94 results about "RESPIRATORY RHYTHMS" patented technology

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention provides a clinical diagnosis and treatment path generation method and system for tracking a chest pain patient. Wherein a dynamic spatio-temporal data atlas is constructed by synchronously collecting respiratory rhythm phase difference, coronary artery calcification integral and hypersensitive troponin curve slope, and risk degree transition signals are generated by coupling analysis of ST segment elevation amplitude asymmetric growth, left ventricular ejection fraction attenuation gradient and B-type natriuretic peptide release rate. In the acute stage, the constraint relation between a vascular recanalization time window and thrombolysis taboo is dynamically bound, in the subacute stage, a phase synchronization rule for enhancing CT scanning and anticoagulant drug dosage is established through a multidisciplinary interface, intravascular ultrasonic plaque morphological mutation characteristics and extracorporeal circulation parameters are fused, the blood supply reconstruction time sequence is dynamically adjusted based on a myocardial oxygen consumption balance equation, and the thrombolysis time sequence is determined. And path self-optimization is realized through space-time superposition of an interventional therapy delay parameter and a drug metabolism curve. According to the technical scheme provided by the invention, chest pain diagnosis and treatment path dynamic optimization and multi-modal parameter collaborative decision-making are realized, and the risk layering precision and the treatment timeliness are improved.

The invention belongs to the technical field of intelligent rehabilitation equipment control, and relates to a multi-parameter feedback regulation and control method for postoperative lung function rehabilitation training, which comprises the following steps: acquiring an electrocardiosignal, a respiration signal and a limb movement signal of a patient; identifying a heart rate fluctuation form of the electrocardiosignal to generate an abnormal fluctuation time period, and analyzing a time difference between an inspiration phase and an expiration phase of the respiratory signal to generate a starting point of respiratory rhythm disorder; judging a cardiopulmonary risk level; the exercise intensity of rehabilitation training equipment is automatically adjusted based on the cardiopulmonary risk level, and meanwhile a regulation and control mode is dynamically selected according to the proportion change of the chest fluctuation amplitude and the abdomen fluctuation amplitude; when the heart rate recovery rate is lower than a preset reference and the respiratory rhythm stabilization time exceeds a critical value, gradually improving the exercise intensity; calculating a motion intensity correction coefficient and triggering a motion posture correction instruction; and generating a rating report containing the cardiopulmonary function fitness. According to the invention, the problem of lack of comprehensive quantitative evaluation of rehabilitation efficiency in the prior art is solved.

The invention provides a meditation effect intelligent scoring method and system based on big data analysis, and relates to the technical field of data processing.The method comprises the steps that 1, brain wave signals, heart rate variability, skin conductance original waveforms and respiratory rhythm signals of a user during meditation are collected in real time through a wearable device, and the brain wave signals, the heart rate variability, the skin conductance original waveforms and the respiratory rhythm signals are sent to the wearable device; environment noise decibel values, illumination intensity and environment temperature parameters are obtained through an environment sensor, and a multi-dimensional original data set fusing the physiological signals and the environment parameters is generated; 2, according to the resting state physiological parameters in the multi-dimensional original data set, Gaussian mixture probability distribution is adopted, personalized reference features are generated, and the personalized reference features comprise mean vectors and covariance matrixes of brain wave signals, heart rate variability, skin conductance and respiratory rhythm parameters; according to the invention, through multi-source data acquisition and fusion, personalized reference construction, multi-dimensional physiological and environmental index analysis and dynamic score adjustment, quantitative evaluation of a meditation effect, which is comprehensive, accurate and real-time and fits individuals, is realized.

The invention discloses a thoracic surgery postoperative complication assessment method and system based on multi-dimensional analysis, and relates to the technical field of thoracic surgery postoperative complication. Key sign signals such as heart rate fluctuation amplitude Cxv, respiratory rhythm drift coefficient Chx, pulse waveform disturbance degree Cmd and epidermal temperature change rate Cbt of a postoperative patient are continuously collected and denoised through a postoperative sign data acquisition module, and a structured sign data set TW is formed. By means of the method, tiny physiological fluctuation of a postoperative patient within 24 hours can be captured, early risk signals are prevented from being neglected, and the time sensitivity of complication recognition is improved from the source. A dynamic sliding window regression technology is introduced to carry out trend modeling on the sign data, so that a miniature instability section can be accurately extracted; through the improvement, the system can capture precursor signals of potential infection and bleeding earlier, and the possibility of early intervention is improved.

The invention belongs to the field of intelligent driving interaction, discloses an HUD interaction method and system combining DMS and ADAS functions, and constructs a multi-mode state sensing system by obtaining a real-time image of a driver and steering wheel grip data. According to the method, eye, pupil and head posture recognition based on deep learning is complementary with a grip distribution matrix, so that fatigue behaviors, distraction behaviors and abnormal holding behaviors can be detected earlier and more accurately, and the precision and robustness of driver state recognition are effectively improved. According to the method, after the fatigue degree of the driver is analyzed, the cognitive load of the driver is dynamically evaluated in a finer-grained mode by constructing the composite indexes based on the eyelid opening degree, the sight line deviation, the grip variation coefficient and the respiratory rhythm. The evaluation mode can reflect the attention, mental load and information processing capability of the driver in real time, so that the system can generate a prompt strategy which better conforms to human factors according to the current state of the driver.

The invention discloses an external diaphragm pacemaker regulation and control method and device based on a respiratory radar. The method comprises the following steps: acquiring a chest radar echo signal of a target; analyzing the radar echo signal to obtain a target breathing signal; analyzing the target breathing signal to obtain breathing parameters, and generating a synchronizing signal; the treatment parameters are adjusted by adopting the breathing parameters and the synchronizing signals; the treatment parameters are adopted to output control signals; and regulating and controlling the external diaphragm pacemaker based on the control signal. The breathing state of the target is detected and sensed in real time through the breathing radar, key parameters are extracted, and the opportunity and intensity of electrical stimulation are dynamically adjusted based on the key parameters, so that personalized synchronization with the natural breathing rhythm is realized, and the treatment efficiency and the user comfort are improved.

The invention relates to a neonatal respiratory distress risk assessment construction method, which comprises the following steps of: implementing non-intrusive micro-disturbance operation on a neonatal, and detecting respiratory rhythm phase drift, respiratory driving force low-frequency suppression and physiological tremor window change under disturbance so as to identify a pre-instability signal regulated and controlled by a respiratory system; collecting nerve and respiration feedback path data, analyzing the time difference between respiration instruction conduction and actual respiration starting, and identifying an abnormal decoupling phenomenon; based on multiple times of perturbation induction and feedback acquisition results, continuously monitoring the micro-adaptive recovery capability of the respiratory system of the newborn, and identifying the hidden fatigue trend of the respiratory system by judging progressive extension of respiratory frequency recovery time and gradual upward movement of respiratory power consumption; the respiratory redundancy regulation capability in the stress regulation process is monitored, premature intervention and repeated activation of an auxiliary breathing mode or a respiratory muscle group are judged, and the redundancy regulation critical compression phenomenon of a respiratory regulation system is analyzed to complete evaluation construction of the neonatal respiratory distress risk.

The invention relates to the technical field of medical auxiliary equipment, and discloses a self-adaptive guiding system and method based on patient posture positioning, and the system comprises a multi-modal sensing module which obtains a skeleton key point sequence, body pressure distribution data and human eye vision parameters of a patient; the intelligent processing module is used for calculating the real-time posture of the patient and matching the real-time posture with the reference posture to generate a multi-dimensional adjustment instruction; the AR-HUD display module is used for calibrating a virtual image based on human eye visual parameters, superposing a three-dimensional arrow direction indication and a semitransparent target body position contour to an actual visual field through a space registration technology, and synchronously displaying a respiratory rhythm guide light band and instruction text content; and the bidirectional feedback module is used for receiving the body position adjustment completion signal of the patient and synchronously displaying an AR view, a skeleton key point matching result and an instruction execution state in real time. According to the invention, the accuracy of posture adjustment of the patient and the treatment efficiency are remarkably improved, and the risk of treatment effect reduction caused by action deviation is effectively reduced.

The invention relates to the technical field of behavior prediction, in particular to an intelligent shooting behavior prediction and optimization method based on deep learning, which comprises the following steps: acquiring stretching data to analyze a displacement trend, identifying synclastic enhancement and sudden change synchronization, acquiring aiming speed, grip strength, recoil and respiration data to analyze collaborative conflicts, and extracting angle change to classify an aiming trend. Integrating the three types of labels to judge behavior deviation, and matching prompt content to output a prediction result. According to the method, the stretching trend change of the sensing area is recognized, the structure change state sensing is enhanced, the aiming speed, the holding force, the recoil response and the dynamic sequence of the respiratory rhythm are integrated, the cooperative relation of behavior driving factors is mined, the aiming angle deviation frequency is extracted, the behavior trend characteristics are recognized, and transverse comparison is conducted between multiple types of labels; the behavior deviation direction and the trend affiliation are determined and matched to the prompt content and then are output in a classified mode, a whole-process closed loop from multi-source data analysis to prediction guidance is achieved, and prediction accuracy, real-time performance and tactical adaptation degree are improved.

The invention relates to the field of intelligent medical treatment, in particular to a percutaneous lung channel acupoint electrical stimulation optimization system based on artificial intelligence, which comprises a multi-source data acquisition module (M1), a dynamic modeling module (M2) and a self-adaptive electrical stimulation module (M3). M1, collecting lung channel acupoint skin impedance, infrared thermal imaging, respiratory rhythm and cough frequency data in real time, and generating multi-source fusion features through space-time alignment; m2, based on a deep learning model, combining with a preset database to dynamically generate an electrical stimulation parameter combination, and ensuring parameter safety through impedance calibration and constraint optimization; and M3, adjusting the stimulation intensity according to the parameters, and setting an impedance threshold to trigger power-off protection. The traditional Chinese medicine meridian theory and the AI algorithm are fused, accurate acupoint positioning, personalized electrical stimulation and dynamic safety regulation are achieved, and the respiratory system disease treatment effect is improved.

The invention relates to a wearable music diaphragm pacemaker and a use method thereof, and belongs to the technical field of medical rehabilitation equipment, the wearable music diaphragm pacemaker comprises a diaphragm pacemaker host, an electrical stimulation module, a respiration monitoring module and a wearable bag, the diaphragm pacemaker host is connected with the electrical stimulation module and the respiration monitoring module through wires, the electrical stimulation module adopts an electrode patch, and the wearable bag is connected with the electrical stimulation module. The respiration monitoring module is used for applying adjustable electric pulses to phrenic nerves or diaphragm muscles, and is provided with a piezoelectric pressure sensor for collecting thoracico-abdominal respiration movement signals in real time. In the invention, compared with a traditional diaphragm pacemaker which mainly depends on electrical stimulation to passively enhance diaphragm contraction and a patient needs to actively cooperate with respiratory training, through real-time matching of rhythm and tone changes of megawatt music and electrical stimulation parameters, such as inspiratory phase tone rising and expiratory phase rhythm slowing, multi-sensory synergistic stimulation is formed; perceiving and active cooperation of the patient on the respiratory rhythm are enhanced, and therefore the rehabilitation effect can be effectively improved.

The invention relates to the technical field of auxiliary medical treatment, in particular to an oxygen generator on-demand pulse oxygen supply control method based on respiratory rhythm perception, and the method comprises the steps: obtaining a plurality of respiratory pressure sequences, and dividing the respiratory pressure sequences into an expiratory phase and an end-expiratory transition section; determining a signal confidence coefficient according to the performance of the expiratory phase and the end-expiratory transition section in the respiratory pressure sequence; a regularization item of physiological rationality constraint is introduced into the regression loss function, and time sequence smooth constraint is applied to a model cell state updating process to obtain an optimized respiratory rhythm prediction model; taking the three-dimensional feature vector as input data of a respiratory rhythm prediction model to obtain an end-expiratory stationary section duration ratio of a next respiratory cycle; and determining a pre-judgment window period from the beginning of inspiration according to the duration proportion of the end-expiratory stationary section of the next respiratory cycle, and controlling pulse oxygen supply of the oxygen generator by taking the pre-judgment window period as a pulse trigger countdown parameter of the oxygen generator, thereby realizing accurate synchronization of the oxygen supply action and the inspiration action.

The invention discloses an intelligent ward risk assessment method and system, a product and a medium. The method comprises the following steps: synchronously monitoring breathing audio and mattress pressure signals of a patient, and identifying discrete action events with remarkable amplitude and duration from pressure data; further judging whether the breathing of the patient is abnormal or not before and after the action events occur, for example, the breathing rhythm is interrupted for a long time or the breathing cycle obviously deviates from a baseline; when the two conditions of the violent action and the abnormal breathing cooperatively occur, an effective cooperative event record is confirmed; according to the amplitude and the duration of the action in the event and the severity of the abnormal breathing, weighted summation is carried out through a preset risk weight, and a single event risk score is calculated; and accumulating all event risk scores in an evaluation period to obtain a comprehensive risk score, comparing the comprehensive risk score with a risk threshold, and outputting a risk level. By implementing the technical scheme provided by the invention, the accuracy of clinical compound action risk early warning of the patient is improved.

The invention relates to a motion breathing rhythm optimization method, system and device based on somatosensory synchronization of smart clothing and a medium, and the method comprises the steps: collecting human motion posture data in real time through a sensor assembly, extracting the main frequency and phase features of a motion rhythm through a time-frequency mixed analysis method, and recognizing the type and intensity of a current motion; according to the current motion type and intensity, calling a matched inspiration and expiration ratio parameter in a preset respiration database, predicting a starting point of a next period by combining the phase characteristics, and generating a target respiration rhythm template containing a phase compensation amount; generating a pneumatic tactile waveform parameter group based on the target respiratory rhythm template, controlling the working time sequence and the pressure gradient of a flexible pneumatic assembly of the intelligent garment according to the pneumatic tactile waveform parameter group, and forming simulated respiratory fluctuating waves propagating along the direction of the intercostal muscles on the body surface of the human body. Therefore, implicit accurate regulation and control of the motion respiratory rhythm are achieved through the synergistic effect of pneumatic tactile feedback and an intelligent algorithm.

The invention relates to an adaptive breathing phase estimation method, which comprises the following steps of: firstly, acquiring data through a breathing flow sensor, preprocessing a breathing signal by adopting a Kalman filter, removing noise interference in the breathing signal, and ensuring that the acquired breathing data is accurate and reliable; further, the extreme value of the lung volume curve is calculated, then potential phase conversion points are recognized, and division of breathing phases is achieved; then, through a real-time anomaly detection mechanism, non-rhythm respiration data is quickly rejected, and only rhythm respiration data is reserved for estimating a respiration phase. According to the algorithm, the safety of the breathing assisting system in the breathing training process can be ensured, and human body confrontation caused by control logic confusion is avoided; and finally, estimating the respiratory rhythm change of the human body based on a prediction algorithm of physiological features, and completing the accurate recognition of the respiratory phase. The method provided by the invention can effectively adapt to human respiratory rhythm changes, and can provide powerful support for real-time control and stable operation of a respiratory assist system.

The invention relates to the field of intelligent medical treatment, in particular to a human body full-size respiratory rhythm respiration three-dimensional geometric model and a construction method thereof. The method includes: acquiring image data and anatomical data of a respiratory tract; performing first respiratory tract three-dimensional reconstruction based on the image data to obtain a first respiratory tract geometric model; performing second respiratory tract three-dimensional reconstruction based on the anatomical data to obtain a second respiratory tract geometric model; the first respiratory tract geometric model and the second respiratory tract geometric model are spliced to obtain the full-size respiratory tract geometric model, the full-size respiratory tract geometric model is constructed, and compared with an existing model, the full-size respiratory tract geometric model is more comprehensive and fits the whole particle motion process in the actual respiratory tract; basic conditions are provided for research on the rule of respiratory tract particle deposition, and good clinical value is achieved.

The invention relates to the field of breath-holding training, and discloses a breath-holding training device capable of synchronously detecting respiratory rhythm and heart rate, which comprises a support frame, a hollow plate is arranged on the inner side of the support frame, the same fixing mechanism is arranged on the left side and the right side of the support frame, and the fixing mechanism is used for conveniently fixing the device. A detection mechanism is arranged on the inner side of the hollow plate, the detection mechanism is used for conveniently detecting the heart rate of a patient by the device, and a dismounting mechanism is arranged on the inner side of the supporting frame and used for conveniently dismounting the hollow plate by a worker; the fixing mechanism comprises a first hollow block, and the first hollow block is fixedly connected to the right side of the supporting frame. The connecting rod can drive the pawl to rotate, the fixing belt can be wound around the waist of a patient, the mounting piece is fixed, and therefore the device is fixed to the waist of the patient, the supporting rod drives the winding roller to rotate, and the length of the fixing belt can be conveniently adjusted.

This invention provides a method and system for risk warning of dysphagia based on multi-signal analysis. The method includes: acquiring surface electromyography (EMG) signals, bioimpedance signals, laryngeal vibration signals, and neck microenvironment temperature and humidity signals from the wearer's neck using a flexible wearable sensing unit; using the respiratory phase determined by the neck microenvironment temperature and humidity signals as a time reference, performing time alignment and preprocessing on the multimodal physiological signals to obtain multi-channel time-series data; extracting and fusing features from the multi-channel time-series data to generate a discriminative fusion feature vector; inputting the vector into a pre-trained classification model for real-time inference to identify swallowing function states, including effective swallowing, ineffective swallowing, and suspected aspiration; and generating a graded risk warning signal in response to the identified abnormal states. This invention solves the problems of motion artifact interference and time alignment through multimodal signal fusion and respiratory rhythm calibration, achieving accurate and continuous monitoring of dysphagia risk.

The application discloses a kind of unmanned plane overhead wounded detection and priority recommendation method and system for medical service training and storage medium, it is related to computer vision technical field.For realizing the quick identification of wounded in training scene and priority treatment decision.First, obtain the overhead video stream of training area, carry out ground texture suppression and illumination difference processing to continuous frame, construct ground structure gradient field and detect human body profile curvature abnormal section, generate profile confidence chart;Subsequently, according to the confidence chart positioning head and shoulder area, extract pitch attitude, bleeding distribution and limb movement characteristics, obtain motion frequency index;Respiratory rhythm is estimated based on head and shoulder area and pitch attitude, and is fused with bleeding proportion and motion frequency, constructs life-behavior joint parameter vector;Finally, input preset weight matrix to calculate the severity of illness emergency degree score of joint parameter vector, divide treatment priority and generate spatial distribution chart, realize the comprehensive quantitative evaluation and visual management of wounded state.

The invention provides a traditional Chinese medicine acupuncture and respiratory training integrated rehabilitation apparatus. The rehabilitation apparatus comprises a controller; the respiration detection assembly is electrically connected with the controller through a transmission line; and the mounting assembly is arranged on the side surface of the controller. According to the traditional Chinese medicine acupuncture and respiratory training integrated rehabilitation apparatus, through mutual cooperation of the controller, the respiration detection assembly, the mounting assembly, the guide assembly, the driving assembly and other structures, when the rehabilitation apparatus is used, the respiration detection assembly collects respiration data in real time and transmits the respiration data to the controller, and after the controller analyzes the respiratory rhythm, the respiratory training is completed. Through a servo motor, a threaded rod and a threaded sleeve of the driving assembly, a sliding plate, a mounting ring and an acupuncture needle of the guiding assembly are driven to move accurately, automatic and accurate cooperation of acupuncture and respiratory rhythm is achieved, manual control is not needed, and the influence of human factors on the treatment effect is reduced; therefore, the problems that in the prior art, cooperation of acupuncture and respiratory rhythm depends on manual work, and accuracy is low are solved.

The invention discloses an AI sleep monitoring intelligent ring based on dynamic breath feedback and a health early warning system, and relates to the technical field of intelligent wearable equipment, and the AI sleep monitoring intelligent ring comprises an acquisition module which is used for acquiring photoelectric volume pulse waves, skin electrical activity and body temperature and body movement signals during sleep; the respiratory phase module extracts and constructs a time alignment frame of respiratory rhythm synchronization based on photoelectric volume pulse waves, the phase segmentation alignment and signal enhancement module performs specific filtering enhancement on multi-mode signals according to respiration, the enhanced signals are guided into the recognition module, a personalized health reference module is established by combining results, the physiological state is monitored in real time, and the recognition module is used for recognizing the physiological state. And when the reference is deviated, the health early warning module is triggered, the personalized respiration guiding module is started according to the abnormal type, the respiration rhythm is adjusted through tactile feedback, the guiding intensity is dynamically adjusted according to the intervention effect, progressive separation is achieved, new data is fed back to the personalized health reference model, and continuous self-adaptive updating is completed.

The application discloses a kind of multi-channel sensing fusion systems for exposure stress judgment, it is related to sensing fusion technical field, for solving the problem of insufficient accuracy of stress exposure state judgment, by the monitoring equipment corresponding to the working individual, the respiratory rhythm, the heart beat interval, the skin electricity response and the action trajectory such as multi-dimensional physiological and behavioral parameters in the working process are continuously collected, the multi-channel stress state observation basis is constructed, the feature extraction and abnormal discrimination of each channel data are carried out, the state mode is classified modeling using pattern recognition method, combined with cross-channel consistency analysis and causal order constraint, the abnormal response is comprehensively evaluated, when the evaluation result meets the preset stress exposure determination condition, combined with action trajectory characteristics, the stress exposure grade of working individual is graded determination, and according to the determination result, continuous monitoring or event record and information upload operation is selected, realize the automation, real-time judgment of stress exposure state.

This invention discloses an intelligent, adaptive local oxygen supply system and its control method, belonging to the technical field of oxygen supply auxiliary equipment. The system includes an oxygen supply module, a target tracking module, an adaptive air delivery module, and a control module. The target tracking module acquires the spatial coordinate information of the user's face or nasal cavity area in real time; the control module generates a pointing command based on the spatial coordinate information, driving the adaptive air delivery module to perform displacement compensation, ensuring the oxygen jet direction is towards the target area. Furthermore, the system, through a respiratory rhythm monitoring module in conjunction with a throttle switch, closes the throttle switch during the exhalation phase and accumulates pressure in the buffer pressurization container; during the inhalation phase, it triggers the throttle switch in advance to release pressurized oxygen based on the airflow flight time difference calculated by the infrared ranging module, achieving synchronous coverage of the oxygen jet and the inhalation peak. This invention can automatically adjust the air delivery direction according to changes in the user's position and match the respiratory rhythm for intermittent pressurized oxygen supply, effectively improving oxygen utilization.

The invention discloses a non-invasive sleep monitoring and intervening system, which constructs a thoracic and abdominal ventilation displacement model through human body micro-motion phase calculation of millimeter wave radar, and realizes continuous and non-contact monitoring of respiratory rhythm, ventilation change and body motion state. The system further utilizes a time-frequency fusion feature and trend analysis method to identify and predict sleep disorder events such as snoring, apnea and hypopnea in real time, and generates acoustic disturbance signals meeting sound pressure and frequency constraints before and after the events are formed, so that the ventilation mode or sleeping posture of a user is promoted to be controllably and finely adjusted on the premise that the user is not awakened, and the sleep disorder events are prevented from being disturbed. Therefore, the abnormal event duration is shortened and the sleep quality is improved. The method is suitable for sleep monitoring equipment, bedside lamps, intelligent sound boxes or desktop type health equipment, and algorithms and intervention strategies can be further optimized through software upgrading.

The embodiment of the invention relates to the field of data processing and machine learning, and provides a respiratory rhythm recommendation method based on multi-modal data fusion and a related device.The method comprises the steps that electrocardiosignals of a to-be-detected user during CTA examination are obtained; performing heart rate state evaluation according to the electrocardiosignal to obtain a heart rate state evaluation result; if the heart rate state evaluation result indicates that the to-be-detected user is in a heart rate stable state, respiratory rhythm evaluation parameters of the to-be-detected user are obtained; and pushing the target rhythm information to the to-be-detected user in a voice, image or rhythm rhythm mode and the like, and guiding the to-be-detected user to carry out autonomous adjustment, so that respiration guiding closed-loop control from monitoring to intervention is realized, and the coronary artery CTA image quality and diagnosis stability are optimized.

The invention relates to the technical field of medical data analysis, in particular to an intelligent screening method for respiratory disorders. According to the method, the key feature point sequence is constructed by accurately recognizing the speed peak value and the change inflection point in the continuous respiratory airflow data, fine description of the dynamic structure of the respiratory rhythm can be achieved, and then the dynamic structure of the respiratory rhythm can be obtained according to the change trend of the time interval between the key points. Long-time respiration monitoring data are automatically segmented into a plurality of sections capable of reflecting different expressions such as rhythm stability, acceleration or deceleration, so that a context with physiological significance is provided for subsequent analysis, and the irregularity of the respiration rhythm in each section is quantized by utilizing an information entropy theory; according to the method, an objective index for measuring the rhythm stability can be obtained, and pseudo events caused by swallowing, body movement and the like can be effectively filtered out by judging whether the change of the rhythm entropy value before and after a suspected pause event is obvious or not and combining with confirmation of the airflow amplitude contraction trend before and after pause.

A computer-implemented method is provided to estimate a periodic feature, such as a period length or a period location, of a physiological rhythm, such as a cardiac rhythm or an autonomic respiratory rhythm. The computer-implemented method comprises receiving a signal representing the physiological rhythm, estimating a first set of periodic feature estimates based on the signal, generating a prior probability of the periodic feature based on at least a subset of the first set of periodic feature estimates, and estimating a second set of periodic feature estimates based on the first set and the prior probability. By using such a computer-implemented method, the accuracy of the signal is improved, in particular when the signal is obtained from an accelerometer arranged on the chest of a subject.

The invention discloses an intelligent regulation and control system and method for tea fermentation, and relates to the technical field of tea processing, and the method comprises the following steps: S001, establishing a synchronous acquisition process, continuously recording a wind speed rhythm, a spraying rhythm, air pressure, humidity and leaf surface temperature, and generating a damp-heat cross plot according to a time sequence for identifying an overlapping relationship between wind flow and a humidifying rhythm; and S002, identifying a beat overlap region based on the damp and hot cross plot, setting a spray collection window and a spray stop buffer window in the beat overlap region, and generating phase pointer data for time sequence coordination by adjusting spray residence time and a diffusion range. According to the method, rhythm overlapping recognition is achieved through multi-parameter synchronous collection and a damp-heat cross plot, wind and fog dislocation regulation and control are achieved based on a phase pointer, evaporation stress is slowly released by combining measures such as fog particle preposition, temperature rise gentle slope and wind direction guiding, closed-loop control is constructed based on a breathing rhythm index, and self-adaptive steady-state regulation of the fermentation environment is achieved.

This invention discloses a multivariate fusion detection method for neonatal respiratory distress monitoring, belonging to the field of variable detection technology. It addresses the problem of inaccurately reflecting changes in true respiratory rhythm under scenarios with sudden changes in light intensity or frequent occlusion. By setting the light source observation time and dividing it into luminous and non-luminous phases, the method extracts the photoelectric response voltage to achieve temporal separation of ambient light and detection light. A photoelectric receiving occupancy level is constructed based on probe contact pressure, and the driving current of the light-emitting diode is compensated and adjusted based on this occupancy level. Ambient light irradiance data is collected under compensated driving conditions. Furthermore, the frequency of ambient light occlusion is analyzed by varying irradiance, and a joint judgment index is constructed by combining this with the respiratory waveform amplitude. This adaptively selects between conventional monitoring and compensated maintenance processing, achieving stable acquisition and quality improvement of respiratory signals under complex lighting and contact conditions.