal lobe;

[0056] a signal processing module for pre-processing the TOI signal and the EEG signal and respectively obtaining a TOI signal and an aEEG signal;

[0057] a time-frequency spectrum calculation module for calculating a time-frequency spectrum of the TOI signal and the aEEG signal;

[0058] a marked region extraction module for extracting a marked region of the TOI signal and the aEEG signal;

[0059] an evaluation index extraction module for extracting an NVC evaluation index of the TOI signal and the aEEG signal according to the labeled region data.

[0060] In a third aspect, an electronic device is provided, comprising:

[0061] at least one processor; and,

[0062] a memory in communication with the at least one processor; wherein

[0063] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the neural vascular coupling feature extraction method of any of the preceding.

[0064] In a fourth aspect, a non-transitory computer-readable storage medium is provided, which stores computer instructions for causing the computer to perform the neural vascular coupling feature extraction method of any of the preceding.

[0065] The above technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art:

[0066] Based on the NVC physiological principle, the wavelet coherence of the TOI signal and the EEG signal collected from the frontal lobe is calculated by using the wavelet coherence analysis method, the active region is innovatively marked by using the CWT time-frequency spectrum of the aEEG signal, and the wavelet coherence region is extracted. The average wavelet coherence and the average phase difference in each frequency band are extracted by using the three divided frequency bands, and the effective coherence percentage parameter based on the global time ratio is extracted, a total of 7 NVC evaluation indexes. The present application has the following three beneficial effects:

[0067] (1) The CWT time-frequency spectrum of the aEEG signal is used to mark the time-frequency band of the active neural activity, so that the wavelet coherence region involved in the calculation is the active neural activity region, and the degree of connection between the NVC index value and the NVC physiological principle is improved;

[0068] (2) Three frequency bands are used, so that the frequency band utilization rate of the data is improved, which is helpful to find more effective information;

[0069] (3) The average wavelet coherence and the average phase difference of each frequency band are extracted, the former mainly reflects the significance of the hemodynamic response of neural activity, and the latter mainly reflects the timeliness of the hemodynamic response of neural activity, which is more diverse and comprehensive than the effective coherence percentage index. BRIEF DESCRIPTION OF DRAWINGS

[0070] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0072] Figure 1 is a schematic diagram of a feedforward model and a feedback model in the NVC mechanism in the prior art;

[0073] Figure 2 is a schematic diagram of a list of neurodegenerative diseases that may be caused by NVU function changes;

[0074] Figure 3 is a schematic diagram of aEEG signal and processed EEG signal calculation process;

[0075] Figure 4 is a schematic diagram of wavelet coherence time-frequency spectrum of aEEG signal and SctO2 signal of anencephalic newborns;

[0076] Figure 5 is a schematic diagram of effective coherence percentage comparison between anencephalic newborn group and newborns with encephalopathy group;

[0077] Figure 6 is a schematic diagram of a flow of a neural vascular coupling feature extraction method provided by an embodiment of the present application;

[0078] Figure 7 is a schematic diagram of comparison between TOI signal and EEG signal before and after preprocessing in a neural vascular coupling feature extraction method provided by an embodiment of the present application;

[0079] Figure 8 is a schematic diagram of CWT time-frequency spectrum of aEEG and wavelet coherence time-frequency spectrum of aEEG and TOI signal in a neural vascular coupling feature extraction method provided by an embodiment of the present application;

[0080] Figure 9 is a schematic diagram of CWT time-frequency spectrum marking of aEEG and wavelet coherence time-frequency spectrum marking of aEEG and TOI signal in a neural vascular coupling feature extraction method provided by an embodiment of the present application;

[0081] Figure 10 is a structural schematic diagram of a neural vascular coupling feature extraction device provided by an embodiment of the present application;

[0082] Figure 11 is a structural schematic diagram of an electronic device provided by the present application;

[0083] Figure 12 is a structural schematic diagram of a non-transitory computer readable storage medium provided by the present application. DETAILED DESCRIPTION

[0084] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0085] Figure 6 A flowchart of a neural vascular coupling feature extraction method provided by the embodiments of the present application is shown.

[0086] The present application provides a neural vascular coupling feature extraction method, which comprises the following steps:

[0087] S1: obtaining TOI signals and EEG signals of the frontal lobe;

[0088] Specifically, the TOI signals and the EEG signals of the frontal lobe can be obtained by a special medical device and stored on a corresponding storage medium.

[0089] S2: preprocessing the TOI signals and the EEG signals and respectively obtaining TOI signals and aEEG signals;

[0090] In the embodiments of the present application, the preprocessing of the TOI signals and the EEG signals and the respectively obtaining of the TOI signals and the aEEG signals comprise the following steps:

[0091] obtaining a first plurality of files of the TOI signals and a second plurality of files of the EEG signals;

[0092] performing multi-file merging and time axis synchronization on the first plurality of files and the second plurality of files;

[0093] converting the EEG signals into original aEEG signals;

[0094] merging the original aEEG signals and obtaining aEEG signals;

[0095] performing optimization processing on the aEEG signals and the TOI signals.

[0096] Specifically, due to data acquisition problems or data length limitations and other problems, the original TOI signal and the EEG signal can be divided into two or more files, that is, the TOI signal has a first plurality of files, and the EEG signal has a second plurality of files, so the present application needs to first merge the EEG signal and the TOI signal and synchronize the time axis, so that the lengths of the two groups of signals are equal and have a common time period. Then the EEG signal is converted into the original aEEG signal by using the original EEG to aEEG toolbox (WU-NEAT, Vesoulis, Z. A. et al, 2020).

[0097] In the embodiment of the present application, the merging the original aEEG signal and obtaining the aEEG signal comprises the steps of:

[0098] Obtaining the Fp1 channel signal and the Fp2 channel signal in the EEG signal;

[0099] Converting the Fp1 channel signal into a corresponding aEEG-Fp1 signal;

[0100] Converting the Fp2 channel signal into a corresponding aEEG-Fp2 signal;

[0101] Judging whether the TOI signal has one channel;

[0102] If yes, merging the aEEG-Fp1 signal and the aEEG-Fp2 signal into an aEEG signal;

[0103] If no, keeping the aEEG-Fp1 signal and the aEEG-Fp2 signal.

[0104] Specifically, this step mainly uses the Fp1 channel signal and the Fp2 channel signal in the EEG data, so the aEEG signals of the left and right channels are obtained, which are respectively denoted as the aEEG-Fp1 signal and the aEEG-Fp2 signal. Due to possible differences in the attachment of the NIRS probe, the TOI signal can have one or two channels. When the TOI has two channels, it represents the TOI signals of the left and right frontal lobes, respectively denoted as TOI-1 and TOI-2. At this time, this step will use the aEEG-Fp1 and TOI-1 signals and the aEEG-Fp2 and TOI-2 signals to calculate the left and right NVC indexes, respectively. If the TOI has only one channel, it represents the TOI signal of the entire frontal lobe, and at this time, the aEEG-Fp1 and aEEG-Fp2 signals need to be merged into an aEEG signal, and the NVC index representing the entire frontal lobe is calculated.

[0105] In the embodiment of the present application, the optimizing the aEEG signal and the TOI signal comprises the steps of:

[0106] low-pass filtering the aEEG signal and the TOI signal;

[0107] value range scaling the aEEG signal and the TOI signal.

[0108] Specifically, after obtaining the aEEG signal and the TOI signal, low-pass filtering and value range scaling are further needed to be performed on the signals to remove the influence of invalid frequency bands and absolute value differences on the calculation results. The TOI signal and the EEG signal before and after the preprocessing are compared as shown in Figure 7 .

[0109] S3: calculating time-frequency spectrums of the TOI signal and the aEEG signal;

[0110] In the embodiments of the present application, the calculating time-frequency spectrums of the TOI signal and the aEEG signal comprises the steps of:

[0111] calculating CWT time-frequency spectrums of the aEEG signal and the TOI signal respectively;

[0112] calculating cross spectrum by using the CWT time-frequency spectrums of the aEEG signal and the TOI signal;

[0113] squaring and normalizing the cross spectrum to obtain a wavelet consistency map of the aEEG signal and the TOI signal.

[0114] Specifically, after obtaining the preprocessed aEEG signal and the TOI signal, the CWT time-frequency spectrums of the aEEG signal and the TOI signal are calculated respectively, and then the cross spectrum is calculated by using the CWT time-frequency spectrums of the two signals and the wavelet consistency map of the aEEG signal and the TOI signal is obtained by squaring and normalizing. In Figure 8 , the CWT time-frequency spectrum of the aEEG signal and the wavelet consistency map of the aEEG signal and the TOI signal are exemplified.

[0115] S4: extracting a marked region of the TOI signal and the aEEG signal;

[0116] In the embodiments of the present application, the extracting a marked region of the TOI signal and the aEEG signal comprises the steps of:

[0117] obtaining a preset threshold and a time-frequency spectrum of the aEEG signal;

[0118] marking a CWT time-frequency spectrum in the time-frequency spectrum according to the threshold;

[0119] extracting a corresponding region of the wavelet consistency map of the aEEG signal and the TOI signal according to the marking.

[0120] Specifically, based on the physiological principle of NVC, the corresponding hemodynamic response when neurogenesis occurs needs to be obtained. Therefore, the application innovatively adds the aEEG high-energy region marking function. The CWT time-frequency spectrum of the aEEG signal is marked by setting a threshold, and the corresponding region of the wavelet consistency spectrum of the aEEG signal and the TOI signal is extracted according to the marking. Subsequent NVC evaluation index extraction will only use the data of the marked region for calculation. Figure 9 The use of the CWT time-frequency spectrum of the aEEG signal for marking and the use method in the wavelet consistency time-frequency spectrum are illustrated in the specific embodiments.

[0121] S5: Extract the NVC evaluation index of the TOI signal and the aEEG signal according to the marked region data.

[0122] In the embodiments of the application, the extraction of the NVC evaluation index of the TOI signal and the aEEG signal according to the marked region data includes the steps of:

[0123] Obtain the ultra-low frequency band, the low frequency band and the high frequency band;

[0124] Calculate the average wavelet consistency of the TOI signal and the aEEG signal in the ultra-low frequency band, the low frequency band and the high frequency band;

[0125] Calculate the average phase difference of the TOI signal and the aEEG signal in the ultra-low frequency band, the low frequency band and the high frequency band;

[0126] Calculate the effective consistency percentage of the TOI signal and the aEEG signal.

[0127] Specifically, the step includes seven NVC evaluation indexes in total, including average wavelet consistency in three frequency bands, average phase difference in three frequency bands, and effective consistency percentage. The purpose of dividing into multiple frequency bands is to consider that in different physiological states, the hemodynamic response may appear to lag behind the occurrence of neural activity or the response is not significant, and high-frequency response loss may occur in the frequency band, but low-frequency or ultra-low frequency response still exists, which is conducive to finding the index performance in the NVC critical situation. The average wavelet consistency reflects the time-frequency domain correlation of the aEEG signal and the TOI signal, and the value range is 0 to 1. The closer to 1, the stronger the hemodynamic response to neural activity; on the contrary, the closer to 0, the weaker the hemodynamic response to neural activity. The average phase difference reflects the time delay of the TOI signal relative to the aEEG signal, and the value range is -Π to Π. The closer to 0, the faster the hemodynamic response to neural activity. When the value is positive, it means that the hemodynamic response is later than the neural activity for a certain time. When the value is negative, it means that the hemodynamic response is earlier than the neural activity for a certain time. The effective consistency percentage has the same meaning as that in the work of DAS et al., and measures the percentage of the effective consistency period in the total period.

[0128] As Figure 10 The application provides a neurovascular coupling feature extraction device, which comprises:

[0129] A signal acquisition module 10 is configured to acquire a TOI signal and an EEG signal of a frontal lobe.

[0130] A signal processing module 20 is configured to pre-process the TOI signal and the EEG signal and correspondingly obtain a TOI signal and an aEEG signal.

[0131] A time-frequency spectrum calculation module 30 is configured to calculate a time-frequency spectrum of the TOI signal and the aEEG signal.

[0132] A marked region extraction module 40 is configured to extract a marked region of the TOI signal and the aEEG signal.

[0133] An evaluation index extraction module 50 is configured to extract NVC evaluation indexes of the TOI signal and the aEEG signal according to the marked region data.

[0134] The neurovascular coupling feature extraction device provided by the application can perform the neurovascular coupling feature extraction method provided by the above steps.

[0135] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

[0136] The following is for reference. Figure 11 The diagram illustrates a structural schematic of an electronic device 100 suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 11 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0137] like Figure 11 As shown, the electronic device 100 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 108 into a random access memory (RAM) 103. The RAM 103 also stores various programs and data required for the operation of the electronic device 100. The processing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0138] Typically, the following devices can be connected to I / O interface 105: input devices 106 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 107 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 108 including, for example, magnetic tapes, hard disks, etc.; and communication devices 109. Communication device 109 allows electronic device 100 to communicate wirelessly or wiredly with other devices to exchange data. Although electronic device 100 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0139] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication device 109, or installed from the storage device 108, or installed from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0140] Reference is made below to Figure 12 which shows a structural schematic diagram of a computer readable storage medium suitable for being used to implement the embodiments of the present disclosure, the computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the neurovascular coupling feature extraction method according to any one of the above.

[0141] The neurovascular coupling feature extraction method, device and storage medium provided by the present application are based on the NVC physiological principle, and the wavelet coherence of the TOI signal and the EEG signal collected by the prefrontal lobe is calculated by using the wavelet coherence analysis method. The active region is innovatively marked by using the CWT time-frequency spectrum of the aEEG signal, and is used for extracting the wavelet coherence region. The present application uses the three frequency bands which have been divided to extract the average wavelet coherence and the average phase difference in each frequency band, and to extract the effective coherence percentage parameter based on the global time proportion, a total of 7 NVC evaluation indexes. The present application has the following three beneficial effects:

[0142] (1) The CWT time-frequency spectrum of the aEEG signal is used to mark the time-frequency band of the active neural activity, so that the wavelet coherence region involved in the calculation is all the active neural activity region, and the degree of connection between the NVC index value and the NVC physiological principle is improved;

[0143] (2) Three frequency bands are used, so that the frequency band utilization rate of the data is improved, which is helpful to find more effective information;

[0144] (3) The average wavelet coherence and the average phase difference of each frequency band are extracted, the former mainly reflects the significance of the hemodynamic response to neural activity, and the latter mainly reflects the timeliness of the hemodynamic response to neural activity. The effective coherence percentage index is more diverse and comprehensive.

[0145] It has to be noted that, in the present document, relational terms are intended only to convey a possible relationship between elements or

[0146] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, which modifications and changes are to be understood as intended to be encompassed by the general scope of the application. Accordingly, the application is not to be limited to the above described or illustrated embodiments, but is intended to encompass all embodiments consistent with the principles of the application.

Claims

1. A method for extracting neurovascular coupling features, the method comprising: The method comprises the steps of: acquiring TOI signals and EEG signals of the frontal lobe; preprocessing the TOI signals and the EEG signals and correspondingly obtaining TOI signals and aEEG signals; calculating time-frequency spectrum of the TOI signals and the aEEG signals; extracting marked regions of the TOI signals and the aEEG signals; extracting NVC evaluation indexes of the TOI signals and the aEEG signals according to marked region data; the preprocessing the TOI signals and the EEG signals and correspondingly obtaining TOI signals and aEEG signals comprises the steps of: acquiring a first plurality of files of the TOI signals and a second plurality of files of the EEG signals; performing multi-file merging and timeline synchronization on the first plurality of files and the second plurality of files; converting the EEG signals into original aEEG signals; merging the original aEEG signals and obtaining aEEG signals; optimizing the aEEG signals and the TOI signals; the merging the original aEEG signals and obtaining aEEG signals comprises the steps of: acquiring Fp1 channel signals and Fp2 channel signals in the EEG signals; converting the Fp1 channel signals into corresponding aEEG-Fp1 signals; converting the Fp2 channel signals into corresponding aEEG-Fp2 signals; judging whether the TOI signals have one channel; if yes, merging the aEEG-Fp1 signals and the aEEG-Fp2 signals into aEEG signals; if no, keeping the aEEG-Fp1 signals and the aEEG-Fp2 signals; the extracting marked regions of the TOI signals and the aEEG signals comprises the steps of: acquiring a preset threshold and time-frequency spectrum of the aEEG signals; labeling CWT time-frequency spectrum in the time-frequency spectrum according to the threshold; extracting corresponding regions of wavelet consistency maps of the aEEG signals and the TOI signals according to the labels; the extracting NVC evaluation indexes of the TOI signals and the aEEG signals according to marked region data comprises the steps of: acquiring ultra-low frequency bands, low frequency bands and high frequency bands; calculating average wavelet consistency of the TOI signals and the aEEG signals in the ultra-low frequency bands, the low frequency bands and the high frequency bands; calculating average phase difference of the TOI signals and the aEEG signals in the ultra-low frequency bands, the low frequency bands and the high frequency bands; calculating effective consistency percentage of the TOI signals and the aEEG signals.

2. The neuro-vascular coupling feature extraction method of claim 1, wherein, the optimizing the aEEG signals and the TOI signals comprises the steps of: performing low-pass filtering processing on the aEEG signals and the TOI signals; performing value range scaling processing on the aEEG signals and the TOI signals. 3.The method of claim 1, wherein, the calculating time-frequency spectrum of the TOI signals and the aEEG signals comprises the steps of: calculating CWT time-frequency spectrum of the aEEG signals and the TOI signals respectively; calculating cross spectrum by using the CWT time-frequency spectrum of the aEEG signals and the TOI signals; The cross spectrum is squared and normalized to obtain a wavelet coherence map of the aEEG signal and the TOI signal.

4. A neurovascular coupling feature extraction apparatus adapted for use in the method of any one of claims 1-3, characterized in that, The method comprises the steps of: a signal acquisition module, configured to acquire a TOI signal and an EEG signal of a frontal lobe; a signal processing module, configured to pre-process the TOI signal and the EEG signal and correspondingly obtain a TOI signal and an aEEG signal; a time-frequency spectrum calculation module, configured to calculate a time-frequency spectrum of the TOI signal and the aEEG signal; a marked region extraction module, configured to extract a marked region of the TOI signal and the aEEG signal; an evaluation index extraction module, configured to extract an NVC evaluation index of the TOI signal and the aEEG signal according to the marked region data.

5. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for extracting neurovascular coupling features according to any one of claims 1-3.

6. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method for extracting neurovascular coupling features according to any one of claims 1-3.

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