613 results about "Peripheral oxygen saturation" patented technology

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention relates to the technical field of medical diagnosis, and discloses a blood oxygen monitoring system fused with sleep rhythm modeling, which comprises the following steps: identifying a sleep stage conversion moment through a photoelectric volume pulse wave signal, detecting a blood oxygen saturation valley value in a window after conversion, and calculating response lag duration; according to the method, the physiological rhythm is converted into a natural probe for the compensatory ability of the respiratory system, dual diagnosis of sleep staging and respiratory function is realized through a single-path signal, and meanwhile, body movement interference is converted into an observation window for the respiratory stress recovery ability. The diagnosis reliability in a home monitoring scene is remarkably improved, the system completes core analysis at an edge node, and a respiratory function evaluation report which can be directly used for clinical decision making is output.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention discloses an oxygenerator remote control system and method based on the Internet of Things, and belongs to the technical field of intelligent medical equipment and remote health management, and the method comprises the steps: S1, collecting multi-dimensional physiological data of a user in real time through a multi-source physiological signal collection module, a heart rate variability index and a body movement signal; s2, based on the multi-dimensional physiological data, processing is performed through a preset physiological state prediction model, and a unified physiological stress index is solved; s3, determining a predictive oxygen supply flow rate value in combination with the physiological stress index and a preset user basic oxygen supply flow rate; s4, according to the predictive oxygen supply flow velocity value, a control signal for a physical execution component of the oxygen generator is generated so as to dynamically adjust oxygen supply parameters, and conversion from passive compensation to active prediction is achieved.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention discloses an adjustable blood transfusion reaction monitoring system. The system comprises a multi-mode physiological signal acquisition module, a data processing and analysis module, a user interaction and parameter configuration interface, a wireless communication module, an alarm and feedback module and a power management module. By integrating an electrocardiogram (ECG) sensor, an oxyhemoglobin saturation (SpO2) detection probe, a non-invasive blood pressure measuring device, a body temperature sensor, a respiratory rate monitor and a skin impedance change sensor, real-time acquisition of various physical parameters is realized; a rule-based threshold judgment model and a time sequence prediction model are built in the data processing and analysis module, and the data processing and analysis module is used for identifying potential blood transfusion reaction risks; and the user interaction and parameter configuration interface provides a touch display screen and remote terminal equipment to realize information input, data display and operation recording. Continuous dynamic monitoring, intelligent early warning and graded response in the blood transfusion process are achieved, blood transfusion safety and nursing efficiency are improved, and the system has wide clinical application prospects.

The invention relates to the technical field of old-age care monitoring, and discloses an intelligent old-age care monitoring method and system based on wearable equipment. According to the method, original physiological data such as heart rate signals, oxyhemoglobin saturation and body movement frequency are collected through a plurality of wearable devices, multi-modal feature extraction is conducted on the original physiological data, and a physiological feature vector set is generated. A to-be-analyzed health event sequence containing a periodic abnormal mode and a non-periodic burst mode is screened out, and a health knowledge base is called to retrieve a matched candidate health event entry set. And inputting the two health events into a health event aggregator for cross-modal semantic fusion analysis, generating a health event decision coding matrix, obtaining an intervention measure instruction set for each health event to be analyzed through hierarchical decoding, and finally generating a response operation queue executable by the pension monitoring terminal. According to the method, the health condition of the old people can be comprehensively and accurately monitored, intervention measures can be timely and effectively provided, and the quality and efficiency of old-age care monitoring are greatly improved.

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The invention relates to the technical field of image processing and biological signal detection, discloses a facial physiological detection method and system based on signal quality driven ROI selection, and aims to solve the problem of signal degradation of a traditional fixed geometric ROI in a complex scene. The method comprises the following steps: collecting a user face video stream through a camera and preprocessing the user face video stream; detecting a face bounding box and dividing the face bounding box into a plurality of sub-regions; calculating the signal-to-noise ratio, the periodic intensity and the motion artifact interference degree of each sub-region; screening an optimal sub-region according to a weighted fusion formula to generate a dynamic ROI mask; extracting a pure rPPG signal from the dynamic ROI mask coverage area; detrending and band-pass filtering are carried out on the rPPG signals, and physiological parameters such as the heart rate and the blood oxygen saturation degree are extracted. The system comprises a face video acquisition module, a face region positioning and segmentation module, a signal quality evaluation module, a dynamic ROI selection module, an rPPG signal extraction module and a physiological parameter estimation module. According to the technical scheme, signal degradation caused by local shielding, illumination abrupt change or attitude offset can be effectively avoided, the signal-to-noise ratio and the stability of the rPPG signal are remarkably improved, and the universality and the robustness of the method are enhanced.

The embodiment of the invention provides a continuous blood oxygen monitoring system in extracorporeal circulation and a control method thereof. The continuous blood oxygen monitoring system comprises a vein sensor, an artery sensor, a blood flow sensor, an oxygen sensor, a carbon dioxide sensor, a controller and a display, the vein sensor monitors the vein blood oxygen saturation degree; the artery sensor monitors arterial oxygen saturation and hematocrit; the blood flow sensor monitors the blood flow of the artery pipeline; the oxygen sensor monitors the oxygen concentration of the oxygenator; the carbon dioxide sensor monitors the carbon dioxide concentration of the oxygenator; the display provides an interaction control, responds to a trigger operation acting on the interaction control, sends an interaction instruction to the controller, and updates information displayed in an interaction interface according to the control of the controller; the controller receives the interaction instruction and executes processing corresponding to the interaction instruction; the processing includes controlling display information of the display; the display information comprises information corresponding to the monitoring parameters. The scheme ensures the safety and effectiveness of the treatment process.

The invention belongs to the technical field of brain function monitoring, and discloses a three-in-one non-invasive brain function monitoring system and method, a medium, equipment and application, and the monitoring method comprises the following steps: monitoring brain oxygen saturation of a critical patient; carrying out transcranial Doppler ultrasonic measurement on intracranial blood vessel blood flow velocity and ultrasonic measurement on the inner diameter of the optic nerve sheath; monitoring a quantitative electroencephalogram of the patient; various brain function monitoring series indexes and continuous changes of the indexes along with disease evolution are obtained, the pathogenic mechanism and pathogenesis of the indexes are found in time, and primary diseases and related complications of the primary diseases are treated. According to the invention, the brain oxygen saturation can avoid too low brain oxygen or low brain oxygen for a long time and too high brain oxygen due to oxygen utilization disorder; cerebral blood flow can be monitored through a transcranial Doppler (TCD) technology to find an optimal craniocerebral perfusion pressure (CPP), and a cerebral blood flow self-adjusting function is enabled to be in an optimal state; the electroencephalogram can avoid early warning of over-sedation or abnormal discharge and the like. Through organic combination of the three, noninvasive, real-time and dynamic monitoring of critical patients is realized.

A non-contact, non-invasive health monitoring device and method utilizing advanced artificial intelligence (AI) and machine learning techniques. This system captures real-time image data of a user's face using a high-resolution camera and processes the data to extract physiological signals, including Photoplethysmography (PPG, iPPG, and rPPG) and Ballistocardiography (BCG and iBCG). By leveraging facial landmark detection and deep learning models such as Convolutional Neural Networks (CNNs) and Transformers, the device predicts vital signs such as heart rate, respiratory rate, blood pressure, and oxygen saturation, alongside wellness metrics like stress levels and metabolic health. The device employs robust feature construction and signal processing modules to ensure accurate metrics under varying conditions, with error margins below 5%. Outputs are displayed in real time and integrated with external systems using standardized healthcare protocols. Applications include telehealth, fitness monitoring, public health screening, and automotive safety systems.

The invention discloses a non-inductive physical and psychological health monitoring and early warning system and method before a coal miner goes down a well, and belongs to the technical field of coal mine safety production and personnel health monitoring. A physiological monitoring unit comprises a millimeter-wave radar, an infrared camera and an imaging type photoplethysmography device, the millimeter-wave radar is installed in the center of the top of a monitoring channel, and the infrared camera is installed on the imaging type photoplethysmography device; the infrared cameras are symmetrically installed on the two sides of the channel, the imaging type photoplethysmography devices are integrated in the infrared cameras and used for synchronously collecting the core body temperature and the oxyhemoglobin saturation, and the integrated imaging type photoplethysmography devices focus on the forehead and cheek areas through the RGB channel. Reflected light intensity fluctuation signals caused by blood vessel blood volume changes are collected. Through cooperation of the three devices, non-contact synchronous collection of multiple parameters can be completed without deliberate cooperation of workers, comprehensive and continuous basic data are provided for subsequent physical and psychological health analysis, the wellhead passing efficiency is improved, and the cross infection risk of contact type devices is avoided.

The invention discloses personal life support equipment in a high-altitude environment and a cooperative control method thereof, and relates to the technical field of personal life support equipment, and the equipment comprises a respiration sensor which is arranged on a mask and is used for detecting respiration frequency and phase; the blood oxygen and heart rate sensor is worn on the wrist of the user and used for detecting the blood oxygen saturation degree and the heart rate; the body temperature sensor is attached to the chest of the user and used for detecting the core body temperature; the automatic oxygen supply device is used for providing oxygen; the intelligent heating device comprises partition heating pieces attached to the chest and the four limbs of the user and is used for partition heating; the microcontroller unit is used for formulating an optimal life support strategy according to the sensor data and intelligently and cooperatively controlling the working states of the automatic oxygen supply device and the automatic heating device according to the optimal life support strategy; the sensor data comprises respiratory frequency and phase, oxyhemoglobin saturation, heart rate and core body temperature, and dynamic oxygen supply and body temperature adjustment can be cooperatively controlled.

A portable, multimodal dental diagnostic device for assessing the vitality of the dental pulp, consisting of: a detachable diagnostic component comprising a variety of probes, including the following: a heat probe configured to deliver controlled heat stimuli to a tooth surface and measure the heat response of the dental pulp; an electrical probe configured to deliver low-voltage electrical pulses to the tooth surface and measure the electrical impedance of the dental pulp; and an optical probe configured to assess blood oxygen saturation and microcirculation in the dental pulp using near-infrared light; a microcontroller unit configured to process sensor data from the thermal probe, the electrical probe and the optical probe, to calibrate measurements based on the oral base temperature and the ambient conditions, to aggregate multimodal sensor data and to control the operation of the thermal probe, the electrical probe and the optical probe; a cloud platform configured to receive and store aggregated sensor data, run artificial intelligence algorithms to analyze multimodal sensor data and generate diagnostic insights that include vital signs and diagnostic probabilities; a user application that is communicatively connected to the cloud platform and configured to wirelessly receive aggregated sensor data, display detailed diagnostic reports with vital signs and trends to the user, integrate with electronic health data systems, and issue alerts in case of abnormal readings; a wireless communication module connected to the microcontroller unit, configured to transmit the aggregated sensor data to the cloud platform; a display interface configured to show real-time feedback from the probes, including vitality assessments and sensor readings; and a main body containing the microcontroller unit, a rechargeable power supply and the display interface, wherein the rechargeable power supply can be charged via the current provided through a charging port on the main body, and wherein the main body is also equipped with on / off and start / stop switches; the device is configured to provide a non-invasive, quantitative real-time analysis of dental pulp health by combining physiological responses from thermal, electrical, and optical measurements.

The invention provides a life support stretcher. The life support stretcher is internally provided with a controller and an air bag pressurization binding belt used for binding the wounded, wherein a vital sign data detection unit is arranged in the air bag pressurization binding belt; the life promotion data specifically comprises heart rate, breathing parameters, blood pressure, body temperature and oxyhemoglobin saturation; the controller is used for controlling the air pressure in the air bag pressurization binding belt according to the vital sign data, detected by the vital sign data detection unit, of the wounded, and dynamically adjusting the binding tightness degree through a multi-modal data fusion algorithm and wound part recognition. Thus, when the life support stretcher is used for transferring a wounded person, the air bag pressurization binding belt can be used for binding the wounded person, the vital sign data of the wounded person can be detected through the vital sign data detection unit in the air bag pressurization binding belt, and then the controller can control the wounded person according to the detected vital sign data. And the air pressure in the air bag pressurizing binding belt is controlled, so that the trauma to the wounded caused by bumping is reduced.

The invention discloses an oxygen therapy instrument control system and method and a storage medium, and the system comprises an oxygen saturation obtaining module which is used for obtaining the current blood oxygen saturation value of a user; the air and oxygen flow control module is used for controlling the mixed air and oxygen flow; the oxygen concentration control module is used for controlling the concentration of oxygen in the air-oxygen gas; and the analysis control module is used for inputting the initial air-oxygen flow and the oxygen concentration, and carrying out stepped control adjustment on the air-oxygen flow and the oxygen concentration according to the current blood oxygen saturation value of the user, so that the equipment parameters of the oxygen therapy instrument gradually approach to the body characteristics of the user. Gradient adjustment and real-time calculation can be carried out, adjustment is accurate, intelligent regulation and control are achieved, it is guaranteed that the oxygen therapy instrument is suitable for the physical condition of a user in real time, real-time manual adjustment is not needed, and discomfort of the user or oxygen waste caused by inapplicable parameters is also avoided.

The invention relates to a health index monitoring tooth socket and a monitoring method thereof, and the method comprises the steps: collecting an optical signal in an oral cavity in real time through a collection module, and extracting a DC component and an AC component from the optical signal; and evaluating the signal quality of the direct-current component and the alternating-current component according to the standard signal proportion, and obtaining a target direct-current component and a target alternating-current component which meet the standard signal quality. On the basis of the cross correlation between the target direct-current component and the target alternating-current component, the blood oxygen saturation health index is obtained through a piecewise function. Based on the target alternating component, the heart rate health index is obtained through peak value detection. The problem of low monitoring accuracy of a monitoring method for monitoring health indexes in related technologies is solved.

The invention discloses an emergency nursing system capable of dynamically adjusting priority, which belongs to the technical field of emergency nursing and comprises the following steps: acquiring heart rate detection data, blood pressure detection data and oxyhemoglobin saturation detection data of a patient at preset time intervals, and filtering; setting a sliding time window, and respectively calculating a heart rate change rate, a blood pressure change rate and an oxyhemoglobin saturation change rate for the filtered heart rate detection data, the filtered blood pressure detection data and the filtered oxyhemoglobin saturation detection data in the sliding time window; correlation analysis is conducted on the heart rate change rate, the blood pressure change rate and the blood oxygen saturation degree change rate, parameter correlation characteristics are obtained, and a comprehensive parameter deterioration index is calculated; the comprehensive parameter deterioration index is compared with a preset threshold value, and whether a priority improvement mechanism is triggered or not is judged; and executing the priority lifting mechanism. By establishing a dynamic monitoring mechanism based on the change rate of the physiological parameters, real-time capture and quantitative analysis of the change trend of the illness state of the patient are realized.

An apparatus for evaluating cerebral autoregulation includes a data acquisition unit configured to acquire blood pressure data and oxygen saturation data of a patient undergoing surgery, a correlation coefficient calculation unit configured to calculate correlation coefficients between the acquired blood pressure data and the acquired oxygen saturation data, a filtering unit configured to filter the calculated correlation coefficients using a moving average filter having a predetermined time window, and a cerebral autoregulation evaluation unit configured to evaluate the cerebral autoregulation of the patient undergoing surgery based on the filtered correlation coefficient.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention belongs to the field of artificial intelligence and contact type measurement, and discloses a blood oxygen saturation degree measuring method based on a smart phone camera. The method comprises the following steps: acquiring fingertip video data by using a smart phone under the condition of no external equipment, extracting a pulse photoplethysmography (PPG) signal from the fingertip video data, and constructing a multi-dimensional feature set through feature extraction and calculation; furthermore, the invention provides a two-channel deep learning model fusing a convolutional neural network and a long-short term memory network, the multi-dimensional feature set and the preprocessed PPG signal are processed in parallel, the advantages of the two networks in the aspects of feature extraction and time sequence modeling are fully exerted, and the accuracy of feature extraction and time sequence modeling is improved. And the sufficiency of the extracted features and the generalization ability of the model are ensured, so that the accurate estimation of the oxyhemoglobin saturation is realized. The research provides a feasible scheme for health self-examination in a family environment.

The invention provides a thoracic surgery respiratory training system and method, and the method comprises the steps: constructing a respiratory-physiological feature tensor through the time sequence change of a respiratory airflow parameter during respiratory training in combination with the fluctuation feature of blood oxygen saturation in the respiratory training process, and carrying out the space-time convolution fusion of the respiratory-physiological feature tensor and a chest respiratory movement track, obtaining a respiratory efficiency index; constructing a taboo risk vector based on the risk relationship map of the thoracic surgery postoperative complications and the electronic medical record of the target user, and generating a complication risk entropy of the target user under the current respiratory training according to the taboo risk vector in combination with the pain tolerance physiological data; and according to the respiratory efficiency index and the complication risk entropy, determining a dynamic adaptation margin of the target user to current respiratory training, and based on the dynamic adaptation margin, dynamically adjusting the training resistance of the self-adaptive resistance adjustment respiratory training device. By the adoption of the scheme, a multi-dimensional respiratory training feedback mechanism can be constructed to conduct self-adaptive adjustment on the training resistance.

The invention relates to the technical field of breathing machine off-line evaluation, in particular to a breathing machine intelligent off-line evaluation system based on multiple parameters, which is characterized in that a data acquisition unit acquires breathing and physiological parameters of a patient in real time; the data analysis unit extracts a respiratory rate instantaneous value and a blood oxygen saturation continuous measurement value through preprocessing, inputs the respiratory rate instantaneous value and the blood oxygen saturation continuous measurement value into an intelligent analysis model composed of a deep residual network and a time convolution network, fuses a physiological hysteresis compensation coefficient to generate an offline index, and outputs a preliminary offline risk index; the data processing unit is embedded into a clinical rule base through a decision tree algorithm, generates a risk correction factor through a taboo check factor, allocates differentiation weights according to severity levels, corrects initial risk deviation and generates a comprehensive offline risk index, and the clinical rule base is continuously optimized through online learning, so that the offline risk assessment accuracy is improved; and a reliable basis is provided for clinical decision-making.

The invention discloses a chest image diagnosis method and system based on multi-modal sign collection, and relates to the technical field of medical image.The method comprises the steps that a chest image of a patient is obtained, electrocardiosignals and blood oxygen saturation data are synchronously collected, and an associated radiology report is obtained; the image lesion features and the frequency domain rhythm template are combined for processing, motion artifacts are eliminated through a frequency domain decoupling equation, and refined image features are output; inputting the refined image features and the text pathological semantic features into a bidirectional attention mechanism to generate fusion features, and splicing the oxyhemoglobin saturation data and the text pathological semantic features into a sign-text vector; and inputting the fusion feature and the sign-text joint vector into a multi-task loss function, and outputting a structured diagnosis report. According to the method, accurate elimination of motion artifacts is achieved through a frequency domain decoupling equation, and coupling calculation is conducted on an electrocardio rhythm template and image lesion features in a frequency domain space.

An embodiment of a spectrophotometric device for measuring blood oxygen saturation includes a light source, a detector for detecting a light signal reflected by a subject's tissue, an electronic data processing unit to calculate blood oxygen saturation based on the light signal, and a motion sensor for detecting a motion signal indicative of motion of the light source and / or the light detector. The electronic data processing unit may discard a light signal measured by the detector if the motion signal detected by the motion sensor exceeds a predetermined threshold value. An embodiment of a method for measuring blood oxygen saturation includes emitting a light signal into a subject's tissue, detecting the light signal reflected by the tissue, calculating blood oxygen saturation using the light signal measured by the detector, and detecting a contact signal indicative of contact between a subject's skin and the light source and / or the detector.

The invention relates to the technical field of medical signal processing, in particular to a brain oxygen entropy-based dynamic blood flow simulation system which comprises a data acquisition module, a structure modeling module, a state monitoring module, a simulation calculation module and an anomaly prediction module. According to the method, through synchronous utilization of brain oxygen saturation and blood flow velocity signals, accurate capture of brain oxygen supply and blood flow dynamic dynamic correlation is achieved, sensitivity of abnormal fluctuation recognition is enhanced, individualization and accuracy of blood flow simulation are improved by combining a brain blood vessel geometric structure and blood vessel elasticity and compliance parameters, and multi-point dynamic coupling is utilized to achieve the purpose of improving the accuracy of blood flow simulation. According to the method, abnormal blood supply state recognition is enhanced, brain blood supply stability evaluation is optimized, the local blood flow micro-dynamic feature reduction degree is improved through dynamic updating of the inertia coefficient and the shear stress, the oxygen entropy fluctuation, the shear stress and the flow velocity gradient are combined, the nonlinear coupling relation is extracted, the abnormal event prediction accuracy is improved, and the brain blood supply stability is improved. And the early warning capability of abnormal cerebral blood supply is enhanced.