CN114246595A

CN114246595A - Physiological state monitoring method, device and system based on amplitude integrated electroencephalogram

Info

Publication number: CN114246595A
Application number: CN202010956125.7A
Authority: CN
Inventors: 薛江; 耿艳芳; 万德勇
Original assignee: Second Hospital of Shandong University
Current assignee: Second Hospital of Shandong University
Priority date: 2020-09-11
Filing date: 2020-09-11
Publication date: 2022-03-29

Abstract

The invention discloses a physiological state monitoring method, a device and a system based on amplitude integrated electroencephalogram, wherein the method comprises the following steps: acquiring an amplitude variation rhythm of an amplitude integrated electroencephalogram based on amplitude characteristic data of the amplitude integrated electroencephalogram of at least one designated region of the brain of the monitoring subject in a designated time period; comparing the similarity of the amplitude change rhythm of the amplitude integrated electroencephalogram with the amplitude change rhythm of a preset amplitude integrated electroencephalogram model; and determining whether the physiological state of the monitored object is normal or not according to the similarity. The physiological state and health condition of the monitored subject can be more accurately evaluated.

Description

Physiological state monitoring method, device and system based on amplitude integrated electroencephalogram

Technical Field

The invention relates to the technical field of physiological state monitoring, in particular to a physiological state monitoring method, device and system based on amplitude integrated electroencephalogram.

Background

The amplitude-integrated electroencephalogram (aaeeg) technology is an effective means for monitoring the brain function state of a newborn, is widely used as an auxiliary examination means for neonatal brain injury at present, and provides objective evaluation indexes for doctors to screen neonatal hypoxia-ischemia encephalopathy, asphyxia, epilepsy, intracranial hemorrhage and hydrocephalus and encephalopathy treatment objects. The aEEG signal is usually in a strip-shaped track, the amplitude is generally between 10 and 40 muV, the aEEG signal width is related to the brain function state of the newborn, the track is usually wide during deep sleep of the newborn, and the track is narrow in a light sleep state or a waking state.

Currently, the analysis of the aaeeg signal mainly depends on manual realization, that is, a doctor visually judges and reads a background image of the aaeeg, and mainly looks at a background activity form (mainly paying attention to an upper boundary and a lower boundary), whether a sleep-wake cycle exists, whether an epileptic wave exists, and the like. In the existing aEEG manual interpretation, a doctor determines the upper and lower boundaries of an amplitude according to the shade of the color of a wave band on a monitor, and takes the high and low amplitudes corresponding to the darker part of the color as the upper and lower boundaries. However, the shade boundaries of the background image are usually not obvious, and different people judge different colors, so that different doctors have different interpretation results on the same aEEG signal, and different doctors also have different physiological states and health condition evaluations on the same neonate.

In recent years, although research work on automatic analysis of an eeg signal of a newborn has been started, the current work is limited to amplitude extraction of the eeg signal, and the physiological state and health condition of the newborn cannot be automatically analyzed based on the eeg signal.

Disclosure of Invention

In view of the above, the present invention has been developed to provide an amplitude integrated electroencephalogram based physiological condition monitoring method, apparatus and system that overcome or at least partially address the above-identified problems.

The embodiment of the invention provides a physiological state monitoring method based on amplitude integrated electroencephalogram, which comprises the following steps:

acquiring an amplitude variation rhythm of an amplitude integrated electroencephalogram based on amplitude characteristic data of the amplitude integrated electroencephalogram of at least one designated region of the brain of the monitoring subject in a designated time period;

comparing the similarity of the amplitude change rhythm of the amplitude integrated electroencephalogram with the amplitude change rhythm of a preset amplitude integrated electroencephalogram model;

and determining whether the physiological state of the monitored object is normal or not according to the similarity.

In some optional embodiments, the method further comprises:

acquiring potential change signals acquired by at least two electrode plates attached to the designated positions of the brain;

synthesizing the electric potential change signals of two designated brain positions in a designated area of a brain into an electroencephalogram of the designated area of the brain;

an amplitude integrated electroencephalogram for the designated area of the brain is generated by amplitude integration processing of the synthesized electroencephalogram.

In some alternative embodiments, the designated area of the brain comprises at least one of a left brain side, a right brain side, a back brain side; the designated brain positions comprise selected brain acupuncture point positions or monitoring positions determined according to the number of the electrode slices.

In some alternative embodiments, the step of obtaining the amplitude variation rhythm of the amplitude integrated electroencephalogram includes:

based on the amplitude characteristic data of the amplitude integrated electroencephalogram, an upper amplitude boundary, a lower amplitude boundary, a change in the upper amplitude boundary, a change in the lower amplitude boundary, a bandwidth, and a change in the bandwidth of the amplitude integrated electroencephalogram are obtained.

In some alternative embodiments, the amplitude-integrated electroencephalographic model includes amplitude-integrated electroencephalographic models of normal physiological states for different periods of growth and/or amplitude-integrated electroencephalographic models of a plurality of abnormal physiological states.

In some optional embodiments, the method further comprises:

aiming at different growth periods of the monitored objects, constructing amplitude integrated electroencephalogram models of normal physiological states in different growth periods through collected amplitude integrated electroencephalograms of a plurality of monitored objects in normal physiological states; and/or

And aiming at different growth periods of the monitored objects, constructing an amplitude integrated electroencephalogram model of each abnormal physiological state by acquiring amplitude integrated electroencephalograms of the monitored objects in each abnormal physiological state.

In some alternative embodiments, the similarity of the amplitude variation rhythm of the amplitude integrated electroencephalogram to the amplitude variation rhythm of the amplitude integrated electroencephalogram model for the respective growth period is compared according to the growth period of the monitored subject.

In some alternative embodiments, comparing the similarity of the amplitude variation rhythm of the amplitude integrated electroencephalogram to the amplitude variation rhythm of the preset amplitude integrated electroencephalogram model comprises determining the similarity according to at least one of the following comparison results:

comparing the amplitude upper boundary of the amplitude integrated electroencephalogram with a preset amplitude upper boundary threshold of an amplitude integrated electroencephalogram model;

comparing the amplitude lower boundary of the amplitude integrated electroencephalogram with a preset amplitude lower boundary threshold of the amplitude integrated electroencephalogram model;

comparing the amplitude bandwidth of the amplitude integrated electroencephalogram with an amplitude bandwidth threshold of a preset amplitude integrated electroencephalogram model;

comparing the change of the upper amplitude boundary of the amplitude integrated electroencephalogram with the reference change trend of the upper amplitude boundary of the preset amplitude integrated electroencephalogram model;

comparing the change of the amplitude lower boundary of the amplitude integrated electroencephalogram with a preset reference change trend of the amplitude lower boundary of the electroencephalogram model;

and comparing the change of the amplitude bandwidth of the amplitude integrated electroencephalogram with the reference change trend of the amplitude bandwidth of a preset amplitude integrated electroencephalogram model.

In some optional embodiments, determining whether the physiological state of the monitoring subject is normal according to the similarity includes:

judging an amplitude integrated electroencephalogram model closest to the amplitude change rhythm similarity of the amplitude integrated electroencephalogram according to the similarity;

determining that the physiological state of the monitored object is normal when the amplitude change rhythm similarity of the amplitude integrated electroencephalogram is closest to an amplitude integrated electroencephalogram model in a normal physiological state;

when the amplitude change rhythm similarity of the amplitude integrated electroencephalogram is closest to that of the abnormal physiological state, the amplitude integrated electroencephalogram model determines that the physiological state of the monitored object is abnormal.

In some optional embodiments, the determining, from the similarity, an amplitude integrated electroencephalogram model closest to an amplitude variation rhythm similarity of the amplitude integrated electroencephalogram, includes:

when the amplitude variation rhythm of the amplitude integrated electroencephalogram is closest to the similarity of the amplitude integrated electroencephalogram model of continuous normal voltage, determining that the amplitude integrated electroencephalogram model in the normal physiological state is closest to the similarity of the amplitude integrated electroencephalogram;

determining that the amplitude integrated electroencephalogram model of the abnormal physiological state is closest to the amplitude integrated electroencephalogram model of the discontinuous normal voltage, burst suppression, sustained low voltage, flat wave, or convulsion when the amplitude variation rhythm of the amplitude integrated electroencephalogram is closest to the amplitude integrated electroencephalogram model of the discontinuous normal voltage, burst suppression, sustained low voltage, flat wave, or convulsion.

In some optional embodiments, obtaining an amplitude variation rhythm of the amplitude integrated electroencephalogram, and comparing the amplitude variation rhythm of the amplitude integrated electroencephalogram with a similarity of the amplitude variation rhythm of a preset amplitude integrated electroencephalogram model, includes:

dividing the amplitude integrated electroencephalogram into a plurality of first graphic segments;

determining the amplitude distribution gravity center of the first graph segment according to the amplitude distribution condition of the first graph segment;

and matching the amplitude distribution gravity center of each first graph segment with the amplitude distribution gravity center of a corresponding second graph segment in a preset amplitude integration electroencephalogram model, and determining the similarity according to the matching result.

In some optional embodiments, matching the amplitude distribution gravity center of each first graph segment with the amplitude distribution gravity center of a corresponding second graph segment in a preset amplitude-integrated electroencephalogram model, and determining the similarity according to a matching result includes:

fitting the amplitude distribution barycenters of the first graphic segments into a first amplitude barycenter fitting curve; matching the first amplitude gravity center fitting curve with a second amplitude gravity center fitting curve of a preset amplitude integration electroencephalogram model, and determining the similarity according to the contact ratio of the first amplitude gravity center fitting curve and the second amplitude gravity center fitting curve; or

Generating a first amplitude gravity center distribution diagram based on the amplitude distribution gravity center of each first graph section; and matching the first amplitude gravity center distribution diagram with a second amplitude gravity center distribution diagram of a preset amplitude integration electroencephalogram model, and determining the similarity according to the coincidence degree of the amplitude distribution gravity centers in the first amplitude gravity center distribution diagram and the first amplitude gravity center distribution diagram.

In some optional embodiments, the method further comprises:

acquiring physiological state monitoring indexes of a monitored object, and determining whether the acquired physiological state monitoring indexes have high-risk factors which can cause abnormal physiological states according to high-risk factor indexes corresponding to various abnormal physiological states in different growth periods.

In some optional embodiments, when there is a high risk factor that may cause the abnormal physiological state, after the determining whether the physiological state of the monitoring subject is normal according to the similarity, the method further includes:

and determining whether the physiological state of the monitored object is normal or not according to the existing high-risk factors possibly causing the abnormal physiological state and the judgment result of whether the physiological state is normal or not determined according to the similarity.

In some optional embodiments, determining whether the physiological state of the monitoring subject is normal according to the existing high-risk factor which may cause the abnormal physiological state and the determination result of whether the physiological state determined according to the similarity is normal comprises:

and when the influence weight of the high-risk factors possibly causing the abnormal physiological state on at least one abnormal physiological state is greater than a set threshold and the physiological state of the monitored object is determined to be abnormal according to the similarity, determining that the physiological state of the monitored object is abnormal.

In some optional embodiments, the method further comprises:

acquiring amplitude integrated electroencephalograms in a plurality of specified time periods, and determining the physiological state change trend of the monitored object according to the physiological state of the monitored object corresponding to the amplitude integrated electroencephalograms in each time period.

The embodiment of the invention also provides a physiological state monitoring device based on amplitude integrated electroencephalogram, which comprises:

the acquisition module is used for acquiring amplitude change rhythm of the amplitude integrated electroencephalogram based on amplitude characteristic data of at least one brain designated area of the monitored object in at least one time period;

the comparison module is used for comparing the similarity between the amplitude change rhythm of the amplitude integrated electroencephalogram and the amplitude change rhythm of a preset amplitude integrated electroencephalogram model;

and the determining module is used for determining whether the physiological state of the monitored object is normal or not according to the similarity.

The embodiment of the invention also provides a physiological state monitoring system based on amplitude integrated electroencephalogram, which comprises: at least two electrode plates and a monitor;

the electrode slice is arranged at a designated position of the brain of the detection object;

the physiological state monitoring device based on amplitude integrated electroencephalogram is arranged in the monitor and used for acquiring the amplitude integrated electroencephalogram of at least one designated brain area of the monitored object in a designated time period through the electrode slice and determining whether the physiological state of the monitored object is normal or not based on the amplitude integrated electroencephalogram.

In some optional embodiments, the monitor further comprises a display for displaying the acquired electroencephalogram, the amplitude integrated electroencephalogram, and the physiological state of the monitored subject.

An embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored, and when the computer executable instructions are executed by a processor, the method for monitoring a physiological state based on amplitude-integrated electroencephalogram is implemented.

An embodiment of the present invention further provides a monitor, including: the device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the physiological state monitoring method based on the amplitude integrated electroencephalogram.

The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:

the method for monitoring physiological state based on amplitude integrated electroencephalogram provided by the embodiment of the invention can realize amplitude characteristic data of amplitude integrated electroencephalogram in a specified time period based on at least one specified brain region of a monitored object, obtain amplitude change rhythm of the amplitude integrated electroencephalogram, compare the amplitude change rhythm of the amplitude integrated electroencephalogram with the amplitude change rhythm of the existing electroencephalogram model, determine the similarity of the amplitude change rhythm, thereby finding out the electroencephalogram model closest to the amplitude integrated electroencephalogram, determine whether the physiological state of the monitored object is normal or abnormal according to the electroencephalogram model closest to the normal physiological state or the abnormal physiological state, further realize automatic analysis of aEEG signal to evaluate the physiological state and the health condition of the monitored object, and the method extracts the amplitude change rule based on amplitude by the analysis equipment, the interpretation difference between different doctors in the manual interpretation of the aEEG signals is avoided, and the physiological state and the health condition are more accurately evaluated.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 is an example of an electroencephalogram in an embodiment of the present invention;

FIG. 2 is an example of an amplitude integrated electroencephalogram in an embodiment of the present invention;

FIG. 3 is a flowchart of a physiological condition monitoring method based on amplitude-integrated electroencephalogram according to one embodiment of the present invention;

FIG. 4 is a flowchart illustrating an implementation of a physiological status monitoring method based on amplitude-integrated electroencephalogram according to a second embodiment of the present invention;

FIG. 5 is a diagram illustrating an example of a single channel mode collection position according to a second embodiment of the present invention;

fig. 6 is an exemplary diagram of a three-channel mode acquisition position in the second embodiment of the present invention;

FIG. 7 is an amplitude integrated electroencephalogram of a continuous positive voltage in a second embodiment of the present invention;

FIG. 8 is an amplitude integrated electroencephalogram of a discontinuous positive voltage in a second embodiment of the present invention;

FIG. 9 is an example of an amplitude integrated electroencephalogram for a sustained low voltage in a second embodiment of the present invention;

FIG. 10 is an amplitude-integrated electroencephalogram example of burst suppression in a second embodiment of the present invention;

FIG. 11 is an example of an amplitude integrated electroencephalogram of a flat wave in a second embodiment of the present invention;

FIG. 12 is an example of an amplitude integrated electroencephalogram of convulsions in the second embodiment of the present invention;

fig. 13 is a flowchart illustrating an implementation of a physiological status monitoring method based on amplitude-integrated electroencephalogram according to a third embodiment of the present invention;

FIG. 14 is an exemplary amplitude integrated electroencephalogram segmentation in accordance with the third embodiment of the present invention;

FIG. 15 is an example of an amplitude-centroid distribution plot of an amplitude-integrated electroencephalogram in a third embodiment of the present invention;

FIG. 16 is an exemplary graph of an amplitude-gravity center fit curve of an amplitude-integrated electroencephalogram in the third embodiment of the present invention;

fig. 17 is a flowchart illustrating an implementation of a physiological status monitoring method based on amplitude-integrated electroencephalogram according to a fourth embodiment of the present invention;

fig. 18 is a schematic structural diagram of a physiological condition monitoring device based on amplitude-integrated electroencephalogram in the fifth embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

In order to solve the problem that different doctors interpret differences in interpretation results of the aEEG signals in the prior art, and the evaluation of the physiological state and the health condition of a monitored object is more accurate, the embodiment of the invention provides the physiological state monitoring method based on the amplitude integrated electroencephalogram. The following is a detailed description by way of specific examples.

First, briefly, an Electroencephalogram (EEG) and an amplitude-integrated Electroencephalogram (aag) will be described.

The EEG is formed by summing up postsynaptic potentials generated synchronously by a large number of neurons during brain activity, and is a method for recording brain activity by using electrophysiological indices, and recording changes of electric waves during brain activity. EEG is generally generated by a signal of change in the electrical potential of the brain acquired by two electrode pads attached to the brain, reflecting the voltage difference between the two electrode pads, typically measured in μ V units, an example of which is shown in fig. 1.

The aag is generally obtained by performing an integrated modification of the EEG, and filtering, correcting, compressing, slowing, etc. the EEG to obtain an example of the aag, as shown in fig. 2.

Example one

An embodiment of the present invention provides a method for monitoring a physiological state based on amplitude-integrated electroencephalogram, the flow of which is shown in fig. 3, and the method includes the following steps:

step S11: an amplitude variation rhythm of an amplitude integrated electroencephalogram is acquired based on amplitude characteristic data of the amplitude integrated electroencephalogram for a specified time period of at least one specified region of the brain of the monitoring subject.

In the step, an electroencephalogram of at least one brain region of a monitoring object is obtained, amplitude integration is carried out on the electroencephalogram to obtain an amplitude integrated electroencephalogram, then amplitude feature extraction is carried out on the amplitude integrated electroencephalogram to obtain the amplitude change condition of the amplitude integrated electroencephalogram, and the amplitude change rhythm is obtained. Example of electroencephalogram referring to fig. 1, and example of amplitude-integrated electroencephalogram referring to fig. 2, by amplitude-integrating the amplitudes of the points in the electroencephalogram, the amplitude variation of the amplitude-integrated electroencephalogram can be obtained, and thus the brain activity of the monitored subject can be obtained.

Step S12: and comparing the similarity of the amplitude change rhythm of the amplitude integrated electroencephalogram with the preset amplitude change rhythm of the amplitude integrated electroencephalogram model.

After the amplitude variation rhythm of the amplitude integrated electroencephalogram is obtained, the amplitude variation rhythm of the amplitude integrated electroencephalogram can be compared with the amplitude variation rhythm of the preset amplitude integrated electroencephalogram model, and the similarity between the obtained amplitude variation rhythm of the amplitude integrated electroencephalogram and the amplitude variation rhythm of each preset amplitude integrated electroencephalogram model is determined, so that the amplitude integrated electroencephalogram model with the similarity meeting the conditions, such as the amplitude integrated electroencephalogram model with the highest similarity, is found. By comparing the amplitude variation rhythm of the obtained amplitude-integrated electroencephalogram with the amplitude variation rhythm of the amplitude-integrated electroencephalogram model, it can be determined whether the amplitude variation rhythm of the obtained amplitude-integrated electroencephalogram conforms to the amplitude-integrated electroencephalogram variation law and trend of the normal physiological state of the monitored subject, or is similar to the amplitude-integrated electroencephalogram variation law and trend of the various abnormal normal physiological states of the monitored subject.

When the monitored subject is in different physiological states, the amplitude variation rhythm of the amplitude integrated electroencephalogram is different. For example, in a newborn, the amplitude integrated electroencephalograms of premature infants and non-premature infants will exhibit different amplitude variation rhythms, and in a newborn with a normal physiological state and a newborn with an abnormal physiological state, the amplitude integrated electroencephalograms will exhibit different amplitude variation rhythms. Also, when the monitored subject is in different growth periods, the rhythm of amplitude change of the amplitude-integrated electroencephalogram is different. For example, a normal newborn or preterm infant may also exhibit different rhythms of amplitude variation in its amplitude-integrated electroencephalogram during the first, second, third, and … … weeks of life. The characteristics of the amplitude-integrated electroencephalogram of amplitude variation rhythms for different growth periods and different physiological states are described in detail later. Thus, the amplitude-integrated electroencephalographic model can include amplitude-integrated electroencephalographic models of normal physiological states for different periods of growth and/or amplitude-integrated electroencephalographic models of a plurality of abnormal physiological states.

The amplitude integrated electroencephalogram model can be obtained by monitoring and analyzing the normal physiological state and various abnormal physiological states of different monitored objects in different growth periods, and the amplitude integrated electroencephalogram models of the monitored objects in different growth periods and different physiological states can be obtained by analyzing and learning a large number of amplitude integrated electroencephalograms in different growth periods and different physiological states

During similarity comparison, the amplitude integrated electroencephalogram model in the normal physiological state and the amplitude integrated electroencephalogram models in various abnormal physiological states in corresponding growth periods can be obtained according to the growth periods of the monitored objects for comparison.

Step S13: and determining whether the physiological state of the monitored object is normal or not according to the similarity obtained by comparison.

And when the similarity between the obtained amplitude change rhythm of the amplitude integrated electroencephalogram and the amplitude change rhythm of the amplitude integrated electroencephalogram model in the normal physiological state of the monitored object is highest, the obtained amplitude change rhythm of the amplitude integrated electroencephalogram conforms to the amplitude integrated electroencephalogram change rule and trend of the normal physiological state of the monitored object, and the physiological state of the monitored object is determined to be normal.

When the similarity between the amplitude change rhythm of the amplitude integrated electroencephalogram and the amplitude change rhythm of the amplitude integrated electroencephalogram model in a certain abnormal physiological state of the monitored object is the highest, the obtained amplitude change rhythm of the amplitude integrated electroencephalogram conforms to the amplitude integrated electroencephalogram change rule and trend in the certain abnormal physiological state, and the abnormal physiological state of the monitored object is determined. And integrating an electroencephalogram model according to the amplitude of the abnormal physiological state to determine the corresponding abnormal physiological state.

Example two

The second embodiment of the present invention provides an optional implementation process of the physiological state monitoring method based on amplitude-integrated electroencephalogram, the flow of which is shown in fig. 4, and the implementation process includes the following steps:

step S21: and acquiring potential change signals acquired by at least two electrode plates attached to the designated positions of the brain.

When the potential change signals of the brain area are collected to synthesize the electroencephalogram, the electrode plates can be attached to the designated positions of the brain according to needs to collect the potential change conditions of the designated brain area. Generally, the potential signals collected by the two electrode slices are combined to obtain an electroencephalogram, and therefore, at least two electrode slices are usually used for collection, so as to combine one or more electroencephalograms.

The designated brain positions comprise selected brain acupuncture point positions or monitoring positions determined according to the number of electrode slices. When the electroencephalogram and amplitude-integrated electroencephalogram is obtained using the single channel mode as shown in fig. 5, the electrode pads may be attached to the positions of P3 and P4 of the parietal region for collection, and when the electroencephalogram and amplitude-integrated electroencephalogram is obtained using the three channel mode as shown in fig. 6, the electrode pads may be attached to the positions of C3, C4, P3, and P4 for collection. The positions of the brain acupoints for monitoring can be selected according to requirements, such as Fengchi acupoint, Baihui acupoint, Chengling acupoint and the like.

Step S22: the potential variation signals of two designated brain locations in a designated area of a brain are synthesized into an electroencephalogram of the designated area of the brain.

The designated area of the brain includes at least one of the left brain side, the right brain side, the back brain side. Referring to signals collected through electrode pads attached at the positions of the crown regions P3 and P4 shown in fig. 5, an electroencephalogram of the rear region of the head is synthesized. Referring to fig. 6, which collects signals through electrode pads attached at positions of C3, C4, P3, and P4, etc., C3 and P3, P3 and P4, P4 and C4 form three collection channels, respectively synthesizing electroencephalograms of three areas of the left side of the brain, the right side of the brain, and the rear side of the brain.

Step S23: by amplitude integration processing for the synthesized electroencephalogram, an amplitude integrated electroencephalogram for the designated area of the brain is generated.

Referring to fig. 5, the synthesized electroencephalogram of the back of the head is subjected to amplitude matching processing, and an amplitude-matched electroencephalogram of the back of the head is obtained. Referring to fig. 6, the synthesized electroencephalograms of the left brain, the right brain, and the back brain are amplitude-integrated to obtain amplitude-integrated electroencephalograms of the left brain, the right brain, and the back brain.

Step S24: an amplitude variation rhythm of an amplitude integrated electroencephalogram is acquired based on amplitude characteristic data of the amplitude integrated electroencephalogram for a specified time period of at least one specified region of the brain of the monitoring subject.

Amplitude integrated electroencephalograms obtained by different monitoring objects at different times are different, the distribution situation of amplitude points of each amplitude integrated electroencephalogram is different, so that each amplitude integrated electroencephalogram has respective amplitude characteristic data, and based on the distribution of a large number of amplitude points of the amplitude integrated electroencephalogram, the upper amplitude boundary and the lower amplitude boundary of the amplitude integrated electroencephalogram can be formed, so that the amplitude integrated electroencephalograms with different bandwidths are formed. Thus, the amplitude variation rhythm of the amplitude integrated electroencephalogram may include an upper amplitude boundary, a lower amplitude boundary, a variation in the upper amplitude boundary, a variation in the lower amplitude boundary, a bandwidth, a variation in bandwidth.

After the amplitude integrated electroencephalogram is generated, the upper amplitude boundary, the lower amplitude boundary, the change in the upper amplitude boundary, the change in the lower amplitude boundary, and the change in the bandwidth and the bandwidth of the amplitude integrated electroencephalogram are obtained based on the amplitude feature data of the amplitude integrated electroencephalogram.

Step S25: and comparing the similarity of the amplitude change rhythm of the amplitude integrated electroencephalogram with the preset amplitude change rhythm of the amplitude integrated electroencephalogram model.

After the amplitude variation rhythm is obtained by extracting the signal features of the amplitude integrated electroencephalogram, the amplitude variation rhythm can be compared with a pre-constructed model to determine the closest amplitude integrated electroencephalogram model.

Optionally, during the similarity comparison, according to the growth period of the monitoring object, the similarity between the amplitude change rhythm of the amplitude integrated electroencephalogram and the amplitude change rhythm of the amplitude integrated electroencephalogram model in the corresponding growth period is compared. And the amplitude integrated electroencephalogram model is compared with the amplitude integrated electroencephalogram model in the corresponding growth period, and a more accurate comparison result can be obtained when the physiological state is judged.

In the comparison, the amplitude integrated electroencephalogram model for comparison may be selected according to the growth period of the monitored object, for example, the amplitude integrated electroencephalogram model of the same growth period is selected to be compared with the acquired amplitude integrated electroencephalogram of the monitored object. The amplitude-integrated electroencephalography models for comparison include amplitude-integrated electroencephalography models for normal physiological states and/or amplitude-integrated electroencephalography models for a plurality of abnormal physiological states in the same growth period.

Comparing the similarity of the amplitude variation rhythm of the amplitude integrated electroencephalogram with the amplitude variation rhythm of the preset amplitude integrated electroencephalogram model, wherein the similarity is determined according to at least one of the following comparison results:

1) comparing the amplitude upper boundary of the amplitude integrated electroencephalogram with a preset amplitude upper boundary threshold of the amplitude integrated electroencephalogram model;

2) comparing the amplitude lower boundary of the amplitude integrated electroencephalogram with a preset amplitude lower boundary threshold of the amplitude integrated electroencephalogram model;

3) comparing the amplitude bandwidth of the amplitude integrated electroencephalogram with the amplitude bandwidth threshold of a preset amplitude integrated electroencephalogram model;

4) comparing the change of the upper amplitude boundary of the amplitude integrated electroencephalogram with the reference change trend of the upper amplitude boundary of the preset amplitude integrated electroencephalogram model;

5) comparing the change of the amplitude lower boundary of the amplitude integrated electroencephalogram with a preset reference change trend of the amplitude lower boundary of the electroencephalogram model;

6) and comparing the change of the amplitude bandwidth of the amplitude integrated electroencephalogram with the reference change trend of the amplitude bandwidth of the preset amplitude integrated electroencephalogram model.

By comparing the amplitude upper boundary, the amplitude lower boundary, the change of the amplitude upper boundary, the change of the amplitude lower boundary, the bandwidth and the change of the bandwidth of the amplitude integrated electroencephalogram with the preset amplitude integrated electroencephalogram model, the amplitude integrated electroencephalogram model with the amplitude upper boundary, the amplitude lower boundary and the change condition thereof and the bandwidth and the change condition thereof which are closest to each other can be found, so that whether the amplitude change rhythm of the amplitude integrated electroencephalogram conforms to the normal change rule or trend is determined.

Step S26: and judging the amplitude integrated electroencephalogram model closest to the amplitude change rhythm similarity of the amplitude integrated electroencephalogram according to the similarity.

When the amplitude variation rhythm of the amplitude integrated electroencephalogram is closest to the amplitude integrated electroencephalogram model similarity of continuous normal voltage, determining that the amplitude integrated electroencephalogram model in the normal physiological state is closest to the amplitude integrated electroencephalogram model similarity;

when the amplitude variation rhythm of the amplitude integrated electroencephalogram is closest to the amplitude integrated electroencephalogram model similarity of discontinuous normal voltage, burst suppression, sustained low voltage, flat wave and convulsion, determining that the amplitude integrated electroencephalogram model in the abnormal physiological state is closest to the amplitude integrated electroencephalogram similarity.

Step S27: and according to the amplitude integration electroencephalogram model with the closest similarity, determining whether the physiological state of the monitored object is normal.

When the amplitude change rhythm similarity of the amplitude integrated electroencephalogram is closest to the amplitude integrated electroencephalogram model in the normal physiological state, determining that the physiological state of the monitored object is normal; when the amplitude change rhythm similarity of the amplitude integrated electroencephalogram is closest to the amplitude integrated electroencephalogram model of the abnormal physiological state, the physiological state abnormality of the monitored object is determined.

Such as: the amplitude integration electroencephalogram model of the continuous normal voltage is as follows: the upper boundary of the amplitude is more than 10 muV, the lower boundary of the amplitude is more than 5 muV, and the bandwidth range is 5-10 muV; when the similarity between the acquired amplitude integrated electroencephalogram and the amplitude integrated electroencephalogram model is the highest, for example, the amplitude characteristics that the upper boundary range of the amplitude is greater than 10 μ V and the lower boundary range of the amplitude is greater than 5 μ V are met, and the bandwidth variation range is 5-10 μ V, the physiological state of the monitored object is considered to be normal. An example of an amplitude integrated electroencephalogram for a continuous positive producing voltage is shown in fig. 7.

Such as: the amplitude integration electroencephalogram model of the discontinuous normal voltage is as follows: the upper boundary of the amplitude is greater than 10 muV, the lower boundary of the amplitude is less than 5 muV, the bandwidth variation range is 30-40 muV, and the variation trend is that the bandwidth is increased and the variation range is wide; when the similarity between the acquired amplitude integrated electroencephalogram and the amplitude integrated electroencephalogram model is the highest, for example, the amplitude characteristics that the upper boundary range of the amplitude is >10 μ V, the lower boundary range of the amplitude is <5 μ V are met, and the bandwidth variation range is 30-40 μ V and the variation trend is increased, the physiological state of the monitored object is considered to be abnormal. An example of an amplitude integrated electroencephalogram for a discontinuous positive producing voltage is shown in fig. 8.

For another example: the amplitude integrated electroencephalogram model for sustained low voltage is: the upper amplitude boundary is <10 μ V and the lower amplitude boundary is <5 μ V, and when the similarity between the acquired amplitude integrated electroencephalogram and the amplitude integrated electroencephalogram model is the highest, for example, the amplitude characteristics that the upper amplitude boundary threshold range is <10 μ V and the lower amplitude boundary threshold range is <5 μ V are met, it is considered that the physiological state of the monitored subject is abnormal. An example of amplitude integrated electroencephalogram for sustained low voltages is shown in fig. 9.

For another example: the amplitude-integrated electroencephalogram model for burst suppression is as follows: the upper amplitude boundary is >10 μ V, the lower amplitude boundary is <5 μ V, and the lower boundary is limited in variation (e.g., the lower boundary is within a specified range); when the similarity between the acquired amplitude-integrated electroencephalogram and the amplitude-integrated electroencephalogram model is the highest, for example, the amplitude characteristics that the upper boundary range of the amplitude is >10 μ V, the lower boundary range of the amplitude is <5 μ V are met, and the change of the lower boundary is within a specified range, the physiological state of the monitored object is considered to be abnormal. An example of amplitude-integrated electroencephalogram for burst suppression is shown in fig. 10.

In addition, an example of an amplitude-integrated electroencephalogram of a flat wave or an electrical standstill is shown with reference to fig. 11. An example of amplitude-integrated electroencephalogram of convulsions is shown in fig. 12. Corresponding models also exist for flat waves, convulsions and the like, for example, the amplitude integrated electroencephalogram model of the flat wave is as follows: the upper boundary of the amplitude is <5 μ V, and the bandwidth variation range is <1 μ V, which is not listed here.

When the amplitude-integrated electroencephalogram of the test subject acquired in the first specified period of time (e.g., the first day) is similar to the amplitude-integrated electroencephalogram model for the sustained low voltage, and the amplitude-integrated electroencephalogram of the test subject acquired in the second specified period of time (e.g., the second day) is similar to the amplitude-integrated electroencephalogram model for the continuous normal voltage, it is considered that the physiological state of the monitored subject is changed from abnormal to normal.

In addition, a comprehensive amplitude-integrated electroencephalogram model can be established, such as an amplitude-integrated electroencephalogram model for converting an abnormal physiological state into a normal physiological state, or a physiological state change condition model for a plurality of days after the birth of a newborn or premature infant, and the like.

In some alternative embodiments, the amplitude integrated electroencephalogram model may be constructed by collecting amplitude integrated electroencephalograms of a plurality of monitoring subjects as samples, and performing feature learning and extraction on the plurality of samples.

For the amplitude integration electroencephalogram models in the normal physiological state in different growth periods, the amplitude integration electroencephalogram models in the normal physiological state in different growth periods can be constructed by collecting amplitude integration electroencephalograms of a plurality of monitoring objects in the normal physiological state aiming at different growth periods of the monitoring objects; for example, amplitude integrated electroencephalograms of a plurality of neonates with normal physiological states for 1 week of birth are collected, and an amplitude integrated electroencephalogram model with normal physiological states for 1 week of birth is constructed. For example, amplitude integrated electroencephalograms of a plurality of neonates with normal physiological states for 2 weeks of birth are collected, and an amplitude integrated electroencephalogram model with normal physiological states for 2 weeks of birth is constructed.

The electroencephalogram model integrated with the amplitude of each abnormal physiological state can be constructed by integrating the amplitude of a plurality of monitoring objects of each abnormal physiological state into the electroencephalogram model for different growth periods of the monitoring objects. The abnormal physiological state may include various diseases or abnormal conditions such as premature birth. Such as: the amplitude integrated electroencephalogram of a plurality of monitoring objects of the congenital development deformity of the brain constructs an amplitude integrated electroencephalogram model of the congenital development deformity of the brain in an abnormal physiological state. For example, amplitude-integrated electroencephalograms of a plurality of monitored subjects with brain injury are acquired, and an amplitude-integrated electroencephalogram model of an abnormal physiological state of brain injury is constructed.

In some alternative embodiments, amplitude integrated electroencephalograms in a plurality of specified time periods can be acquired, and the physiological state change trend of the monitoring object is determined according to the physiological state of the monitoring object corresponding to the amplitude integrated electroencephalograms in each time period.

For example, a plurality of days of amplitude-integrated electroencephalograms may be acquired, for example, three days, the amplitude-integrated electroencephalograms acquired each day may be compared with a preset amplitude-integrated electroencephalogram model, and according to the comparison result, if the amplitude-integrated electroencephalogram model in the severe abnormal physiological state on the first day is similar to the amplitude-integrated electroencephalogram model in the mild abnormal physiological state on the second day, and the amplitude-integrated electroencephalogram model in the normal physiological state on the third day is similar to the amplitude-integrated electroencephalogram model in the normal physiological state, it is indicated that the physiological state of the newborn gradually improves, and the abnormality tends to be normal.

In constructing the amplitude-integrated electroencephalogram model, in addition to the factors such as the upper and lower boundaries of the voltage change and the bandwidth, the conditions such as abrupt change in amplitude and abrupt change in bandwidth are also considered.

EXAMPLE III

An optional implementation process of the amplitude-integrated electroencephalogram-based physiological state monitoring method is provided in the third embodiment of the present invention, and a flow thereof is shown in fig. 13, and includes the following steps:

step S31: an amplitude integrated electroencephalogram of at least one designated region of the brain of the subject is acquired over a designated time period.

Step S32: the amplitude integrated electroencephalogram is divided into a number of first graphical segments.

The amplitude integrated electroencephalogram can be divided according to the time sequence, each graph segment corresponds to one section of amplitude integrated electroencephalogram with set duration, and the electroencephalogram can be divided into equal lengths or unequal lengths. Referring to the amplitude integrated electroencephalogram illustrated in fig. 14, one amplitude integrated electroencephalogram is divided into 6 graphic segments: graphic segment 1, graphic segment 2, graphic segment 3, graphic segment 4, graphic segment 5, and graphic segment 6.

Step S33: and determining the gravity center of the amplitude distribution of the first graph segment according to the amplitude distribution condition of the first graph segment.

Following the above example, the centroid of the amplitude distribution is determined for the amplitude distribution of each graph segment, the determined centroid of the amplitude distribution is shown as a dot in the figure, and one centroid of the amplitude distribution can be determined for each graph segment. There are various ways to determine the center of gravity of the amplitude distribution, and any one of them can be selected to determine the center of gravity of the amplitude distribution:

1) the outer contour of each graphic segment can be sketched out, and the graphic gravity center of the shape of the outer contour is calculated as the amplitude distribution gravity center.

2) The mean value calculation may be performed on the amplitude points included in each graph segment, the abscissa mean value and the ordinate mean value of the amplitude points included in the graph segment are calculated respectively, and points corresponding to the abscissa mean value and the ordinate mean value are taken as the amplitude distribution gravity center.

Step S34: and matching the amplitude distribution gravity center of each first graphic segment with the amplitude distribution gravity center of a corresponding second graphic segment in the preset amplitude integrated electroencephalogram model, and determining the similarity between the amplitude change rhythm of the amplitude integrated electroencephalogram and the amplitude change rhythm of the preset amplitude integrated electroencephalogram model according to the matching result.

For the preset amplitude integrated electroencephalogram model, the amplitude distribution gravity center of the amplitude integrated electroencephalogram model can be determined in advance so as to be used in matching, when the amplitude integrated electroencephalogram is subjected to image segment division, preferably, the corresponding length division is carried out on the amplitude integrated electroencephalogram based on the image segment length divided by the amplitude integrated electroencephalogram model, and each first image segment divided by the amplitude integrated electroencephalogram is in one-to-one correspondence with each second image segment divided by the amplitude integrated electroencephalogram model.

When the center of gravity of the amplitude distribution is matched in this step, there may be a plurality of optional matching modes, which are exemplified below:

for example, one: and integrating the amplitude distribution centers of all the graph sections into an amplitude center distribution graph, and matching the amplitude center distribution graph.

Generating a first amplitude gravity center distribution diagram based on the amplitude distribution gravity center of each first graph section; and matching the first amplitude gravity center distribution diagram with a second amplitude gravity center distribution diagram of a preset amplitude integrated electroencephalogram model, and determining the similarity of the amplitude change rhythm of the amplitude integrated electroencephalogram and the amplitude change rhythm of the preset amplitude integrated electroencephalogram model according to the coincidence degree of the amplitude distribution gravity centers in the first amplitude gravity center distribution diagram and the first amplitude gravity center distribution diagram.

Fig. 15 is a diagram showing an amplitude-gravity-center distribution diagram in which the amplitude distribution centers of the respective pattern segments in the amplitude-integrated electroencephalogram are integrated. When the amplitude gravity center distribution diagram is integrated, the amplitude distribution gravity centers of all graphic segments of the amplitude integrated electroencephalogram can be directly extracted according to the position coordinates in the amplitude integrated electroencephalogram and integrated into the amplitude gravity center distribution diagram in a single coordinate system; or after being extracted, the horizontal coordinate and/or the vertical coordinate are/is scaled according to a certain proportion based on the position coordinates of the amplitude distribution gravity center in the amplitude integrated electroencephalogram, and the amplitude distribution gravity center distribution diagram is integrated in a single coordinate system. Optionally, whether scaling is needed or not can be determined based on whether scaling is performed on the amplitude gravity center distribution diagram of the amplitude integration electroencephalogram model, scaling is performed on the amplitude gravity center coordinate positions of the amplitude integration electroencephalogram model and the amplitude integration electroencephalogram model in an equal proportion, and corresponding matching can be performed more conveniently.

And matching the integrated amplitude gravity center distribution diagram with an amplitude gravity center distribution diagram of a preset amplitude integrated electroencephalogram model, and determining the similarity according to the coincidence degree of the amplitude gravity center distribution diagrams, wherein the amplitude gravity center distribution diagram can be determined according to the distance between the integrated amplitude gravity center distribution diagram and a corresponding gravity center point in the preset amplitude integrated electroencephalogram model. For example, the coincidence degree of each pair of corresponding gravity center points is determined according to the distance between each pair of corresponding gravity center points, and the coincidence degree of all the gravity center points is averaged or weighted average calculated to obtain the coincidence degree of the amplitude gravity center distribution diagram.

Example two: and fitting the amplitude distribution gravity centers of all the graph segments into an amplitude gravity center fitting curve, and matching the fitted curve.

Fitting the amplitude distribution gravity centers of the first graphic segments into a first amplitude gravity center fitting curve; matching the first amplitude gravity center fitting curve with a second amplitude gravity center fitting curve of a preset amplitude integration electroencephalogram model, and determining the similarity of the amplitude change rhythm of the amplitude integration electroencephalogram and the amplitude change rhythm of the preset amplitude integration electroencephalogram model according to the coincidence degree of the first amplitude gravity center fitting curve and the second amplitude gravity center fitting curve;

as shown in fig. 16, the amplitude distribution center of gravity of each graph segment in the amplitude-integrated electroencephalogram is fitted to form an amplitude center of gravity fitting curve, the fitted amplitude center of gravity fitting curve is matched with the amplitude center of gravity fitting curve of the preset amplitude-integrated electroencephalogram model, and the similarity is determined according to the degree of coincidence of the curves. The contact ratio of the curves can be determined according to the length of the overlapped part of the two curves, or according to the distance average value of the two curves, and the distance average value of the two curves can be obtained by averaging or weighted averaging the distances between a plurality of groups of position points corresponding to the selected positions on the two curves.

The above-mentioned steps S32 to S34 realize the acquisition of the amplitude variation rhythm of the amplitude integrated electroencephalogram and the comparison of the similarity between the amplitude variation rhythm of the amplitude integrated electroencephalogram and the amplitude variation rhythm of the preset amplitude integrated electroencephalogram model.

Step S35: and determining whether the physiological state of the monitored object is normal or not according to the similarity obtained by comparison.

The implementation process of some steps in this embodiment has been described in detail in the first and second embodiments, and reference is made to the related description, which is not repeated herein.

Example four

An optional implementation process of the amplitude-integrated electroencephalogram-based physiological state monitoring method is provided in the fourth embodiment of the present invention, and a flow thereof is shown in fig. 17, and includes the following steps:

step S41: and acquiring a physiological state monitoring index of the monitored object.

Step S42: and determining whether the acquired physiological state monitoring index has high-risk factors which can cause abnormal physiological states.

Different abnormal physiological states during different growth periods may be caused by different high risk factors, such as neonatal brain injury, factors associated with the occurrence of brain injury include, but are not limited to, continuous positive airway pressure (nCPAP), constant frequency ventilation, high frequency ventilation and apnea, respiratory distress syndrome (NRDS), hypercapnia, metabolic acidosis, hyperglycemia, anemia, Patent Ductus Arteriosus (PDA), wherein NRDS, PDA, high frequency concussion ventilation (HFO) are important high risk factors for the occurrence of brain injury, and other factors may also be high risk factors for brain injury.

Therefore, after the physiological state index of the monitored object is acquired, whether a high-risk factor possibly causing a certain abnormal physiological state exists can be judged.

Step S43: an amplitude variation rhythm of an amplitude integrated electroencephalogram is acquired based on amplitude characteristic data of the amplitude integrated electroencephalogram for a specified time period of at least one specified region of the brain of the monitoring subject.

Step S44: and comparing the similarity of the amplitude change rhythm of the amplitude integrated electroencephalogram with the preset amplitude change rhythm of the amplitude integrated electroencephalogram model.

Step S45: and determining whether the physiological state of the monitored object is normal or not according to the similarity obtained by comparison.

Step S46: and determining whether the physiological state of the monitored object is normal or not according to the judgment result of whether the high-risk factor possibly causing the abnormal physiological state exists or not and the judgment result of whether the physiological state is normal or not according to the similarity.

Optionally, the steps S41 to S42 may be executed first, after the screening of the high-risk factor is performed, if the high-risk factor exists, the steps S43 to S46 are executed again, the obtaining and the judgment of the amplitude integrated electroencephalogram are performed, and then the judgment results of the two are integrated to finally determine whether the physiological state of the monitored object is normal.

Optionally, the steps S43 to S46 may be executed first, the amplitude integrated electroencephalogram is obtained and determined, if the physiological state of the monitored object is determined to be abnormal according to the amplitude integrated electroencephalogram, the steps S41 to S42 are executed, after the high risk factor is screened, the determination results of the two are synthesized, and finally whether the physiological state of the monitored object is normal or not is determined.

Optionally, the screening of the high risk factor and the acquisition and judgment of the amplitude integrated electroencephalogram may also be performed simultaneously, that is, the two processes of step S41-step S42 and step S43-step S46 are performed simultaneously, and then the judgment results of the two processes are integrated to finally determine whether the physiological state of the monitored subject is normal.

In this step, when there is no high-risk factor, it may be determined whether the physiological state of the monitoring object is normal only according to the similarity of the amplitude integrated electroencephalogram. Or the physiological state monitoring index of the monitored object and the similarity of the amplitude integrated electroencephalogram can be comprehensively judged, for example, when the physiological state of the monitored object is judged to be abnormal by the similarity of the amplitude integrated electroencephalogram, whether the physiological state monitoring index associated with the monitored object is normal or not is judged, so that the judgment result is more accurate

In this step, when there is a high-risk factor, it is determined whether the physiological state of the monitoring subject is normal or not, based on the high-risk factor that causes the abnormal physiological state and the determination result of whether the physiological state is normal or not, which is determined based on the similarity of the amplitude-integrated electroencephalograms. For example, a high-risk factor of brain injury exists, and the electroencephalogram is integrated based on the amplitude to determine that the brain injury exists, and then the brain loss of the monitored object is judged, so that the result is more accurate. Or high-risk factors of the congenital heart disease exist, and the heart is determined to be abnormal based on the amplitude integrated electroencephalogram, and then the monitored object is judged to have the congenital heart disease, so that the result is more accurate.

Specifically, when the influence weight of the high-risk factor possibly causing the abnormal physiological state on at least one abnormal physiological state is greater than the set threshold, and the physiological state of the monitored object is determined to be abnormal according to the similarity, the physiological state of the monitored object is determined to be abnormal. The specific type of the physiological state abnormality of the monitoring object, namely which physiological state abnormality is, can be further determined according to the type of the abnormal physiological state influenced by the high-risk factor larger than the set threshold.

Specifically, according to which abnormal physiological state influence weight is greater than a set threshold value by a high-risk factor causing the abnormal physiological state, it is specifically determined which physiological state abnormality the possibly existing physiological state abnormality is specifically. For example, when the influence weight of the brain injury caused by the high-risk factor a is greater than a set threshold, it is considered that the brain injury may exist, and when the influence weight of the brain injury caused by the high-risk factor B is not greater than the set threshold, it is considered that the brain injury may not exist, and then, the judgment is further performed in combination with the judgment result of the similarity, for example, when the brain injury is judged to possibly exist according to the high-risk factor a and the physiological state of the monitoring object is determined to be abnormal according to the similarity, it is determined that the physiological state of the monitoring object is abnormal and is the brain injury.

The implementation process of some steps in this embodiment has been described in detail in the first, second, and third embodiments, and reference is made to the related description, which is not repeated herein.

EXAMPLE five

Based on the same inventive concept, the embodiment of the present invention further provides an amplitude-integrated electroencephalogram-based physiological state monitoring device, which can be disposed in a monitor, and the structure of the device is shown in fig. 18, and the device comprises:

an obtaining module 131, configured to obtain an amplitude change rhythm of an amplitude integrated electroencephalogram based on amplitude characteristic data of an amplitude integrated electroencephalogram of at least one designated brain region of a monitored subject in at least one time period;

a comparison module 132 for comparing the similarity between the amplitude variation rhythm of the amplitude integrated electroencephalogram and the amplitude variation rhythm of the preset amplitude integrated electroencephalogram model;

and the determining module 133 is configured to determine whether the physiological status of the monitored object is normal according to the compared similarity.

Optionally, the apparatus further includes a model building module 134, configured to build, for different growth periods of the monitored object, amplitude-integrated electroencephalogram models in normal physiological states in different growth periods through the collected amplitude-integrated electroencephalograms of the multiple monitored objects in normal physiological states; and/or constructing an amplitude integrated electroencephalogram model of each abnormal physiological state by acquiring amplitude integrated electroencephalograms of a plurality of monitoring objects of each abnormal physiological state aiming at different growth periods of the monitoring objects.

Optionally, the apparatus further includes a high-risk factor monitoring module 135, which acquires a physiological status monitoring index of the monitored object, and determines whether there is a high-risk factor causing an abnormal physiological status in the acquired physiological status monitoring index according to high-risk factor indexes corresponding to various abnormal physiological statuses in different growth periods. Correspondingly, the determining module 133 is further configured to determine whether the physiological state of the monitored subject is normal according to the existing high-risk factor causing the abnormal physiological state and the determination result of whether the physiological state determined according to the similarity is normal, when the high-risk factor causing the abnormal physiological state exists.

Optionally, the determining module 133 is further configured to determine a physiological state change trend of the monitoring subject according to the physiological state of the monitoring subject corresponding to the amplitude integrated electroencephalogram in each time period based on the amplitude integrated electroencephalograms in the plurality of specified time periods acquired by the acquiring module 131.

Based on the same inventive concept, the embodiment of the present invention further provides an amplitude-integrated electroencephalogram-based physiological state monitoring system, comprising: at least two electrode plates and a monitor;

the physiological state monitoring device based on the amplitude integrated electroencephalogram is arranged in the monitor and used for acquiring the amplitude integrated electroencephalogram of at least one designated brain area of the monitored object in a designated time period through the electrode slice and determining whether the physiological state of the monitored object is normal or not based on the amplitude integrated electroencephalogram.

The monitor also includes a display for displaying the acquired electroencephalogram, amplitude integrated electroencephalogram, and the physiological state of the monitored subject.

The embodiment of the invention also provides a computer storage medium, wherein computer executable instructions are stored in the computer storage medium and are executed by a processor to realize the physiological state monitoring method based on the amplitude integrated electroencephalogram.

An embodiment of the present invention further provides a monitor, including: the physiological state monitoring method based on the amplitude-integrated electroencephalogram is realized when the processor executes the program.

With regard to the apparatus and system in the above embodiments, the specific manner in which each module or device performs operations has been described in detail in relation to the embodiments of the method, and will not be described in detail herein.

Unless specifically stated otherwise, terms such as processing, computing, calculating, determining, displaying, or the like, may refer to an action and/or process of one or more processing or computing systems or similar devices that manipulates and transforms data represented as physical (e.g., electronic) quantities within the processing system's registers and memories into other data similarly represented as physical quantities within the processing system's memories, registers or other such information storage, transmission or display devices. Information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.

In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.

For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.

What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".

Claims

1. A method for monitoring a physiological condition based on amplitude-integrated electroencephalography, comprising:

2. The method of claim 1, further comprising:

3. The method of claim 1, wherein comparing the similarity of the amplitude variation rhythm of the amplitude integrated electroencephalogram to the amplitude variation rhythm of the preset amplitude integrated electroencephalogram model comprises determining the similarity based on at least one of:

4. The method of claim 1, wherein obtaining the amplitude variation rhythm of the amplitude integrated electroencephalogram, and comparing the amplitude variation rhythm of the amplitude integrated electroencephalogram to a similarity of the amplitude variation rhythm of a preset amplitude integrated electroencephalogram model comprises:

5. The method of claim 1, further comprising:

acquiring physiological state monitoring indexes of a monitored object, and determining whether the acquired physiological state monitoring indexes have high-risk factors possibly causing abnormal physiological states or not according to high-risk factor indexes corresponding to various abnormal physiological states in different growth periods;

and when high-risk factors possibly causing abnormal physiological states exist, determining whether the physiological states of the monitored objects are normal or not according to the high-risk factors possibly causing the abnormal physiological states and the judgment result of whether the physiological states are normal or not according to the similarity.

6. The method of any of claims 1-5, further comprising:

7. An amplitude-integrated electroencephalogram-based physiological condition monitoring device, comprising:

8. An amplitude-integrated electroencephalogram-based physiological condition monitoring system, comprising: at least two electrode plates and a monitor;

9. A computer storage medium having computer-executable instructions stored thereon that, when executed by a processor, implement the amplitude integrated electroencephalogram-based physiological state monitoring method of any one of claims 1-6.

10. A monitor, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the amplitude integrated electroencephalogram based physiological state monitoring method of any one of claims 1 to 6.