Abnormal state detection device and method

Abstract

The application relates to an abnormal state detection device and method. The detection device comprises a control module and a signal acquisition module, and the signal acquisition module and the control module are in communication connection. The signal acquisition module is used for acquiring electroencephalogram signals and muscle signals of a to-be-detected object; the control module is used for receiving the electroencephalogram signals and the muscle signals, and determining an abnormal state of the to-be-detected object according to the abnormal type of the to-be-detected object and the muscle signals in the case of electroencephalogram signal abnormality. The abnormal state detection device provided by the application can improve the accuracy of detecting the abnormal state of the to-be-detected object.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical equipment, in particular to an abnormal state detection device and method. BACKGROUND

[0002] Epilepsy is a chronic disease caused by the sudden abnormal discharge of neurons in the brain, resulting in temporary dysfunction of the brain. The site of seizure and the cause of seizure are various, and the patient may be attacked at any time and any place due to an uncertain cause. If the patient cannot be found in time and appropriate treatment is not taken, the patient will suffer physical or psychological harm.

[0003] In the traditional technology, the electroencephalogram carried by the to-be-detected object is used to collect the brain electrical signals of the to-be-detected object for a long time, and the real-time seizure state of the to-be-detected object needs to be observed by the experience of the doctor to effectively analyze the epilepsy state of the to-be-detected object.

[0004] However, the above method relies too much on the experience of the doctor, and there is a problem of inaccurate analysis of the epilepsy state. SUMMARY

[0005] Therefore, it is necessary to provide an abnormal state detection device and method aiming at the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides an abnormal state detection device, which comprises a control module and a signal acquisition module.

[0007] The signal acquisition module is configured to acquire the brain electrical signals and muscle signals of the to-be-detected object.

[0008] The control module is configured to receive the brain electrical signals and muscle signals, and determine the abnormal state of the to-be-detected object according to the abnormal type of the to-be-detected object and the muscle signals if the brain electrical signals are abnormal.

[0009] In one embodiment, the control module is specifically configured to determine that the abnormal state of the to-be-detected object is a non-occurrence state if the abnormal type is a first type and the fluctuation amplitude of the muscle signals is within a preset range.

[0010] In one embodiment, the control module is specifically configured to determine that the abnormal state of the to-be-detected object is a non-occurrence state if the abnormal type is a second type and the fluctuation amplitude of the muscle signals is not within the preset range.

[0011] In one embodiment, the signal acquisition module is further configured to acquire the electromyographic signals and acceleration signals.

[0012] The control module is configured to receive the electromyographic signals and acceleration signals, and determine the fluctuation amplitude of the muscle signals according to the electromyographic signals and acceleration signals.

[0013] In one of the embodiments, the control module is further configured to input the abnormal type and the muscle signal into the abnormal state determination model to obtain the abnormal state of the to-be-detected object.

[0014] In one of the embodiments, the control module is specifically configured to obtain abnormal type samples and muscle signal samples; and input the abnormal type samples and the muscle signal samples into the initial abnormal state determination model to obtain the abnormal state determination model.

[0015] In one of the embodiments, the signal acquisition module comprises an electroencephalogram signal acquisition component and a muscle signal acquisition component.

[0016] The electroencephalogram signal acquisition component is configured to acquire the electroencephalogram signal of the to-be-detected object.

[0017] The muscle signal acquisition component is configured to acquire the muscle signal of the to-be-detected object.

[0018] In one of the embodiments, the muscle signal acquisition component comprises an electromyogram signal acquisition unit and an acceleration acquisition unit.

[0019] The electromyogram signal acquisition unit is configured to acquire the electromyogram signal of the to-be-detected object.

[0020] The acceleration acquisition unit is configured to acquire the acceleration signal of the to-be-detected object.

[0021] In a second aspect, an embodiment of the present application provides a method for abnormal state detection by using the abnormal state detection device provided in the first aspect, and the method comprises the following steps:

[0022] Acquiring the electroencephalogram signal and the muscle signal of the to-be-detected object by using the signal acquisition module.

[0023] In the case that the electroencephalogram signal is abnormal, determining the abnormal state of the to-be-detected object according to the abnormal type and the muscle signal of the to-be-detected object by using the control module.

[0024] In one of the embodiments, in the case that the electroencephalogram signal is abnormal, determining the abnormal state of the to-be-detected object according to the abnormal type and the muscle signal of the to-be-detected object by using the control module comprises:

[0025] If it is determined by the control module that the abnormal type is the first type and the fluctuation amplitude of the muscle signal is within the preset range, then it is determined that the abnormal state of the to-be-detected object is the non-occurrence state.

[0026] If it is determined by the control module that the abnormal type is the second type and the fluctuation amplitude of the muscle signal is not within the preset range, then it is determined that the abnormal state of the to-be-detected object is the non-occurrence state.

[0027] The embodiment of the present application provides a device and a method for detecting an abnormal state. The device comprises a control module and a signal acquisition module. The signal acquisition module is used for acquiring an electroencephalogram signal and a muscle signal of a to-be-detected object. The control module is used for receiving the electroencephalogram signal and the muscle signal, and determining an abnormal state of the to-be-detected object according to an abnormal type and the muscle signal in the case that the electroencephalogram signal is abnormal. In the embodiment, the control module can determine whether the electroencephalogram signal of the to-be-detected object is abnormal by analyzing the electroencephalogram signal of the to-be-detected object. In the case that the electroencephalogram signal of the to-be-detected object is abnormal, the control module can distinguish whether the electroencephalogram signal is caused by an abnormality of the to-be-detected object or caused by a daily movement of the to-be-detected object according to the abnormal type and the muscle signal. Therefore, the electroencephalogram signal caused by the daily movement of the to-be-detected object can be avoided to be mistaken as the electroencephalogram signal caused by the abnormality of the to-be-detected object, and the to-be-detected object is avoided to be misjudged as having the abnormality. Therefore, the accuracy of detecting the abnormal state of the to-be-detected object can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0029] Figure 1 A structural schematic diagram of the device for detecting an abnormal state provided by one embodiment is shown in the figure;

[0030] Figure 2 A structural schematic diagram of the control module provided by one embodiment is shown in the figure;

[0031] Figure 3 A structural schematic diagram of the device for detecting an abnormal state provided by another embodiment is shown in the figure;

[0032] Figure 4 A structural schematic diagram of the device for detecting an abnormal state provided by another embodiment is shown in the figure;

[0033] Figure 5 A step flow schematic diagram of training the abnormal state determination model provided by one embodiment is shown in the figure;

[0034] Figure 6 A step flow schematic diagram of the method for detecting an abnormal state provided by one embodiment is shown in the figure;

[0035] Figure 7 A step flow schematic diagram of the method for detecting an abnormal state provided by another embodiment is shown in the figure;

[0036] Explanation of reference signs:

[0037] 100, control module; 200, signal acquisition module; 210, electroencephalogram signal acquisition component; 220, muscle signal acquisition component; 221, electromyogram signal acquisition unit; 222, acceleration acquisition unit. DETAILED DESCRIPTION

[0038] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in a number of different ways beyond the specific embodiments described and it is therefore contemplated to cover all such modifications as fall within the scope of the application. It is to be understood that the application is not limited in its application to the details set forth in the following description.

[0039] In this document, the ordinal numbers for components, such as "first", "second", etc., are used only to distinguish the described objects, and do not have any sequential or technical meaning. In this document, "connection" and "coupling" include direct and indirect connections (couplings) unless otherwise specified.

[0040] The technical solutions of the present application and how the technical solutions solve the technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0041] Please refer to Figure 1 An embodiment of the present application provides an abnormal state detection device, which comprises a control module 100 and a signal acquisition module 200, and the signal acquisition module 200 and the control module 100 are in communication connection. The signal acquisition module 200 and the control module 100 can be connected by wire or wirelessly, and the embodiment does not limit the connection mode between the signal acquisition module 200 and the control module 100 as long as the functions can be realized.

[0042] The signal acquisition module 200 is used to acquire the brain electrical signals and muscle signals of the to-be-detected object. The brain electrical signals refer to the brain waves of the to-be-detected object. The muscle signals include electromyography signals and acceleration signals. The electromyography signals are the superposition of motor unit action potentials in time and space in numerous muscle fibers. The surface electromyography signals are the comprehensive effects of electrical activities on the skin surface of the superficial muscle and nerve trunk, and can reflect the activities of the neuromuscular system to a certain extent. The acceleration signals can represent whether the limbs of the to-be-detected object have a large amplitude of movement. In use, the to-be-detected object wears the signal acquisition module 200, so that the signal acquisition module 200 acquires the brain electrical signals and muscle signals of the to-be-detected object. The specific structure of the signal acquisition module 200 is not limited in the embodiment, as long as the function thereof can be realized.

[0043] The control module 100 is used to receive the brain electrical signals and muscle signals, and determine the abnormal state of the to-be-detected object according to the abnormal type of the to-be-detected object and the muscle signals when the brain electrical signals are abnormal.

[0044] The control module 100 can be a terminal, which can be but is not limited to various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device and the like. The specific structure of the control module 100 can be shown as in Figure 2

[0045] The abnormal type of the to-be-detected object refers to the symptoms when the to-be-detected object has an attack. For example, the abnormal type of the to-be-detected object is the epilepsy type, which refers to the symptoms of the to-be-detected object when the to-be-detected object has an epilepsy, such as loss of consciousness, loss of muscle tone, and rigidity, clonic convulsions and the like. Generally, the symptoms of the to-be-detected object when the to-be-detected object has an epilepsy do not change.

[0046] The abnormal type of the to-be-detected object can be stored in the memory of the control module 100, which can be directly obtained from the memory by the control module 100 when needed. The abnormal type of the to-be-detected object can be stored in the signal acquisition module 200, which is sent to the control module 100 by the signal acquisition module 200 of the to-be-detected object when the brain electrical signals and muscle signals acquired by the signal acquisition module 200 are sent to the control module 100.

[0047] After the control module 100 acquires the brain electrical signals of the to-be-detected object, whether the brain electrical signals are abnormal can be determined by analyzing the brain electrical signals. The abnormal brain electrical signals of the to-be-detected object can be caused by the attack of the to-be-detected object, or can be caused by the daily movement of the to-be-detected object. The specific method for determining whether the brain electrical signals are abnormal according to the brain electrical signals is not limited in the embodiment, as long as the function thereof can be realized.​

[0048] In the case that the brain electrical signal is determined to be abnormal, the control module 100 can determine the abnormal state of the subject according to the symptoms and muscle signals of the subject when the subject has an abnormal attack. In other words, the control module 100 can distinguish whether the abnormal brain electrical signal of the subject is caused by an abnormal attack of the subject or caused by daily movement of the subject according to the type of the abnormality and the muscle signals, so as to accurately determine whether the subject has an abnormal attack.

[0049] In an optional embodiment, the control module 100 can display the received brain electrical signal, and a worker can determine whether the brain electrical signal is abnormal according to actual experience.

[0050] In another optional embodiment, the control module 100 can store a pre-trained neural network model, and the control module 100 can input the received brain electrical signal into the neural network model to obtain an output result of the neural network model. The output result of the neural network model includes that the brain electrical signal is abnormal or the brain electrical signal is normal.

[0051] In another optional embodiment, the control module 100 can store a preset brain electrical wave corresponding to a normal brain electrical signal, and the terminal can match the received brain electrical signal with the preset brain electrical wave. If the matching is successful, it indicates that the brain electrical signal is normal; if the matching fails, it indicates that the brain electrical signal is abnormal.

[0052] The abnormal state of the subject includes that the subject has an abnormal attack and the subject does not have an abnormal attack. Specifically, for example, the abnormal state of the subject is a seizure state, i.e., a seizure attack or a non-seizure attack. In the case that the brain electrical signal is determined to be abnormal, the control module 100 can determine the seizure state of the subject according to the historical symptoms (seizure type) and muscle signals of the subject when the subject has a seizure attack. In other words, the control module 100 can distinguish whether the abnormal brain electrical signal of the subject is caused by a seizure attack of the subject or caused by daily movement of the subject according to the type of the abnormality and the muscle signals, so as to accurately determine whether the subject has a seizure attack.

[0053] The abnormal state detection device provided in the application comprises a control module and a signal acquisition module. The signal acquisition module is used for acquiring brain electrical signals and muscle signals of a to-be-detected object. The control module is used for receiving the brain electrical signals and the muscle signals, and determining an abnormal state of the to-be-detected object according to an abnormal type and the muscle signals in the case that the brain electrical signals are abnormal. In the embodiment, the control module can determine whether the brain electrical signals of the to-be-detected object are abnormal by analyzing the brain electrical signals of the to-be-detected object, and can distinguish whether the brain electrical signals are caused by the abnormality of the to-be-detected object or the daily movement of the to-be-detected object according to the abnormal type and the muscle signals in the case that the brain electrical signals of the to-be-detected object are abnormal, so that the brain electrical signal abnormality caused by the daily movement of the to-be-detected object can be avoided to be mistaken as the brain electrical signal abnormality caused by the abnormality of the to-be-detected object, the abnormality of the to-be-detected object can be avoided to be misjudged, and the accuracy of the abnormal state detection of the to-be-detected object can be improved.

[0054] In one embodiment, the control module 100 is specifically configured to determine that the abnormal state of the to-be-detected object is the non-onset state if the abnormal type is the first type and the fluctuation amplitude of the muscle signal is within the preset range.

[0055] The control module 100 determines that the acquired abnormal type is the first type. The first type includes tetany, clonic convulsion and the like when the abnormal type is epilepsy. The preset range is a fluctuation amplitude threshold value for distinguishing muscle movement and static state, which is set by a worker according to actual experience and stored in a memory of the control module 100. The control module 100 determines that the fluctuation amplitude of the muscle signal is within the preset range by analyzing the muscle signal after acquiring the muscle signal, which indicates that the muscle of the to-be-detected object is relatively static, i.e., the to-be-detected object does not perform daily movement at this time, so that it can be determined that the abnormality (epilepsy) of the to-be-detected object does not occur, i.e., the abnormal state (epilepsy) of the to-be-detected object is the non-onset state.

[0056] In the case that the abnormal type (epilepsy) of the to-be-detected object is the first type, i.e., tetany or clonic convulsion, if the abnormality (epilepsy) of the to-be-detected object occurs, the brain electrical signals of the to-be-detected object are abnormal, and the fluctuation amplitude of the muscle signal exceeds the preset range. In the embodiment, in the case that the type of the to-be-detected object is the first type, the fluctuation amplitude of the muscle signal is determined to be within the preset range, so that the abnormality (epilepsy) of the to-be-detected object can be accurately determined not to occur, and the misjudgment of the abnormality (epilepsy) of the to-be-detected object can be avoided according to the brain electrical signal abnormality, and the accuracy of the abnormal state detection of the to-be-detected object can be improved.

[0057] In one embodiment, the control module 100 is specifically configured to determine that the abnormal state of the to-be-detected object is the non-onset state if the abnormal type is the second type and the fluctuation amplitude of the muscle signal is not within the preset range.

[0058] The control module 100 determines that the acquired abnormality type is a second type. The abnormality type is an epilepsy type, and the second type includes absence, atonia, etc. After acquiring the muscle signal, the control module 100 determines that the fluctuation amplitude of the muscle signal is not within a preset range by analyzing the muscle signal, which indicates that the muscle of the to-be-detected object is relatively active, that is, the to-be-detected object is performing daily motion at this time, and it can be determined that the abnormality (epilepsy) of the to-be-detected object does not occur, that is, the abnormality (epilepsy) state of the to-be-detected object is a non-occurrence state.

[0059] When the abnormality (epilepsy) type of the to-be-detected object is the second type, that is, absence or atonia, if the abnormality (epilepsy) of the to-be-detected object occurs, the electroencephalogram of the to-be-detected object is abnormal, and the fluctuation amplitude of the muscle signal is within the preset range. In this embodiment, when the type of the to-be-detected object is the second type, it can be accurately determined that the abnormality (epilepsy) of the to-be-detected object does not occur by determining that the fluctuation amplitude of the muscle signal is not within the preset range, that is, exceeds the preset range. That is, the abnormality of the electroencephalogram of the to-be-detected object is caused by the daily motion of the to-be-detected object, which can avoid misjudgment of the seizure of the abnormality (epilepsy) of the to-be-detected object according to the abnormality of the electroencephalogram, thereby improving the accuracy of detecting the abnormality (epilepsy) state of the to-be-detected object.

[0060] In one embodiment, as shown in Figure 3 The signal acquisition module 200 includes an electroencephalogram acquisition component 210 and a muscle signal acquisition component 220, both of which are in communication connection with the control module 100. The electroencephalogram acquisition component 210 is used to acquire the electroencephalogram of the to-be-detected object, and the muscle signal acquisition component 220 is used to acquire the muscle signal of the to-be-detected object.

[0061] In use, the electroencephalogram acquisition component 210 can be worn on the head of the to-be-detected object to acquire the electroencephalogram of the to-be-detected object. The muscle signal acquisition component 220 can be worn on the limbs or neck of the to-be-detected object to acquire the muscle signal of the to-be-detected object. The electroencephalogram acquisition component 210 can send the acquired electroencephalogram to the control module 100 in real time, and the muscle signal acquisition component 220 can also send the acquired muscle signal to the control module 100 in real time. This embodiment does not limit the specific structure of the electroencephalogram acquisition component 210 and the muscle signal acquisition component 220 as long as they can realize their functions.

[0062] In an optional embodiment, the electroencephalogram acquisition component 210 is an electroencephalogram acquisition electrode, and the muscle signal acquisition component 220 is a muscle signal acquisition electrode.

[0063] In one embodiment, as shown in Figure 4As shown, the muscle signal acquisition component 220 includes an electromyography signal acquisition unit 221 and an acceleration acquisition unit 222; the electromyography signal acquisition unit 221 is configured to acquire an electromyography signal of the to-be-detected object; and the acceleration acquisition unit 222 is configured to acquire an acceleration signal of the to-be-detected object. The acceleration signal of the to-be-detected object refers to the acceleration of the muscle of the to-be-detected object. The specific description of the electromyography signal and the acceleration signal can refer to the description in the above embodiments, which will not be repeated here. The specific structure of the electromyography signal acquisition unit 221 and the acceleration acquisition unit 222 is not limited in this embodiment, as long as the functions thereof can be realized.

[0064] In an optional embodiment, the electromyography signal acquisition unit 221 can be an electromyography signal acquisition electrode, and the acceleration acquisition unit 222 can be an acceleration sensor.

[0065] In an embodiment, the signal acquisition module 200 is further configured to acquire the electromyography signal and the acceleration signal. Specifically, the electromyography signal acquisition unit worn on the to-be-detected object can acquire the electromyography signal of the to-be-detected object, and the acceleration acquisition unit can acquire the acceleration signal of the to-be-detected object.

[0066] The control module 100 is configured to receive the muscle signal and the acceleration signal, and determine the fluctuation amplitude of the muscle signal according to the electromyography signal and the acceleration signal.

[0067] After acquiring the electromyography signal and the acceleration signal of the to-be-detected object, the control module 100 determines that the electromyography signal represents the activity state of the neuromuscular system of the to-be-detected object, and the acceleration signal represents whether the muscle of the to-be-detected object is in a motion state. By superimposing and analyzing the electromyography signal and the acceleration signal, the control module 100 can determine the fluctuation amplitude of the muscle signal, i.e., whether the to-be-detected object has performed daily exercise.

[0068] In this embodiment, the fluctuation amplitude of the muscle signal is directly determined according to the electromyography signal and the acceleration signal in the acquired muscle signal, and the determination method is simple and easy to implement.

[0069] In an optional embodiment, the control module 100 uses a machine learning method to evaluate the fluctuation amplitude of the muscle signal of the to-be-detected object according to the acquired electromyography signal and acceleration signal of the to-be-detected object.

[0070] In an embodiment, the control module 100 is specifically configured to input the abnormal type and the muscle signal into an abnormal state determination model to obtain the abnormal state of the to-be-detected object.

[0071] The abnormal state determination model is pre-trained and stored in the memory of the control module 100. After obtaining the abnormal type and the muscle signal of the to-be-detected object, the control module 100 inputs the abnormal type and the muscle signal into the pre-trained abnormal state determination model, and obtains the output result of the abnormal state determination model, i.e., the abnormal state of the to-be-detected object.

[0072] In one embodiment, the control module 100 is further configured to train the abnormal state determination model. The specific training process is as shown in Figure 5

[0073] Step 500: obtaining abnormal type samples and muscle signal samples.

[0074] The abnormal type samples include a plurality of abnormal types, and the muscle signal samples include a plurality of muscle signals. The description of the plurality of abnormal types in the abnormal type samples can refer to the description of the abnormal type of the to-be-detected object in the above embodiments, and the description of the plurality of muscle signals in the muscle signal samples can refer to the description of the muscle signal of the to-be-detected object in the above embodiments, which will not be repeated here. The abnormal type samples can include the abnormal type of the to-be-detected object, or can not include the abnormal type of the to-be-detected object. The muscle signal samples can include the muscle signal of the to-be-detected object, or can not include the muscle signal of the to-be-detected object.

[0075] The abnormal type samples and the muscle signal samples can be pre-stored in the memory of the control module 100, and the control module 100 can directly obtain them when needed. The abnormal type samples and the muscle signal samples can also be pre-stored in other storage devices, and can be obtained from the other storage devices when needed by the control module.

[0076] Step 510: inputting the abnormal type samples and the muscle signal samples into the initial abnormal state determination model to obtain the abnormal state determination model.

[0077] The initial abnormal state determination model can be an untrained neural network model. After obtaining the abnormal type samples and the muscle signal samples, the control module 100 inputs the abnormal types in the abnormal type samples and the muscle signals in the muscle signal samples into the initial abnormal state determination model in sequence, trains the initial abnormal state determination model, i.e., modifies the parameters in the initial abnormal state determination model, and obtains the abnormal state determination model.

[0078] In this embodiment, the control module 100 determines the abnormal state of the to-be-detected object by using the trained abnormal state determination model, which can improve the efficiency of determining the abnormal state of the to-be-detected object.

[0079] ​In an optional embodiment, in the process of training the abnormal state determination model, the abnormal state corresponding to the abnormal type in the abnormal type sample and the muscle signal in the muscle signal sample can also be included in the training sample, that is, the process of training the abnormal state determination model is a supervised training process.

[0080] In an embodiment, as shown in Figure 6 The embodiment of the present application provides a method for abnormal state detection by using the abnormal state detection device provided in the above embodiment. The steps of the method include:

[0081] Step 600: obtaining the EEG signal and the muscle signal of the to-be-detected object by using the signal acquisition module.

[0082] The EEG signal and the muscle signal of the to-be-detected object can be obtained by using the signal acquisition module worn on the to-be-detected object. The description of the signal acquisition module, the EEG signal and the muscle signal can refer to the specific description in the above embodiment of the abnormal state detection device, which will not be repeated here.

[0083] Step 610: in the case that the EEG signal is abnormal, determining the abnormal state of the to-be-detected object by using the control module according to the abnormal type of the to-be-detected object and the muscle signal.

[0084] The abnormal EEG signal can be determined by the control module by analyzing the received EEG signal. Specifically, the determination method can include: inputting the received EEG signal into the pre-trained neural network model, and determining whether the EEG signal is abnormal according to the output result of the neural network model. The determination method can also include: matching the received EEG signal with the pre-set EEG wave corresponding to the normal EEG signal stored in the control module, and determining whether the EEG signal is abnormal according to the matching result.

[0085] The method for abnormal state detection provided in the embodiment of the present application is applied to the abnormal state detection device, so the method has all the beneficial effects of the abnormal state detection device, which will not be repeated here.

[0086] In an embodiment, as shown in Figure 7 The embodiment relates to a possible implementation manner of determining the abnormal state of the to-be-detected object by using the control module according to the abnormal type of the to-be-detected object and the muscle signal in the case that the EEG signal is abnormal. The steps of the implementation manner include:

[0087] Step 700: if it is determined by the control module that the abnormal type is the first type and the fluctuation amplitude of the muscle signal is within the preset range, then determining that the abnormal state of the to-be-detected object is the non-occurrence state.

[0088] The description of the preset range can refer to the description in the above embodiments, which will not be repeated here. If the control module obtains the abnormal type of the to-be-detected object as the first type, the abnormal type is the epilepsy type, and the first type includes tetany, clonic convulsion, etc. The control module analyzes the obtained muscle signal to determine whether the fluctuation amplitude of the muscle signal is in the preset range. If the control module determines that the fluctuation amplitude of the muscle signal is in the preset range, it indicates that the muscle of the to-be-detected object is relatively static, that is, the to-be-detected object does not perform daily movement at this time, and it can be determined that the abnormality of the to-be-detected object does not occur, that is, the abnormal state of the to-be-detected object is the non-occurrence state.

[0089] Step 700, if the control module determines that the abnormal type is the second type and the fluctuation amplitude of the muscle signal is not in the preset range, it is determined that the abnormal state of the to-be-detected object is the non-occurrence state.

[0090] If the control module obtains the abnormal type of the to-be-detected object as the second type, the abnormal type is the epilepsy type, and the second type includes catalepsy, atonia, etc. The control module analyzes the obtained muscle signal to determine whether the fluctuation amplitude of the muscle signal is in the preset range. If the control module determines that the fluctuation amplitude of the muscle signal is not in the preset range, it indicates that the muscle of the to-be-detected object is relatively active, that is, the to-be-detected object is performing daily movement at this time, and it can be determined that the abnormality of the to-be-detected object does not occur, that is, the abnormal state of the to-be-detected object is the non-occurrence state.

[0091] In this embodiment, different methods are used to detect the abnormal state of the to-be-detected object for different abnormal types of the to-be-detected object, which has great practicability and reliability.

[0092] It should be understood that, although each step in the flowchart involved in each of the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0093] Each technical feature of the above embodiments can be combined arbitrarily, and in order to make the description concise, not all possible combinations of each technical feature in the above embodiments are described, however, as long as the combination of these technical features does not exist contradictory, it should be considered as the scope of the description.

[0094] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An abnormal state detecting device characterized by comprising: The device comprises a control module and a signal acquisition module, and the signal acquisition module is in communication connection with the control module; The signal acquisition module is configured to acquire electroencephalogram signals and muscle signals of a to-be-detected object; The control module is configured to receive the electroencephalogram signals and the muscle signals, and determine an abnormal state of the to-be-detected object according to an abnormal type of the to-be-detected object and the muscle signals when the electroencephalogram signals are abnormal; the abnormal type of the to-be-detected object refers to symptoms of the to-be-detected object when suffering from epilepsy, and the symptoms include absence, loss of tone, and clonus and clonic spasm; the abnormal state of the to-be-detected object is an epilepsy state, including a seizure or a non-seizure; The control module is specifically configured to determine that the abnormal state of the to-be-detected object is a non-occurrence state if the abnormal type is a first type and a fluctuation amplitude of the muscle signals is within a preset range; the first type includes clonus and clonic spasm. The control module is specifically configured to determine that the abnormal state of the to-be-detected object is a non-occurrence state if the abnormal type is a second type and the fluctuation amplitude of the muscle signals is not within the preset range; the second type includes absence and loss of tone.

2. The device of claim 1, wherein: The signal acquisition module is further configured to acquire electromyogram signals and acceleration signals; The control module is configured to receive the electromyogram signals and the acceleration signals, and determine the fluctuation amplitude of the muscle signals according to the electromyogram signals and the acceleration signals.

3. The device of claim 1, wherein: The control module is further configured to input the abnormal type and the muscle signals into an abnormal state determination model to obtain the abnormal state of the to-be-detected object.

4. The device of claim 3, wherein: The control module is specifically configured to acquire abnormal type samples and muscle signal samples; and input the abnormal type samples and the muscle signal samples into an initial abnormal state determination model to obtain the abnormal state determination model.

5. The apparatus of claim 1, wherein, The signal acquisition module comprises an electroencephalogram signal acquisition assembly and a muscle signal acquisition assembly; The electroencephalogram signal acquisition assembly is configured to acquire the electroencephalogram signals of the to-be-detected object; The muscle signal acquisition assembly is configured to acquire the muscle signals of the to-be-detected object.

6. The apparatus of claim 5, wherein, The muscle signal acquisition assembly comprises an electromyogram signal acquisition unit and an acceleration acquisition unit; The electromyogram signal acquisition unit is configured to acquire electromyogram signals of the to-be-detected object; The acceleration acquisition unit is configured to acquire acceleration signals of the to-be-detected object.

7. A method of detecting an abnormal state by using the abnormal state detecting apparatus according to any one of claims 1 to 6, characterized by, The method comprises: acquiring electroencephalogram signals and muscle signals of a to-be-detected object through a signal acquisition module; In the case that the brain electrical signal is abnormal, the control module determines an abnormal state of the subject to be detected according to the abnormal type of the subject to be detected and the muscle signal; the abnormal type of the subject to be detected refers to symptoms when the subject to be detected has epilepsy, and the symptoms include: loss of consciousness, loss of tension, and tetany, clonic spasm; the abnormal state of the subject to be detected is an epilepsy state, including a seizure or a non-seizure; The method further comprises the following steps: If the control module determines that the abnormal type is a first type, and the fluctuation amplitude of the muscle signal is within a preset range, it is determined that the abnormal state of the subject to be detected is a non-occurrence state; If the control module determines that the abnormal type is a second type, and the fluctuation amplitude of the muscle signal is not within the preset range, it is determined that the abnormal state of the subject to be detected is a non-occurrence state.

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