38 results about "Conscious State" patented technology

The invention relates to a device used for biofeedback training, especially relating to the interactive-reaction multimedia device, which controlled by the physiological signal of trainee and / or the condition of consciousness. The invention comprises a physiological and / or consciousness monitoring module, a signal processing identification module, a control module and a execution module, which detects the physiological signal and / or the condition of consciousness of the trainee, monitors the changes, and calculates the control instruction according to said changes for controlling, regulating and changing the images, voice, motions and other actions of the execution modules in different patterns, so the effect of biofeedback training can be strengthened while it can help the trainee to better adjust the physiologic status and the psychological condition. The invention can be widely used in variable health care and clinical medicine fields.

The invention discloses a coma degree evaluating method based on multiple indexes of non-linearity and complexity, which is used for monitoring the brain state of a patient in a coma, and can realize continuous coma depth monitoring, and coma phase grading and early warning. Multiple complexity indexes such as complexity, Lyapunov exponent, approximate entropy, related dimensions, and the like are extracted from coma electroencephalographic signals by adopting a nonlinear dynamics analytical method, and a comprehensive coma state exponent is obtained by combining a traditional GOS (Glasgow Outcome Scale) evaluation system and a traditional GCS (Glasgow Coma Scale) evaluation system; the coefficients of the correlation between the parameters are obtained by clinical experiments; a coma state grading database is founded and a parameter fusion coefficient is determined on the basis of clinical effects; and finally, complete conscious state grading evaluation exponents are founded to guide the treatment of a patient and make prognosis.

A personal monitoring and emergency communications system including a mobile wrist worn apparatus carried by a monitored person for minimizing emergency response time notwithstanding the monitored person's conscious state and including an array of sensors for periodically sensing vital biometric parameters of the monitored person, a memory for storing and comparing the sensed parameters to a pre-stored standard range of the vital parameters for providing a comparator signal, an alarm circuit for evaluating the comparator signal for providing an emergency alarm signal when the sensed vital parameters are not within the pre-stored standard range, and a programmable logic controller for automatically responding to an emergency including broadcasting the alarm signal from a signal transceiver to a worldwide communication network via a pair of parallel communication links for locating and continuously communicating the monitored person's data directly to the closest medical response team for providing emergency medical services.

A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000 / 17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e., in a deeper depth of anesthesia.

A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000 / 17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e., in a deeper depth of anesthesia.

A system and method for assessing causal signaling in the brain during states of consciousness are described. More specifically, a system and method for determining a directed functional connectivity in the brain wherein neurophysiologic correlates are analyzed with respect to feedback and / or feedforward activities to determine a directional feedback connectivity and / or a directional feedforward connectivity associated with a level of consciousness in the brain.

The invention discloses a conscious state detection method based on a multi-mode brain-computer interface. The conscious state detection method includes the steps that two photos are displayed on a graphical user interface, and a user wanting to select one photo can keep an eye on the corresponding photo and silently count the times of flicking of a photo frame; SSVEP (steady state visual evoked potential) is generated by means of flicking of the corresponding photo, and P300 electric potential is generated by means of flicking of the photo frame; a scalp electroencephalogram signal is acquired via a head-wear electroencephalogram acquiring hat and is recorded by a portable amplifier; the scalp electroencephalogram signal is duplicated in two copies which enter into P300 electric potential detection and SSVEP detection respectively; normal conscious state of the user is assessed and determined according to detection results. The conscious state detection method based on the multi-mode brain-computer interface has the advantages that whether the user can recognize a specified object (a photo of the own face or a photo of another's face) or not is determined by means of detecting P300 and SSVEP of the user, and whether the user is conscious or not can be recognized effectively.

A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000 / 17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e., in a deeper depth of anesthesia.

A method for automatically monitoring consciousness of a person and triggering an alarm if the monitored person is not in a state of full consciousness is provided. The method comprises the steps: monitoring at least one aspect of the behavior of the person (S1); analyzing whether the monitored behavior of the person corresponds to an expected behavior for a state of full consciousness or not (S2, S2′); triggering an alarm if the analysis results in that the detected behavior does not correspond to the expected behavior (S3).

The invention discloses a wearable physiological resonance simulation system, an electric stimulation device and a physiological activity sensing device. The wearable physiological resonance simulation system is installed on the head and / or ears of a user by an ear-wearing structure, and the simulation applied to the user is adjusted by using a brain electric signal as the foundation, so that the effect of influencing the physiological state, the brain state, and / or the conscious state of the user can be achieved.

This invention relates to a control device for a vehicular power transmitting device. The control device has shifting-point altering means 108 operative such that when a second electric motor M2 is operated with priority to obtain a charging efficiency and / or electric power generating efficiency, a shifting point for a vehicle to run under a decelerating state (during a coast running state) is altered to a point on a higher vehicle speed than that at which a shifting point for a running-performance-conscious state is set. Therefore, when the second electric motor M2 is operated with priority for generating electric power, a downshift is initiated at a high vehicle speed than that at which the downshift is initiated at a shifting point for a running-performance-conscious state. This increases a rotation speed NM2 of the second electric motor M2, resulting in an increase in regeneration amount (electric power generation amount) of the second electric motor M2.

The invention discloses a conscious state detection system using an invisible watching BCI (brain computer interface). The system comprises a visual and acoustic stimulation module, an EEG (electroencephalogram) acquisition module and an EEG processing module, wherein the visual and acoustic stimulation module randomly plays visual and acoustic digits with consistent and inconsistent semantics tosubjects; the EEG acquisition module records EEG of the subjects when the subjects perform specified tasks in real time; the EEG processing module outputs online detection results in real time. A visible watching visual and acoustic stimulation form is designed, and conscious state of the subjects is detected by the BCI technology. The accuracy is analyzed online and fed back to the subjects in real time, and the conscious state of the subjects is accurately detected. The conscious state of the subjects is detected by visual and acoustic stimulation with consistent and inconsistent semantics,and the detection accuracy of consciousness of the subjects is improved.

The invention discloses a high-security payment system and a method. The system comprises a payment device, a terminal device and a cloud. The terminal device comprises an acquisition module, a judgment module, a first control module and a first communication module. The acquisition module acquires the current physical state parameters of a user and the preset parameter range of the cloud, wherein the parameter range corresponds to the body state parameters of the user in the non-conscious state. The judgment module judges whether the current physical state parameters of the user are within the preset parameter range or not so as to judge whether the user is in the non-conscious state or not. The first control module generates a corresponding control command according to the judgment result of the judgment module and sends a payment put-off control instruction to the payment device to put off the payment when the user in the non-conscious state. An alarm module sends the corresponding alarm information according to the control command. Therefore, the physical state characteristics of the user are detected and uploaded to the cloud. In combination with a large amount of user information stored in the cloud, a preset parameter range corresponding to the user can be obtained. In this way, the judgment accuracy is improved.

The invention relates to the technical field of intelligent eyeglasses, and particularly to a scene displaying method and the intelligent eyeglasses for an autonomous vehicle. The scene displaying method comprises the steps of detecting whether the current environment with the autonomous vehicle is a tunnel environment by the intelligent eyeglasses; if yes, acquiring a tunnel reality scene which corresponds with the tunnel environment by the intelligent eyeglasses; mixing the tunnel reality scene with a tunnel virtual scene by the intelligent eyeglasses, wherein the tunnel virtual scene is prestored in the eyeglasses, thereby obtaining a tunnel mixed scene; and outputting the tunnel mixed scene by the intelligent glasses to a user which wears the intelligent glasses. According to the scenedisplaying method and the intelligent eyeglasses, when the autonomous vehicle drives into the tunnel, other scenes are displayed to the driver of the autonomous vehicle for keeping a driver in a conscious state, thereby making the driver perform emergency measures when an emergency of the autonomous vehicle occurs, and reducing traffic accident probability.

In one embodiment, prior to undergoing surgery, a patient is trained in the use of an anesthesia-awareness detection (AAD) system to produce an observable output by attempting to move a muscle. The AAD system includes an electromyographic (EMG) signal monitor. While undergoing surgery, the patient is anesthetized with a sedative and a paralytic. During surgery, if the surgical team wishes to asses the consciousness state or communicative ability of the patient, then the surgical team asks the patient to move the muscle. If the patient is unable to speak due to the paralytic but is aware and attempts to comply, then the attempt is detected by the AAD system, which provides a corresponding output to inform the surgical team, which may take appropriate action to ask additional questions of the patient and / or address the patient's anesthesia awareness.

A personal monitoring and emergency communications system including a mobile wrist worn apparatus carried by a monitored person for minimizing emergency response time notwithstanding the monitored person's conscious state and including an array of sensors for periodically sensing vital biometric parameters of the monitored person, a memory for storing and comparing the sensed parameters to a pre-stored standard range of the vital parameters for providing a comparator signal, an alarm circuit for evaluating the comparator signal for providing an emergency alarm signal when the sensed vital parameters are not within the pre-stored standard range, and a programmable logic controller for automatically responding to an emergency including broadcasting the alarm signal from a signal transceiver to a worldwide communication network via either of a pair of parallel communication links for locating and continuously communicating the monitored person's data directly to the closest medical response team for providing emergency medical services.

A rehabilitation system for neurological disorders, especially for the rehabilitation of patients being in a vegetative state or minimal conscious state, comprises at least two sensors adapted to measure two different physiological values of the patient and a stimulation generator. Initially predefined target signals determined and based on parameters relating to the injury are generated and, during a rehabilitation session, compared with the measurement signals to drive the stimulation generators delivering sensory stimulation to the patient as feedback. The controlling processor is adapted to change the predefined target signals during a rehabilitation session based on the development or changes of the measurement signals of the different physiological values, to enable a faster and better improvement of the alertness of the patient.

The invention discloses a micro-expression recognition-based consciousness assessment method and system. The method comprises the following steps of: configuring a voice stimulation material; the voice stimulation materials are played to the multiple subjects respectively; collecting facial expression image data of each subject when the subject receives the voice stimulation; extracting feature data of each subject from the facial expression image data; training an SVM classifier through the feature data of the plurality of subjects and the corresponding user categories; and obtaining feature data of a user with an unknown category, and inputting the feature data into the trained SVM classifier to obtain a user category corresponding to the user. According to the consciousness assessment method and system based on micro-expression recognition, the consciousness level of a subject can be reflected more directly, the accuracy and convenience of consciousness state judgment are improved, patients who cannot perform magnetic resonance and electroencephalogram can be assessed more accurately, and universality is improved.

The invention discloses a traditional Chinese medicine for treating acute phase cerebral infarction and a preparation method thereof. The traditional Chinese medicine for treating acute phase cerebral infarction is characterized by comprising the following components in parts by weight: 10-40 parts of rhodiola rosea, 10-40 parts of raw rhubarb, 10-40 parts of rhizoma alismatis, 10-40 parts of leonurus heterophyllus and 10-40 parts of acorus gramineus. The invention further provides a preparation method of the traditional Chinese medicine. The medicine disclosed by the invention is applied based on the conventional treatment method, so that the conscious state of the patient can be effectively improved, the relevant neurologic impairment can be effectively improved, and a foundation is laid for the patient to get well soon and leave the hospital.

The invention relates to the field of brain-computer interfaces, in particular to a somatosensory stimulation consciousness detection method based on electroencephalogram double-feature fusion, which is used for comprehensively evaluating the consciousness state of a user by expanding a coding mode of a BCI through multi-feature fusion and performing intensified induction on brain consciousness-related neurological function response. A tactile stimulation normal form for inducing a corresponding electroencephalogram signal is designed, the electroencephalogram signal of a user is acquired by using an electroencephalogram acquisition system product, feature extraction is performed on corresponding P300 and SSSEP signals after downsampling and filtering processing, the extracted features are fused, and finally classification and recognition are performed. The method is mainly applied to medical instrument design and manufacturing occasions.

A rehabilitation system for neurological disorders, especially for the rehabilitation of patients being in a vegetative state or minimal conscious state, comprises at least two sensors adapted to measure two different physiological values of the patient and a stimulation generator. Initially predefined target signals determined and based on parameters relating to the injury are generated and, during a rehabilitation session, compared with the measurement signals to drive the stimulation generators delivering sensory stimulation to the patient as feedback. The controlling processor is adapted to change the predefined target signals during a rehabilitation session based on the development or changes of the measurement signals of the different physiological values, to enable a faster and better improvement of the alertness of the patient.

The invention belongs to the technical field of anesthesia, and discloses a portable anesthesia and operation integrated system which comprises a physiological data acquisition module, a main control module, an anesthesia module, an anesthesia control module, an anesthetic dosage calculation module, an operation module, a lighting module and a display module. The anesthesia control module collects electroencephalogram analog signals in the anesthesia process of a patient in real time, the electroencephalogram analog signals are converted to extract a physiological feature value system, and judgment is conducted according to the physiological feature value system through the machine model; the working mode of the anaesthesia machine is regulated and controlled according to the judgment result, automation of the process of judging and regulating the anaesthesia machine is achieved, and on the basis that the accuracy of judging the consciousness state and the breathing state of the patient is improved, the real-time performance of regulation and control is guaranteed; the dosage of gaseous anesthetic in the whole anesthesia process can be accurately calculated through the anesthetic dosage calculation module, errors caused by manual calculation are avoided, and the accuracy and precision of a liquid anesthetic dosage calculation result are effectively improved.

The invention discloses a visuoauditory brain-computer interface-based consciousness state detecting method. The method includes the following steps of: 1) interface design; 2) signal acquisition; 3) detection; 4) data processing; and 5) result statistics and evaluation. According to the method of the invention, a visuoauditory brain-computer interface is adopted, so that the possibility of the detection of reserved cognitive function and potential consciousness can be improved. Two digital keys blink in a random order, so that visual related ERP potentials are generated, at the same time, two pieces of digital sound simultaneously appear, so that auditory related ERP potentials are generated; visual and auditory stimulus which appears simultaneously will produce a visuoauditory integration effect; Compared with a visual brain-computer interface and an auditory brain-computer interface, the method can improve the precision rate of a system and increase ERP response waveforms, and therefore, the validity and accuracy of the system can be improved, and services can be provided for consciousness state detection clinic and research.

The invention relates to an electroencephalogram data type annotation method and system in a conscious state, wherein the method comprises the following steps that after a user starts a sleeping process, an electroencephalogram sample of the user is collected; the electroencephalogram sample is subjected to wavelet decomposition; the signal rebuilding is performed according to the wavelet coefficient of the set low frequency band to obtain an electroencephalogram; the sample entropy of the electroencephalogram is calculated, and is compared with a pre-calculated sample entropy threshold vale; if the sample entropy is greater than the sample entropy threshold value, the signal type of the electroencephalogram sample is annotated into the conscious state. By using the technical scheme, the method and the system have the advantages that the electroencephalogram is prevented from being interfered; the conscious state of the electroencephalogram is accurately detected; in addition, the effective data type annotation is performed, so that the recognition accuracy of a personnel classifier trained by using the annotated electroencephalogram sample is higher; the reliability of the detection result of the personnel sleeping stage in the later period is also improved.

The invention discloses a conscious state detection method based on a multi-mode brain-computer interface. The conscious state detection method includes the steps that two photos are displayed on a graphical user interface, and a user wanting to select one photo can keep an eye on the corresponding photo and silently count the times of flicking of a photo frame; SSVEP (steady state visual evoked potential) is generated by means of flicking of the corresponding photo, and P300 electric potential is generated by means of flicking of the photo frame; a scalp electroencephalogram signal is acquired via a head-wear electroencephalogram acquiring hat and is recorded by a portable amplifier; the scalp electroencephalogram signal is duplicated in two copies which enter into P300 electric potential detection and SSVEP detection respectively; normal conscious state of the user is assessed and determined according to detection results. The conscious state detection method based on the multi-mode brain-computer interface has the advantages that whether the user can recognize a specified object (a photo of the own face or a photo of another's face) or not is determined by means of detecting P300 and SSVEP of the user, and whether the user is conscious or not can be recognized effectively.

A consciousness state determination system comprises a first image-taking section which is mounted on a vehicle and takes images of a driver of the vehicle, an electronic control unit which executes a consciousness state determination process of determining whether the driver is conscious, based on a first image corresponding to the image of the driver taken by the first image-taking section, and a mobile terminal carried by the driver. The electronic control unit is configured to determine whether the driver is conscious, based on terminal-side information corresponding to information acquired by the mobile terminal when a predetermined condition that a reliability level of a determination result obtained from the consciousness state determination process is presumably low, becomes satisfied.

The invention relates to an electroencephalogram data type annotation method and system in a conscious state, wherein the method comprises the following steps that after a user starts a sleeping process, an electroencephalogram sample of the user is collected; the electroencephalogram sample is subjected to wavelet decomposition; the signal rebuilding is performed according to the wavelet coefficient of the set low frequency band to obtain an electroencephalogram; the sample entropy of the electroencephalogram is calculated, and is compared with a pre-calculated sample entropy threshold vale; if the sample entropy is greater than the sample entropy threshold value, the signal type of the electroencephalogram sample is annotated into the conscious state. By using the technical scheme, the method and the system have the advantages that the electroencephalogram is prevented from being interfered; the conscious state of the electroencephalogram is accurately detected; in addition, the effective data type annotation is performed, so that the recognition accuracy of a personnel classifier trained by using the annotated electroencephalogram sample is higher; the reliability of the detection result of the personnel sleeping stage in the later period is also improved.

The invention discloses an information-epidemic disease co-evolution analysis method under the action of dynamic multi-source information and behavior response. The method comprises the following steps: firstly, constructing a multi-layer sequential network model with coupled node parts; then constructing an unconsciousness-consciousness-unconsciousness, susceptibility-infection-susceptibility model analysis information-epidemic disease propagation dynamics process; then describing conversion between an epidemic disease state and an information state of the node, and deriving an epidemic disease threshold value betac; then simulating a dynamic evolution process of epidemic propagation and information diffusion; and finally, counting the proportion of the infected state nodes and the conscious state nodes in the network in a steady state, and completing an information-epidemic collaborative evolution process. According to the method, the influence of individual time-varying self-consciousness and behavior response on information-epidemic disease co-evolution is considered in a multi-layer sequential network with partially coupled nodes, state conversion of the nodes is described through a micro Markov chain method, and a critical threshold value beta c of the epidemic disease is theoretically solved; and guidance is provided for intervention and control of COVID-19 or other sudden epidemic diseases possibly outbreak in the future.

A device for waking up a crew member resting on an aircraft includes a processing unit configured to detect current development parameters of the aircraft and / or to detect a state of consciousness of at least one other active crewmember. The processing unit is also configured to activate an apparatus for waking up the resting crew member, when at least one predetermined condition for waking up the resting crew member on one or several current parameters of the aircraft and / or on a parameter representative of the state of consciousness of the at least one other active crew member is satisfied.

The present invention relates to the field of medical imaging technology, and specifically provides a device for predicting the state of consciousness of the human brain, aiming to solve the technical problem of how to accurately predict the state of consciousness of the human brain. To this end, the human brain consciousness state prediction device in the present invention includes a consciousness state prediction model, which can predict the consciousness state of the human brain based on the imaging characteristics of the human brain and corresponding clinical information. Specifically, the imaging feature calculation module in the state of consciousness prediction model can calculate imaging features that represent the patient's brain connection status, the imaging feature screening module can screen imaging features that are highly relevant to prognosis, and the imaging feature processing module can Imaging features and clinical information are combined into new feature vectors, and the model training module can train the partial least squares regression model based on the feature vectors to obtain the final prediction model. The prediction device in the present invention takes into account both clinical information and image detection information, thereby being able to comprehensively and effectively predict the state of brain consciousness.