293 results about "Respiratory cycle" patented technology

The invention discloses a real-time image registration and path planning system in bronchoscope surgical navigation, relates to the technical field of image registration, and is used for solving the problems that in a thoracic and abdominal minimally invasive interventional operation, organ movement and deformation caused by breathing of a patient can cause obvious influence on the registration precision of a navigation system, and the navigation precision is poor. Space registration of preoperative images and an anatomical structure of a patient in an operation often has errors due to mismatching of breathing phase changes; multi-attitude data are fused, the respiration interference degree is comprehensively evaluated through double indexes, the problem that a traditional static deformation field cannot be matched with dynamic respiration is solved through respiratory cycle driving deformation prediction, displacement errors are corrected through tidal volume observation values, and B spline control point weights are reversely optimized through registration residual errors. A deformation field can adapt to breathing amplitude and frequency differences of different patients, online distillation learning is carried out by taking a registration error output by a filter as a teacher signal, and local residual deformation and chronic changes which are difficult to describe by a traditional global model are accurately captured.

Disclosed herein are systems, devices, and methods for stimulating nerves, including electrically stimulating peripheral nerve(s) to treat various diseases and disorders, as well as systems and methods for applying stimulation waveforms for improving the therapeutic benefit, outcomes, and / or experience relating to the same. Some systems and methods generate a biphasic stimulation waveform that includes a plurality of pulses with alternating leading phases. Some systems and methods vary one or more parameters of the stimulation waveform to correlate with a characteristic of the user. Example characteristic of the user include a phase of a tremor exhibited by the user and a respiratory cycle of the user. For some systems and methods, the stimulation waveform includes a plurality of burst with each burst ramping up in amplitude. For some systems and methods, the stimulation waveform is delivered during a time period that includes an on-cycle portion and an off-cycle portion with the stimulation waveform only being delivered during the on-cycle portion of the time period.

The invention discloses a multi-source data-based medical temperature-adjusting atomization drug delivery system for the pneumology department, relates to the technical field of medical temperature-adjusting atomization drug delivery, and aims to solve the problems that the atomization effect is poor and the atomization process is not intelligent. By adjusting atomization power in different respiratory cycle stages, monitoring airway abnormity and respiratory fluctuation in real time to correct parameters and adapting temperature control and atomization components as required, medicine waste and airway stimulation can be reduced, variables such as air source fluctuation and liquid medicine viscosity can be coped, atomization stability is guaranteed, administration efficiency and safety are improved, and the method is suitable for large-scale popularization and application. The requirements of patients with weak respiratory functions are met, the basic information unit is linked with the medical history of the patients and the medicine information to determine the safe temperature, the atomization rhythm is matched in combination with the respiratory parameters, temperature deviation caused by information dispersion can be avoided, manual errors are reduced, and the medication safety is improved.

The invention discloses a non-contact temperature calibration method and system, electronic equipment and a storage medium, and relates to the technical field of temperature calibration. The method comprises the following steps: acquiring a surface temperature value of a bionic head mold; collecting a temperature measurement value of the standard temperature source by the thermometer to be calibrated and an environment parameter value of the environment, wherein the environment parameter value comprises a temperature fluctuation value and a humidity fluctuation value; performing segmentation processing on the environment parameter values and the temperature measurement values based on a preset human body breathing frequency to obtain an environment parameter value sequence and a temperature measurement value sequence corresponding to a plurality of breathing cycles; calculating the fluctuation gradient corresponding to the environmental parameter value sequence in each respiratory cycle and the deviation change rate between the temperature measurement value sequence and the surface temperature value; and determining a temperature compensation coefficient according to the fluctuation gradient and the deviation change rate, and calibrating the temperature measurement value of the to-be-calibrated thermometer based on the temperature compensation coefficient to obtain a target temperature value. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of temperature measurement is achieved.

The invention relates to the technical field of emotional robots, and discloses an embracing emotional robot which comprises an inner shell, low-cost and high-simulation breathing simulation is achieved through cooperative control of an air pump and an air valve, and the embracing emotional robot is designed by combining simulation body temperature brought by a heating piece and synchronous action of a rotatable arm. The emotion robot can naturally rotate forwards when being embraced and is matched with simulated respiration and simulated body temperature, so that the immersion and emotion interaction experience of a user are remarkably improved; the operation cycle of the air pump strictly follows biological breathing characteristics, the inspiration stage accounts for 2 / 3 of the breathing cycle, the speed of the inspiration stage changes fast and slow, the expiration stage accounts for 1 / 3 of the expiration stage, smooth pressure relief is achieved through the air valve, and the highly-simulated breathing mode can effectively guide a user to breathe synchronously, so that parasympathetic nerves are activated, the anxiety level and heart rate fluctuation are reduced, and the user experience is improved. And the whole structure adopts a modular design, complex functions such as breathing simulation, embracing and body temperature simulation can be realized, the maintenance is convenient, and the device is particularly suitable for long-term accompanying scenes.

The invention discloses a PET / MR (positron emission tomography / magnetic resonance) all-in-one machine-based dynamic attenuation correction and respiratory and cardiac dual motion correction method. The method comprises the following steps of: generating a dynamic mu graph of a respiratory cycle; mR cardiac function movie sequence scanning of the cardiac cycle; accurately segmenting the cardiac muscle; 6, performing tissue segmentation on the dynamic mu graph; clustering the PET data; pET data space-time registration is carried out; merging PET data of the same time phase; performing frame-by-frame dynamic attenuation correction; respiratory movement correction; correcting a partial volume effect; carrying out myocardial MR image space standardization on different time phases of the cardiac cycle; and carrying out spatial standardization on the myocardial PET image of different time phases in the cardiac cycle. According to the method, the mu graph of the respiration and cardiac cycles is reconstructed through time-phase sharing and is accurately registered with the PET data, so that the high-quality attenuation correction of the heart PET image is realized and the motion correction is completed at the same time.

The invention discloses a target position prediction method and system based on body surface point cloud data, and relates to the technical field of medical signal processing, and the method comprises the steps: obtaining 4DCT reconstruction point cloud data of a plurality of subjects and body surface point cloud data in a respiratory cycle, and building an in-vivo point cloud data set and a body surface point cloud labeling data set; training a body surface and in-vivo point cloud correlation model by using the body surface point cloud labeling data set and the in-vivo point cloud data set, inputting 4DCT reconstruction point cloud data of the patient and the body surface point cloud contour of the current breathing moment into the trained body surface and in-vivo point cloud correlation model, and obtaining a final prediction phase and a final target region transformation matrix; and in combination with the 4DCT reconstructed point cloud data and the body surface point cloud contour at the current breathing moment, obtaining a predicted position of the in-vivo target region on the body surface point cloud contour at the current breathing moment. According to the invention, the position of the target area can be accurately predicted on the body surface contour, the accuracy of radiotherapy irradiation is improved, and radiation hazards are reduced.

The invention relates to the technical field of atomization rate control, in particular to an intelligent atomization compliance monitoring method and system for children. The method comprises the following steps: firstly, acquiring expiration flow velocity, inspiration flow velocity and multi-direction motion acceleration time sequence data of a child in a historical monitoring period, and reflecting breathing and behavior action conditions; dynamically analyzing the numerical characteristics of the expiration flow velocity and the inspiration flow velocity to divide a respiratory cycle, and analyzing the cycle length, the inspiration flow velocity cumulant and the like to construct a respiratory characteristic vector; screening a target monitoring period based on the breathing feature vector and the period number similarity; further analyzing the motion acceleration change and accumulation characteristics in the target monitoring period, and quantifying the behavior compliance expression degree; the behavior compliance expression degree and the breathing feature vector are deeply fused, a'breathing state-behavior cooperation 'joint evaluation model is established, the breathing and behavior states of children are dynamically recognized, the medicine atomization rate is accurately adjusted, and effective deposition of lung medicine is improved.

The invention discloses a tumor respiratory movement trajectory prediction method and system based on spatio-temporal feature separation LSTM, and the method comprises the following steps: collecting a four-dimensional CT respiratory cycle image, extracting a tumor centroid coordinate, and generating a standardized tumor centroid coordinate sequence through sliding window standardization; the method comprises the following steps: inputting bidirectional LSTM to calculate Euclidean distance screening features and weighting to generate a weighted spatial feature vector sequence, extracting time recursion feature weighted fusion through one-dimensional convolution and unidirectional LSTM, predicting a tumor motion trajectory through autoregression, and comparing real-time coordinate correction to generate an adaptive correction trajectory prediction result. A four-dimensional image sequence is extracted and standardized through registration, a two-way network is combined to calculate two-way distance features to recognize breathing phase changes, spatial-temporal features are fused to establish a nonlinear recursive relation to capture tumor motion differences, real-time correction optimization is achieved through deviation statistics and weight self-adjustment, and prediction stability and trajectory fitting precision are improved.

The invention discloses a point cloud-driven thoracic cavity whole organ dynamic reconstruction and respiration monitoring method and system, and the method comprises the steps: constructing a three-dimensional geometric model corresponding to each target structure in a thoracic cavity based on the thoracic medical image data of a target object, and converting the three-dimensional geometric model into static point cloud data; driving the static point cloud data to perform dynamic deformation simulation in a respiratory cycle based on a respiratory movement rule of the target object to obtain dynamic point cloud data synchronized with a respiratory time phase; performing time-space synchronization association on the dynamic point cloud data and the electrical characteristic parameters, and constructing a digital twin thoracic cavity model; deploying a virtual sensing assembly which dynamically deforms along with the thoracic cavity in the digital twin thoracic cavity model, simulating and monitoring the dynamic breathing process of the target object, and generating a virtual physiological signal; and performing signal processing on the virtual physiological signal to obtain a dynamic reconstruction image reflecting respiratory movement. Continuous simulation of the full-breathing movement process is achieved, and the temporal-spatial resolution and diversity of a virtual database are improved.

The invention relates to the technical field of sleep assessment, and discloses a sleep monitoring method, a breathing parameter control method and breathing equipment, and the method comprises the steps: obtaining an airflow rate signal of a user in a first time period; according to the airflow rate signal of the first time period, the breathing depth and the breathing rate of each breathing cycle are determined; processing the breathing depth and the breathing rate of each breathing cycle, and determining time domain features, frequency domain features and nonlinear features corresponding to the breathing depth and the breathing rate in a plurality of second time periods; based on the trained machine learning model and the time domain features, the frequency domain features and the nonlinear features corresponding to the breathing depths and the breathing rates in the multiple second time periods, the sleep states of the user in all the second time periods are determined, and the sleep states comprise the sober stage, the light sleep stage, the deep sleep stage and the rapid eye movement stage. According to the invention, sleep quality evaluation can be carried out without adding extra hardware and changing the sleep environment, and the accuracy of the determined sleep quality is improved.

The invention relates to the technical field of medical image processing, and discloses a tumor state prediction method, system and device and a medium, and the method comprises the steps: obtaining a CT image group of a lung of a target object in a current respiratory cycle, and constructing a three-dimensional tumor model of a lung tumor based on the CT image group; the lung tumor has a real volume and a real centroid coordinate; performing finite element analysis to obtain a finite element tumor model, and calculating the simulated volume and simulated centroid coordinates of the lung tumor; comparing to obtain a volume retention ratio and a gravity center offset; performing motion state prediction on the lung tumor in the next respiratory cycle to obtain an initial motion state parameter of the lung tumor in the next respiratory cycle; if it is judged that the motion state prediction quality of the lung tumor meets the preset motion state prediction quality based on the volume retention ratio and the gravity center offset, the initial motion state parameters serve as the target motion state parameters, the motion state of the lung tumor is obtained through prediction, and the accuracy and accuracy of radiotherapy irradiation are improved.

The invention relates to the technical field of precise monitoring medical treatment, and discloses a breathing machine control strategy debugging method and system based on an EIT image, and the method comprises the steps: constructing a breathing machine control algorithm model based on deep learning through a case library, and representing a mapping relation between a case EIT image and a key index thereof and a breathing machine ventilation control parameter; acquiring current EIT images of a plurality of respiratory cycles of the patient, inputting the current EIT images into the respirator control algorithm model, matching the most similar case from the case library, and performing respirator ventilation control by using target respirator ventilation control parameters of the most similar case; operation is controlled to start, adjustment EIT images and key indexes of a plurality of respiratory cycles of the patient are obtained, and the adjustment EIT images and the key indexes are compared with the EIT images and the key indexes of the most similar cases for judgment; if the requirements are met, continuously controlling and updating the case library, and optimizing the model; otherwise, switching the control mode. Determination of breathing machine control parameters participated by a patient is reduced, trial and error times of clinical parameter adjustment are reduced, and patient safety and control accuracy are both considered.

The invention relates to a dynamic tumor tracking method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring 4DCT image data of a patient, and performing mixed data enhancement on the 4DCT image data to generate a 3DCT image representing each respiratory stage of the lung; based on the 3D CT image, generating an X-ray image in a respiratory cycle of a simulated patient by adopting a mathematical simulation method; registering the X-ray image and the 4DCT image data to generate a tumor tracking model of the patient; and inputting an X-ray image of a patient in a radiotherapy process into the tumor tracking model, and outputting a tumor tracking result through the tumor tracking model. According to the invention, real-time three-dimensional tracking of the tumor can be realized by using a single low-dose X-ray image under any geometric condition, high-precision tumor positioning can be provided on the premise of ensuring low radiation dose, the method is suitable for any projection angle and source-object distance change conditions, and various geometric relationship changes in the treatment process can be flexibly coped with.

The invention relates to the field of medical instruments, in particular to a respiratory capacity real-time monitoring method and system of a medical vaporizer. According to the method, the initial atomization airflow flow of the medical atomizer is obtained, the breathing signals of the user in the multiple breathing cycles are collected in the first preset time period, and the initial tidal volume information is obtained. Then, the respiratory signals are converted into a power spectrum density map through fast Fourier transform, inspiration waveforms are extracted to obtain tidal volume characteristic values, and deep analysis of the respiratory signals is achieved. Meanwhile, resistance values and temperature values of the heating wire in a plurality of respiratory cycles are collected, a temperature-resistance change coefficient is generated according to the data, and equipment performance changes are quantified. Based on this, the actual atomized airflow flow is calculated in combination with the initial atomized airflow flow and the coefficient. Finally, the actual atomization airflow flow, the tidal volume characteristic value and the coefficient are synthesized to obtain the actual tidal volume, the medicine dosage is matched according to the actual tidal volume, all the steps are linked with one another, and accurate treatment is achieved through multi-dimensional data integration.

The invention discloses a cone beam CT full respiratory cycle image reconstruction method based on a four-dimensional neural network. The method comprises the following steps: step 1, acquiring cone beam projection data and a respiratory signal in a respiratory state; 2, a respiratory cycle is divided into N phases, multi-phase cone beam projection data Qi are obtained, i = 1, 2,..., N, the Qi is reconstructed through a filtered back projection algorithm, and a multi-phase under-sampling CT reconstructed image # imgabs0 # is obtained; 3, # imgabs1 # is input into a four-dimensional neural network, and a three-dimensional deformation field Di is obtained; 4, performing weighted summation after deformation on the # imgabs2 # by using the Di to obtain a reference phase reconstruction image Xref after motion correction; and step 5, performing inversion on the Di to obtain a three-dimensional reverse deformation field # imgabs3 #, for any breathing phase, performing image deformation on the reference phase reconstruction image Xref by using the corresponding # imgabs4 #, and performing reconstruction to obtain a motion correction image Xi. According to the method, the breathing movement correction can be quickly carried out on the cone beam CT image.

The invention relates to the technical field of biology, and discloses a mechanical ventilation patient airway management optimization method and system based on artificial intelligence, electronic equipment, a medium and a program product, and the method comprises the steps: assembling a sound collection device, collecting airway sound wave data, and screening and classifying the airway sound wave data according to requirements; processing the reserved airway sound wave data, removing redundant information, and separating the airway sound wave data into single respiratory cycle samples by detecting sound loss between breaths; establishing an ANN classification and identification model of the airway secretions, determining parameter setting of the model, and verifying stability and accuracy of the trained ANN classification and identification model of the airway secretions by using the airway sound wave data; and determining an output result of the ANN classification and identification model of the airway secretions, and judging whether the airway secretions of the patient exist or not according to the output result of the ANN classification and identification model of the airway secretions.

The invention relates to the technical field of breath monitoring, in particular to a big data intelligent health breath dynamic monitoring method and system and a terminal. The method comprises the following steps: acquiring a time domain interval of each respiratory cycle according to change characteristics of a respiratory pressure signal; further obtaining work doing amount corresponding to each breathing cycle, and dividing breathing modes; the newest seasonal period of the respiratory pressure signal is further extracted, and a respiratory stability coefficient is obtained according to the change characteristics of work doing amounts of respiratory periods with adjacent time sequences in the seasonal period in combination with the number distribution characteristics of the respiratory periods of the respiratory mode; furthermore, when the breathing stability coefficient is larger than or equal to the preset stability threshold value, the predicted breathing pressure signal is obtained and verified, and finally the predicted breathing mode is obtained according to the final predicted breathing pressure signal, so that the hysteresis of real-time processing is reduced, the response speed of breathing support is increased, and the comfort is improved.

The invention discloses a chest wall defect three-dimensional reconstruction and personalized repair material design system, and relates to the technical field of intelligent medical aided design. Comprising a data acquisition module used for acquiring and preprocessing multi-modal data of the chest wall of a patient; the partition module is used for dividing the defect area into different functional partitions by coupling and analyzing stress and blood flow data and setting a threshold value; the dynamic optimization module is used for mapping the breathing time sequence to a three-dimensional space and dynamically correcting the partition boundary according to the stress change in the breathing cycle; the interface matching module is used for carrying out parameterization design on a host bone interface according to the bone mineral density data; and the optimization design module is used for carrying out topological structure and pore distribution dual optimization on the repair material based on the dynamic partition and the interface parameters, and finally, the output module generates design data capable of directly driving manufacturing. Precise matching of the repairing material and the physiological anatomical structure and the dynamic physiological function of the patient is achieved.

The invention provides a method and equipment for constructing lung function characteristics during sleep. The method is applied to lung function data processing. The method comprises the following steps: collecting a breathing velocity waveform of a target object; determining an inspiration stage and an expiration stage in each respiratory cycle based on the respiratory velocity waveform; the duration of each stage is determined based on the inspiration stage and the expiration stage, and the duration of the inspiration sub-stage and the duration of the expiration sub-stage are obtained; basic features, a stability measurement feature group and a comprehensive feature group are calculated based on the inspiration sub-stage duration and the expiration sub-stage duration; according to the method, the breathing speed waveform is acquired without burden, and the comprehensive lung function evaluation feature group suitable for the radar breathing waveform is constructed.

The invention discloses an obstructive sleep apnea event typing method and device based on respiratory center driving characteristics, electronic equipment and a computer readable storage medium, and belongs to the technical field of medical detection technologies and sleep medicine. According to the method, body surface diaphragm electromyographic signals and respiratory airflow signals of a subject are synchronously collected in a non-invasive mode, the body surface diaphragm electromyographic signals are preprocessed to obtain diaphragm electromyographic characteristic values, and the respiratory airflow signals are segmented and subjected to integral processing according to the respiratory cycle to obtain airflow characteristic values; selecting a characteristic value corresponding to at least one respiratory cycle before the event occurs as a reference baseline, calculating a diaphragm myoelectricity change value and an airflow change value of each respiratory cycle in the event relative to the reference baseline, and calculating the diaphragm myoelectricity change value based on a coupling relationship between the airflow change value and the diaphragm myoelectricity change value, and performing central drive dependent or non-central drive dependent judgment on the obstructive sleep apnea event. The method can provide technical support for typing analysis of the obstructive sleep apnea event.

The invention is suitable for the technical field of medical equipment, and provides a medical equipment ventilation control method and system, a storage medium and medical equipment.The method comprises the steps that initial ventilation pressure is calculated according to the target tidal volume and respiratory mechanical parameters measured through tentative ventilation, and the initial ventilation pressure is applied to pressure control ventilation of the first respiratory cycle; the actual tidal volume of each respiratory cycle is monitored in real time, and whether the target tidal volume is reached within the target inspiration time or not is judged; if not, the ventilation pressure of the next respiratory cycle is adjusted according to the actual tidal volume and the target tidal volume; if yes, whether the actual inspiration time is smaller than the target inspiration time is judged; and if the actual inspiration time is less than the target inspiration time, immediately stopping the ventilation flow rate to enter an inspiration pause stage, and dynamically adjusting the ventilation pressure of the next respiratory cycle according to the actual inspiration time and the target inspiration time of each respiratory cycle until the actual inspiration time is equal to the target inspiration time. The problem of tidal volume overshoot possibly generated under an existing pressure adjusting mechanism is solved.

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 disclosure provides systems and methods for treating obstructive sleep apnea using an inertial measurement unit (IMU) comprising an accelerometer and a gyroscope, wherein the IMU is configured to detect chest and / or abdominal movement by a patient during the inspiration and expiration stages of a respiratory cycle and to generate positional data based on the detected movement. Positional data generated by the IMU is used by an implanted stimulation system to determine when to deliver electrical stimulation to a nerve which innervates an upper airway muscle, such as the hypoglossal nerve, to treat sleep apnea.

An example computer system analyzes nasal airflow data to detect sleep disorders. The system can estimate the breathing cycle frequency on the pressure nasal airflow data. Based on this frequency, the system can calculate optimal smoothing window sizes. The system can perform a smoothing operation on the airflow data and auxiliary sensor data using these windows. Breathing cycles can be detected in the smoothed data streams. Cycles meeting artifact conditions but not sigh conditions can be removed as noise. Remaining clean cycles can be analyzed further. A transition segment between the peak of one cycle and trough of the next can be classified. Using this classification and the cleaned auxiliary data, the system can detect sleep disorder events.

The invention relates to the technical field of respiratory health monitoring, and discloses a remote respiratory health monitoring and management system and method. The method comprises the steps that an original respiration data set containing time sequence and frequency domain characteristics is collected through a multi-mode sensor, respiration cycle segments are divided through dynamic segmentation processing, key waveform mark points are extracted, and a respiration state transition network is constructed to form a dynamic topological structure. And generating a breathing environment coupling matrix in combination with environmental parameters, screening interfered abnormal breathing segments, training an analysis model to output a deviation score, triggering multi-level early warning and generating a report containing a backtracking path. User activity log timestamps are matched through cross-device verification, data are integrated to construct a breathing behavior association map, a model judgment threshold value is updated based on the map, and monitoring closed-loop feedback is completed. According to the method, accurate monitoring and dynamic management of respiratory health are achieved, and a complete solution is provided for remote respiratory health management and control.

An apparatus for a treatment delivery system includes a base with a first surface for resting on a patient-receiving surface of a couch of the treatment delivery system and a second surface; and a movable surface that is included in the second surface and can be actuated in a first direction away from the couch and in a second direction toward the couch. Alternatively or additionally an apparatus for a treatment delivery system includes a base with a first surface for resting on a patient-receiving surface of a couch of the treatment delivery system and a second surface; and a movable surface that is included in the second surface and can be actuated in a first direction away from the couch and in a second direction toward the couch.

The invention provides a sleep physiological parameter real-time monitoring method based on flexible piezoelectric sensing, and relates to the technical field of biomedical signal processing.The method comprises the steps that a human body sleep biological mechanical signal is collected through a flexible piezoelectric sensor array, and after the signal is converted through a charge amplifier, phase-space reconstruction denoising and self-adaptive gain control are carried out; wherein the gain is dynamically adjusted based on the weight of the user, the rigidity coefficient of the mattress and the real-time signal amplitude; extracting heartbeat wave crest intervals, respiratory cycles and body movement frequency domain energy characteristics from the preprocessed signals; inputting the features into a cascaded convolutional neural network and a long-short-term memory network model, and synchronously outputting the heart rate, the respiration rate and the heart rate variability; in combination with the historical health data of the user and the cardiovascular health mapping model, sleep quality indexes and disease risk scores are generated through graph convolutional network analysis, and the monitoring frequency is dynamically adjusted and an apnea alarm is triggered.

The invention discloses a three-dimensional in-vivo radiotherapy dose monitoring method and system, and the method comprises the steps: collecting an image sequence {In} containing time dimensions at a theta t time interval through CBCT scanning equipment in a radiotherapy process; calculating target area displacement of adjacent frames of images in the image sequence {In} by using a bidirectional optical flow estimation method; state estimation and prediction are carried out on the displacement data based on an adaptive Kalman filtering algorithm, a dynamic displacement rule of the target area in the respiratory cycle is generated, and a respiratory movement prediction result is output; acquiring radiotherapy plan parameters from a radiotherapy plan system, and calculating planned dose distribution without considering respiratory movement; dynamically adjusting a ray transmission path and energy deposition according to a respiratory movement prediction result, and calculating real-time dose distribution of each respiratory time phase; mapping the dose distribution of each time phase to a reference time phase coordinate system to generate accumulated dose distribution; and comparing and verifying the accumulated dose distribution and the planned dose distribution.