956 results about "Pulsed wave" patented technology

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention relates to a heart rate prediction and model training method and system, wearable equipment and a medium. The training method comprises the following steps: acquiring a training sample set and a corresponding label set; wherein each training sample of the training sample set comprises a photoelectric volume pulse wave sequence and a corresponding acceleration sequence, and the label set is a real heart rate; preprocessing each training sample to obtain a corresponding photoelectric volume pulse wave frequency spectrum sequence and an acceleration frequency spectrum sequence; all the photoelectric volume pulse wave frequency spectrum sequences and the acceleration frequency spectrum sequences are input into a heart rate prediction model to be trained, and corresponding predicted heart rates are obtained; and calculating the difference degree between the predicted heart rate and the corresponding real heart rate, and updating parameters of the heart rate prediction model based on the difference degree to obtain a trained heart rate prediction model. According to the method, the problems of high signal processing cost and high calculation complexity caused by the use of a complex adaptive filtering process in the prior art are solved, and the heart rate prediction accuracy can be remarkably improved.

The invention relates to a motion state detection method and device based on a multi-modal sensor, and the method comprises the following steps: carrying out the inertial parameter extraction of an original acceleration signal in the motion process of a human body through a three-axis acceleration sensor, and obtaining a motion acceleration feature vector; the original pulse wave signals in the movement process are monitored in real time, and a cardiovascular physiological feature sequence is obtained; performing time-frequency domain conjoint analysis on the time sequence myoelectricity feature data set, the motion acceleration feature vector and the cardiovascular physiological feature sequence by adopting an adaptive wavelet transform technology to obtain a multi-dimensional feature fusion matrix; and performing dynamic segmentation on the multi-dimensional feature fusion matrix through a hierarchical clustering algorithm to obtain motion state feature subspaces, thereby solving the problem that a traditional motion monitoring method mainly depends on a single type of sensor, although the motion condition of a human body can be reflected to a certain degree, the motion state feature subspaces cannot be monitored. However, the technical problem of lack of comprehensive understanding of complex motion states is solved.

The invention relates to the technical field of cardiovascular monitoring, in particular to a cardiovascular data monitoring method for the cardiovascular medicine department, which comprises the following steps of: performing sparse regularization processing on characteristic parameters of blood pressure waveform and pulse wave velocity based on a variable set of cardiovascular high-dimensional data; according to the method, redundant variables in high-dimensional data are optimized through sparse regularization processing, a dependency matrix is constructed to extract weight distribution, the accuracy and efficiency of data processing are improved, deep integration of multi-source data is achieved based on variable relevance optimization and distribution parameter updating, the fine analysis capacity of dynamic signals is remarkably enhanced, and the method is suitable for large-scale popularization and application. Through time sequence feature extraction and anomaly labeling, accurate positioning of an abnormal interval is improved, the distribution rule analysis effect of electrocardiosignals is enhanced, dynamic change rate calculation and fluctuation error correction of the pulse wave speed are combined, the reliability of dynamic trend prediction is guaranteed, extraction and evaluation of key risk factors are further enhanced, and the reliability of dynamic trend prediction is improved. And dynamic assessment and accurate quantification of cardiovascular risks are realized.

Disclosed are a method and system for controlling a robotic arm pressing force based on a pulse wave signal. The method comprises: acquiring a pulse wave signal of a target; acquiring an interaction force between an end of a robotic arm and the target, and determining an expected pressing force according to a mapping relation between a pressing effect and the pulse wave signal; establishing an admittance control model to convert the expected pressing force into an expected trajectory of the robotic arm; and determining, on the basis of an error between an actual trajectory and the expected trajectory of the robotic arm, a control quantity of the subsequent movement of the robotic arm by using a PID controller, so as to achieve the tracking of the expected trajectory. The present invention achieves a low-impedance and compliant interaction effect when the target of pressing is moving.

The invention provides a sleep monitoring model training method, a sleep monitoring method and equipment. The method is applied to radar signal processing. The method comprises the following steps: acquiring a data set S1 and a data set S2; a pure radar feature extractor M3 is trained by using a pre-trained teacher network model M2 and the data set S1, the teacher network model M2 fuses the radar data and the pulse wave data in the data set S1 and outputs fused feature data, and the pure radar feature extractor M3 performs feature extraction on the radar data in the data set S1 and outputs radar feature data; training a bimodal sleep monitoring model M4 and a pure radar modal sleep monitoring model M5 by using the data set S2, and determining first sleep stage and / or respiratory event information by a first recognition layer according to fusion feature data output by the teacher network model M2, and the second identification layer determines second sleep stage and / or respiratory event information according to the radar feature data output by the pure radar feature extractor M3. According to the invention, the sleep monitoring task is efficiently completed at low cost.

The invention relates to the field of computer medical engineering, and discloses a blood pressure continuous measurement abnormity early warning method and system based on AI data analysis, and the method comprises the steps: collecting a pulse wave signal, triaxial accelerometer data, an ambient light intensity signal and a skin conductance signal through a sensor on a preset wearable device; generating multi-scale physiological features; systolic pressure and diastolic pressure are calculated, and blood vessel stiffness is calculated; calculating the main arteriosclerosis risk level; when the main arteriosclerosis risk level is higher than a preset risk level, a user is prompted through the wearable device. According to the invention, the pulse wave, the triaxial accelerometer, the ambient light intensity and the skin conductance signal are synchronously acquired and analyzed to obtain the systolic pressure, the diastolic pressure dynamic trend and the blood vessel stiffness, and the main arteriosclerosis risk level can be accurately quantified. In addition, a'physiological parameter-angiosclerosis-risk decision 'closed-loop management mechanism is established, and automatic and active health management of the user is realized.

The invention discloses a cuff-free continuous calibratable blood pressure estimation method and system, equipment and a medium. The method comprises the following steps: establishing a blood pressure estimation model and a blood pressure calibration model, continuously estimating blood pressure based on PPG signals, calibrating blood pressure based on actively measured ECG and PPG pulse waves, and calibrating blood pressure based on actively measured ECG and PPG pulse waves and multi-modal information. The system comprises an acquisition module, a calculation module and a transmission module. According to the scheme, the cuff-free continuous calibratable blood pressure estimation process is more scientific and reasonable, the blood pressure measurement process of a user is more comfortable, the final blood pressure measurement result is accurate and is not affected by interference, meanwhile, the purpose of continuous blood pressure monitoring without frequent manual intervention is achieved, and the blood pressure measurement efficiency is improved. And the influence of the accuracy of blood pressure reading caused by individual differences is reduced.

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 discloses a wearable cardiovascular pressure early warning system, which relates to the technical field of wearable medical health monitoring and comprises wearable equipment, and a signal acquisition module, a signal processing module, a posture self-adaptive module and a communication module which are integrated in the wearable equipment. The signal acquisition module comprises a photoelectric volume pulse wave sensor and an inertial measurement unit and is used for acquiring physiological signals and motion data of a user; the signal processing module is used for extracting pulse wave characteristics based on the signals and judging the motion state and the body position state of the user in combination with data of the inertial measurement unit so as to realize pulse-by-pulse blood pressure estimation and cardiovascular pressure trend analysis; the posture self-adaption module identifies user postures through deep learning and dynamically selects a compensation algorithm according to different postures, so that the signal accuracy is improved. According to the invention, continuous, non-invasive and dynamic monitoring of cardiovascular pressure is realized, and long-term and convenient health state monitoring and risk early warning service can be provided for users.

The invention is suitable for the technical field of intelligent wearable devices, and provides a blood pressure dynamic calibration method and device of an intelligent wearable device and the wearable device. According to the blood pressure dynamic calibration method of the intelligent wearable device, the dual-wavelength pulse wave signal collected by the dual-wavelength PPG sensor is obtained, differential processing is conducted on the dual-wavelength pulse wave signal, the purified pulse wave signal is obtained, the signal-to-noise ratio of the purified pulse wave signal is calculated, and when the signal-to-noise ratio is higher than the threshold value, the blood pressure dynamic calibration of the intelligent wearable device is completed. Extracting a pulse wave characteristic value from the purified pulse wave signal, iterating the vascular elasticity coefficient according to the pulse wave characteristic value to obtain an iterative vascular elasticity coefficient, and outputting a first calibrated blood pressure value according to the pulse wave characteristic value and the iterative vascular elasticity coefficient. According to the embodiment of the invention, motion artifacts are effectively suppressed through differential processing of the dual-wavelength PPG sensor, the signal-to-noise ratio of signals is increased, and the problem that the blood pressure measurement error is large due to noise interference and blood vessel elastic fixation when a traditional intelligent wearable device is in a motion state is solved.

The invention relates to an imaging type non-contact gating signal obtaining method for a magnetic resonance system, and belongs to the field of physiological signal detection.The method comprises the steps that pulse and respiration signals of a magnetic resonance detection patient are collected through a camera, and face information of the patient is obtained through a reflector in a magnetic resonance instrument cavity; a living body skin detection technology is utilized to efficiently select a region of interest of the face of a patient, and an IPPG technology is combined to process a video image to obtain a curve of image gray along a time sequence; a motion detection algorithm is used for tracking micro motion of the chest, and stable and accurate breathing signals are extracted by combining frequency selection. By analyzing and quantifying pulse wave signals and delay of respiration signals and electrocardiosignals, the purpose of replacing electrocardiosignal R-peak gating triggering with pulse wave and respiration signal feature triggering is achieved. The invention provides an imaging type non-contact gating technology to solve the problems that traditional ECG and other contact gating technologies are easily affected by a strong static magnetic field, an alternating gradient magnetic field and a radio-frequency pulse electromagnetic field in a magnetic resonance imaging system, and motion artifacts can be generated on motion of fluid such as human blood, breathing and cardiac contraction. Therefore, the quality of a magnetic resonance imaging image is improved.

The invention relates to the technical field of medical monitoring, and discloses a portable blood oxygen signal OSA intelligent detection method which comprises the following steps: acquiring a pulse wave signal through a blood oxygen sensor, generating an amplitude difference sequence and a period difference sequence in parallel, analyzing rhythm synchronism of the amplitude difference sequence and the period difference sequence, and judging a respiratory effort event when a real-time relation index deviates from a reference value. By capturing a pulse wave time sequence-amplitude decoupling phenomenon, direct perception of a specific mode process of an invalid respiratory effort signal is realized, a traditional hysteretic monitoring mode depending on blood oxygen decline is avoided, and meanwhile, the system has the internal capability of distinguishing physiological fluctuation and specific mode struggling of the signal, and is convenient to use. And a brand new solution is provided for early screening of light and moderate OSA.

The invention belongs to the technical field of physiological state estimation, and discloses a blood oxygen estimation method based on combination of successive variational mode decomposition and adaptive filtering, which comprises the following steps of: removing high-frequency noise and baseline drift from a collected pulse wave signal; performing successive variational mode decomposition and adaptive filtering on the pulse wave signal to remove motion artifacts; accurate peak values and valley values are extracted according to the denoised pulse waves; the R value is calculated according to the denoised pulse wave and the extracted peak value and valley value, the blood oxygen saturation degree is solved according to the R value, and the R value refers to the absorbance ratio of the red light and the infrared light in the blood. According to the blood oxygen estimation method based on the combination of the successive variational mode decomposition and the self-adaptive filtering, the effect of removing motion artifact noise of photoelectric volume pulse wave signals is achieved, multi-peak-value joint detection is used, peak values and valley values are accurately extracted, and therefore the R value is determined, the blood oxygen saturation degree is obtained, and the accuracy of blood oxygen estimation is improved. And the precision of estimating the blood oxygen saturation is greatly improved.

The invention relates to a symbol structure configuration method and device of a sensing frame, communication equipment, a storage medium and a computer program product. The method comprises the following steps: determining initial symbol configuration data based on expected sensing overhead and expected sensing distance of a target scene, the total number of symbols of a sensing frame and single symbol duration of the sensing frame; target symbol configuration data of the sensing frame is determined based on the performance index requirement of the target scene and the initial symbol configuration data, the performance index at least comprises the expected sensing speed of the target scene, and the sensing frame is at least used for sending one or more of pulse waves and continuous waves. By adopting the method, various requirements on sensing distance and sensing precision in different scenes can be met by flexibly configuring the symbol structure of the sensing frame, the sensing performance of the sensing frame in different scenes can be effectively improved, and popularization and implementation of a communication sensing integrated scheme based on a cellular network are facilitated.

The invention discloses an auxiliary stroke rehabilitation method and device based on a multi-source physiological signal wearable device. The method comprises the steps that the pulse waveform variation rate and sublingual vein feature parameters of a subject are monitored through a self-powered five-hidden monitoring wrist strap; dynamically evaluating the internal organ imbalance index of the subject according to the pulse waveform variation rate and the sublingual vein characteristic parameters; generating personalized rehabilitation adjustment parameters according to the internal organ imbalance index of the subject; synchronously acquiring an electroencephalogram signal of a subject and a surface electromyogram signal of a target muscle, and generating an acupoint activation time sequence map of preset hand actions; the acupoint activation time sequence map is optimized according to the personalized rehabilitation adjustment parameters so as to adjust acupoint selection, stimulation intensity and stimulation time sequence; and presenting a built-in foot yangming stomach meridian virtual acupuncture scene to the subject through the VR guide instrument. According to the apoplexy rehabilitation method, electroencephalogram and myoelectricity cooperative decoding, personalized parameter adjustment, VR and a viscera imbalance traditional Chinese medicine diagnosis method are integrated into a rehabilitation scheme, and the apoplexy rehabilitation effect is improved.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

The invention provides an iPPG blood pressure detection method based on deep learning. According to the method, a transfer learning strategy is adopted, and the problem that deep learning training cannot be completed due to scarcity of iPPG data is solved. According to the method, the model is pre-trained by using existing PPG data with abundant types and sufficient data volume, so that the model preliminarily understands the implicit relationship between the blood volume pulse wave and the blood pressure, and then the pre-trained model is finely adjusted by using self-collected iPPG data, so that the model is more adaptive to an iPPG blood pressure detection task. Before the data are input into the model, the data are classified according to a volume pulse wave waveform characteristic quantity K value and divided blood pressure intervals, and each interval is independently trained to adapt to different types of blood pressure detection. During actual deployment and use, a personalized model is automatically allocated to carry out subsequent blood pressure detection on a subject by taking a true value measured for the first time of the subject as a benchmark.

The invention discloses a non-contact blood pressure prediction method based on multi-modal fusion. The method comprises the following steps: firstly, carrying out preprocessing such as space-time alignment and face region cutting on visible light video and infrared video sequences; then, obtaining multi-scale and multi-channel depth feature representations from the visible light image and the infrared light image through an independent feature extraction module, and generating adaptive weights for two modals in a space-channel dimension by using a dynamic weighted fusion mechanism based on L1 / L2 mixed norm evaluation and Softmax normalization combination; fusing into a uniform feature vector according to a complementary principle; finally, fitting and reconstruction of rPPG signals are carried out based on the fusion features, the reconstructed pulse wave signals are input into a blood pressure prediction network at the tail end, and high-precision non-contact estimation of systolic pressure and diastolic pressure is achieved. According to the method, visible light and infrared light videos are cooperatively analyzed, and the problem that an existing face-based video method is sensitive to illumination conditions is successfully solved.

The invention discloses a pulse wave monitoring method and system based on telemetry photoplethysmography, and the method comprises the steps: collecting a video file of a carotid artery region based on telemetry photoplethysmography, obtaining a plurality of frames of images in the video file, and determining a gray level image corresponding to each frame of image; acquiring two regions of interest in the grayscale image, and extracting two original pulse wave signals from the two regions of interest; performing zero phase shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals; performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain a time difference between the two pulse wave signals; according to the time difference of the two pulse wave signals and the distance between the two interested areas, the pulse wave speed is calculated, and the arteriosclerosis degree is evaluated based on the pulse wave speed. The carotid artery pulse wave is monitored in a non-invasive mode, arteriosclerosis is evaluated, and cardiovascular health is guaranteed.

The invention provides a blood pressure measurement model training method and a pulse blood pressure measurement system, a pulse wave measurement module obtains a pulse wave signal, and pulse waves of the wrist of a user act on pressure of a pressurization module to obtain a pressure change signal; the signal denoising module is used for denoising the pulse wave signals and the pressure change signals and transmitting the signals to the blood pressure measurement module, and the blood pressure measurement module executes a blood pressure measurement model training method; an initial neural network comprising a time sequence prediction network, a feature extraction network and a decoding network is trained through a pulse wave signal data set and a pressure change signal data set, the time sequence prediction network and the feature extraction network output personal blood vessel features and high-dimensional pulse features respectively, and a blood pressure data prediction result is output through the decoding network. And minimizing the deviation between a blood pressure data prediction result and an actual result, performing parameter iteration updating on the initial neural network model to obtain a blood pressure measurement model, and obtaining blood pressure data through the blood pressure measurement model to realize continuous measurement of blood pressure.

Method and apparatus for directing pulsed light beams at an in vivo treatment site to create thermoelastic expansion of tissue which generates ultrasonic pulse waves in the mega hertz range. One or more photosensitizers are applied to the treatment site. Further, the ultrasonic pulse waves either singly, or in combination with the pulsed light waves cause the activation of the photosensitizers to generate singlet oxygen at the treatment site.

The invention discloses a non-invasive wearable vascular arteriosclerosis assessment system and method based on multi-mode pulse wave signal fusion, and belongs to the field of vascular function assessment. The system comprises a distributed acquisition front end, a butterfly-shaped flexible substrate, an integrated double-accelerometer, a four-wavelength LED (530nm / 660nm superficial blood vessel measurement), a 810nm / 940nm deep artery measurement device, a double-photodiode, a double-impedance electrode and a pressure sensor. The central integrated control end processes a multi-mode signal and realizes pressure-LED current closed-loop control; the upper computer terminal extracts the multi-modal pulse wave time difference of the limbs and the neck, the local and regional pulse wave conduction velocity and the blood oxygen hemoglobin change parameters induced by the standardized posture, the dimension is reduced to 30 through principal component analysis, then the parameters are input into the random forest regression model, and the vascular elasticity index is output. Signal stability is improved through flexible fitting, single-source limitation is solved through multi-modal fusion, signal quality is guaranteed through closed-loop control, sensitivity is enhanced through dynamic physiological excitation, and accuracy and universality of arteriosclerosis assessment are improved.

The invention provides a blood pressure measuring method, wearable equipment and a storage medium. The method is applied to the wearable equipment, the wearable equipment comprises a blood pressure measuring device, and the blood pressure measuring device comprises an air bag. In the first time period, the wearable device inflates the air bag and obtains a first pulse wave signal, the air pressure in the air bag is gradually increased along with time in the first time period, and the first pulse wave signal is used for obtaining a first blood pressure measurement value; in a second time period, the wearable device inflates the air bag and obtains a second pulse wave signal, the air pressure in the air bag is kept unchanged or fluctuates in the first range in the second time period, and the second pulse wave signal is used for evaluating the cardiovascular risk; the wearable device outputs a first blood pressure measurement and cardiovascular risk. By means of the method, the wearable device can monitor the cardiovascular disease risk of the user while obtaining the peripheral arterial blood pressure value, and cardiovascular diseases are prevented.

The invention discloses a pressure adjusting method and system for a pressure air gun for drying, and relates to the technical field of pressure air gun adjusting.The method comprises the steps that pressure distribution data and drying effect parameters of the surface of a dried object are collected in real time; the drying effect parameters are input into a reinforcement learning and quantum annealing collaborative decision model, a dynamic pressure adjusting strategy is generated, and the dynamic pressure adjusting strategy comprises control parameters of a pressure value, a waveform frequency and a nozzle direction; an air gun is controlled to output a target pressure waveform according to the dynamic pressure adjusting strategy, the target pressure waveform is generated based on the 3D geometrical characteristics of the drying object, and the impedance of an internal flow channel of the air gun is adjusted in real time through a magnetorheological fluid strategy; in the drying process, three-section mode switching is triggered according to the real-time water content and surface roughness data, chaos frequency modulation pulse waves are output in the initial stage to peel off water, fractal diffusion waves are switched in the middle stage to achieve uniform coverage, and nanometer resonance waves are adopted in the final stage to complete drying.

An image capturing device includes: an image capturing unit configured to capture a 4K or higher resolution moving image; and a selector switch configured to switch an image capturing mode in which the image capturing unit captures the moving image from a first image capturing mode to a second image capturing mode, the second image capturing mode providing a higher resolution in detecting an amount of change in pulse wave information representing a pulse wave of a living body in the captured moving image than does the first image capturing mode.

The invention discloses a method and device for predicting average arterial pressure based on a full-connection neural network, and relates to the technical field of biomedicine based on artificial intelligence. The method comprises the steps that blood pressure pulse wave signals of each patient are collected, and S-G filtering processing is conducted on the blood pressure pulse wave signals; constructing a blood pressure pulse wave envelope line through a double-Gaussian fitting algorithm, and estimating the average arterial pressure of each patient according to the blood pressure pulse wave envelope line; respectively calculating relevancy between the multiple human body physiological parameters of each patient and the average arterial pressure; the human physiological parameter variables with the relevancy larger than a preset value serve as the input quantity of a full-connection neural network, the predicted value of the average arterial pressure serves as the output quantity of the full-connection neural network, a training sample set and a verification sample set are constructed, and the full-connection neural network is trained; and predicting the average arterial pressure of the patient by using the trained full-connection neural network. The accuracy of the non-invasive blood pressure measuring equipment can be improved.

The invention relates to a non-invasive cardiac output function detection method and device, and belongs to the technical field of biological measurement. According to the method, on the basis of pulse wave related data, electrocardiowave related data and biological impedance and biological reactance related data which are obtained through detection, the bioelectrical impedance method is adopted to calculate and obtain the cardiac output of the bioelectrical impedance method, and the pulse wave method is adopted to calculate and obtain the cardiac output of the pulse wave method. And according to the impedance signal confidence coefficient and the pulse wave signal confidence coefficient, carrying out weighted average on the cardiac output of the bioelectrical impedance method and the cardiac output of the pulse wave method, and taking the weighted average as a cardiac output detection result. The detection device comprises a pulse wave detection system, an electrocardiogram detection system, a biological impedance and reactance detection system and a host used for data processing and result display. The reliability of cardiac output detection is remarkably improved while portability is guaranteed, and accurate, continuous and portable detection of non-invasive cardiac output is achieved.

The invention relates to the technical field of physiological signal detection. The invention discloses a physiological signal detection method and system oriented to magnetic resonance scanning and a storage medium. The method and system can achieve accurate detection of multi-dimensional physiological parameters in a high-intensity magnetic field environment. The physiological signal detection method for magnetic resonance scanning comprises the following steps: in the process of scanning a patient by magnetic resonance equipment of the physiological signal detection system for magnetic resonance scanning, acquiring a multispectral pulse wave signal of the face of the patient by adopting a time sequence light splitting method; when the multispectral pulse wave signals are obtained, heart vibration signals of the chest of the patient are obtained through a vibration measurement method; preprocessing the multispectral pulse wave signal to obtain a first target signal; preprocessing the heart vibration signal to obtain a second target signal; extracting a physiological signal with a plurality of physiological parameters based on the first target signal and the second target signal; the physiological signals are fed back to medical staff.