713results about "Stethoscope" patented technology

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

The invention discloses a borborygmus detection system and method based on multi-sensor fusion and deep learning, and the method comprises the following steps: S1, constructing a multi-mode sensor array for collecting abdominal acoustics, vibration and motion signals; s2, adaptive filtering, noise suppression and data synchronization preprocessing are carried out on the collected signals; s3, performing multi-sensor data fusion and voting on the filtered signal; s4, constructing a deep learning classification model; according to the method, acoustics, vibration and motion signals are synchronously collected based on a multi-mode sensor, abnormal data are eliminated through a voting algorithm, a fusion result is optimized in combination with Kalman filtering, filter parameters can be dynamically adjusted according to priori knowledge of borborygmus, key frequency bands are reserved, and characteristics of different borborygmus types are extracted in a targeted mode. The classification accuracy is improved by more than 15%, high-precision classification is realized, and F1-score of normal / hyperfunction / weakening sound on a test set reaches 92.5%.

The embodiment of the invention provides a real-time pulmonary ventilation impedance monitoring method and system based on multi-modal data fusion, and the method comprises the steps: obtaining real-time multi-modal data from a plurality of sensors, the multi-modal data comprising a sound signal, an airflow signal and a pressure signal; performing fusion processing on the multi-modal data to generate comprehensive pulmonary ventilation impedance data; based on the comprehensive pulmonary ventilation impedance data, analyzing data distribution characteristics to detect anomalies; and when abnormal data distribution characteristics are detected, a fault early warning system is triggered to solve burst signal distortion. Through the scheme of the embodiment of the invention, the problem of how to carry out fault early warning on the monitoring system according to the abnormal data distribution characteristics so as to solve the problem of burst signal distortion can be solved.

The invention relates to the technical field of man-machine collaboration and information processing, discloses an AI processing-based motion capture recognition pre-judgment system, and aims to solve the problems of difficulty in physiological signal monitoring and performance drifting of a myoelectricity intention recognition model in a high-noise environment, and the AI processing-based motion capture recognition pre-judgment system comprises a processing module, a visual capture module, a myoelectricity acquisition module, a bioacoustics acquisition module and an information prompting module, according to the method, myoelectricity and visual information decoding operation intentions are fused, a self-adaptive filter is guided through the intentions, accurate noise suppression is carried out on biological acoustic signals of a target object, and therefore the physiological state risk of the target object is accurately evaluated, meanwhile, an online self-adaptive calibration mechanism is introduced, the actual action of visual recognition serves as a supervision true value, and the accuracy of the physiological state risk of the target object is improved. And the myoelectricity model is continuously calibrated, so that the accuracy of long-term identification is ensured. According to the method, the decoding intention and the physiological risk level are integrated, the composite cooperation instruction is intelligently generated and fed back to the operator, the situation awareness ability of the operator is enhanced, and the safety and efficiency of man-machine cooperation are improved.

The invention relates to the technical field of medical equipment, in particular to a cardiopulmonary sound and electrocardiogram rapid screening system suitable for emergency treatment scenes. The system comprises a heart and lung sound acquisition unit, an electrocardiogram acquisition unit and an intelligent analysis unit. And the intelligent analysis unit realizes signal synchronization by establishing a cross-modal time sequence alignment model, adaptively matches the physiological state by adopting a dynamic time window adjustment mechanism, and introduces multiple rounds of verification processes to ensure the result reliability. The system can automatically complete synchronous collection, feature extraction and fusion analysis of the heart and lung sound and the electrocardiosignals, the diagnosis accuracy is remarkably improved while the screening speed is guaranteed, and reliable technical support is provided for rapid screening of heart and lung diseases in the emergency department.

The invention discloses a child mouth breathing monitoring method, device and equipment and a medium, and the method comprises the steps that a non-contact sensor group is used for synchronously collecting multi-mode physiological data of a target child in the sleep period, and the multi-mode physiological data at least comprises a face thermal imaging video stream, a thoracic and abdominal micro-motion signal and an environment audio signal; the multi-modal physiological data is processed, multi-modal features related to respiration are extracted respectively, and the multi-modal features comprise thermodynamic change features of mouth and nose areas extracted based on a face thermal imaging video stream, respiratory effort waveforms extracted based on thoracic and abdominal micro-motion signals, and the respiratory effort waveforms extracted based on the thoracic and abdominal micro-motion signals; extracting a sound source spatial position and a spectrum feature of breathing sound based on the environment audio signal; inputting the multi-modal features into a pre-trained mouth breathing recognition model, and outputting a judgment result of the functional mouth breathing event of the target child within a specific time period; and generating a mouth breathing monitoring report of the target child based on the judgment result.

The invention discloses a heart sound, electrocardio and echocardiogram multi-modal data phase synchronization method, which belongs to the technical field of medical image processing, and comprises the following steps: A1, obtaining an original echocardiogram video and independently recorded heart sound audio data information; a2, preprocessing the acquired data and performing sample automatic construction; a3, a double-path neural network architecture is constructed, electrocardiogram waveform extraction is carried out, and a digital electrocardiogram signal sequence is obtained; a4, periodic feature points of the heart sound and the electrocardio are recognized based on the digital electrocardio signals; and A5, aligning the asynchronous data based on linear phase resampling. According to the method, the video frames, the electric signals and the heart sound signals under different sampling rates are forcibly mapped into the unified phase coordinate system through a linear interpolation algorithm, absolute synchronization of the video frames, the electric signals and the heart sound signals in cardiac pulsation physiological logic is finally achieved, and the influence of individual heart rate differences on data alignment is eliminated.

The invention relates to the technical field of audio signal processing and biomedical signal analysis, and still has a further optimized space for the recognition of anti-noise requirements, signal individual differences and complex pathological modes in a noise environment. The invention provides a double-path CNN heart sound classification method based on time-frequency and double-spectrum fusion features, and the method comprises the steps: carrying out the preprocessing of an original heart sound signal of a data set which is classified into a normal heart sound and an abnormal heart sound, and obtaining a to-be-recognized heart sound signal; based on dynamic continuous wavelet transform, adaptively selecting parameters to extract time-frequency characteristics, introducing bispectrum analysis, capturing nonlinear characteristics, generating a dual-channel characteristic pattern, and efficiently storing the dual-channel characteristic pattern in an HDF5 format; and based on a designed double-path convolutional neural network structure, respectively processing the extracted time-frequency and double-spectrum features, performing classification after fusion, and training a model in combination with category weighted loss and an optimization strategy to obtain a heart sound classification result. The heart sound recognition accuracy can be improved.

The invention discloses a breathing and electrocardio information integrated recording and displaying method and system based on overall spatial-temporal characteristics. The method comprises the steps that a high-precision sensor synchronously collects electrocardiogram, phonocardiogram, arterial pulse waveform, venous pulse waveform and breathing wave signals and preprocesses the signals based on different signal characteristics; carrying out multi-cycle electrocardiogram merging by utilizing an R-wave detection algorithm and introducing a variational mode decomposition algorithm; carrying out feature extraction from the multi-mode signal, and carrying out respiratory wave and electrocardiowave conjoint analysis; carrying out feature fusion through a spatial-temporal feature fusion network of a multi-branch attention mechanism, and generating and displaying a combined electrocardiograph distribution diagram; carrying out anomaly detection through an anomaly detection model based on a Transform architecture; synchronously displaying in an integrated display interface; according to the system and the method, efficient acquisition, analysis and visualization of the multi-modal physiological signals are realized by integrating synchronous acquisition, intelligent interpretation and integrated display of various physiological signals, and the efficiency and the accuracy of clinical diagnosis are favorably improved.

The invention particularly relates to a respiratory disease diagnosis device and method, and the device comprises a respiratory sound signal collection unit which is used for obtaining a current respiratory sound signal; the breathing sound signal preprocessing unit is used for performing segmentation processing and denoising processing on the current breathing sound signal to obtain a preprocessed breathing sound signal; the feature extraction unit is used for extracting multi-modal features of the preprocessed breath sound signals; and the detection unit is used for inputting the multi-modal features into a preset deep learning network model to obtain a respiratory system disease detection result. Therefore, through multi-modal feature extraction, dynamic fusion and an efficient classification strategy, the problems of relatively low feature identification degree, relatively low disease classification accuracy and the like in the process of researching disease diagnosis based on the breath sound signals in related technologies are solved, and the detection performance of chronic respiratory system diseases based on the breath sound signals is remarkably improved.

The invention discloses a portable detection system for early diagnosis of orthopaedic joint diseases, and relates to the technical field of orthopaedic joint disease diagnosis, which comprises the following steps: acquiring acoustic and motion feature vectors of joint activities, calculating a quality score based on an acoustic signal-to-noise ratio and a motion goodness of fit, performing weighted splicing on features by taking the quality score as a weight after normalization, generating a fusion feature vector; thirdly, reconstructing features by using a reverse decoding model, comparing the same degree with the original features, weighting to obtain fusion same degree, and iteratively adjusting the weight to optimize the fusion effect; and performing clustering analysis on the candidate feature vectors, replacing the candidate feature vectors if the candidate feature vectors deviate from a clustering center, and finally inputting a health model to output a diagnosis report. Through dynamic weighting of quality scores, high-fidelity features are processed preferentially, the information density and discrimination capability are improved, and low-quality data interference is reduced; and self-checking and dynamic correction are realized through reconstruction and similarity comparison, so that the fidelity and consistency of the characteristics are ensured, and the robustness of the system is enhanced.

The invention relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and respiration monitoring system which comprises a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded into the laryngeal mask main body and comprises an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates and couples an original signal, combines physical parameters of the coating and inputs the original signal into a multi-parameter coupling correction model, and decouples independent and accurate multiple parameters; and the target thickness of the coating is determined through acoustic correction error minimization and comprehensive calculation that the sensitivity of each environmental parameter reaches the standard. The system can generate an abnormal ventilation alarm and feed back to control the breathing machine. Multi-parameter synchronous monitoring can be achieved, coating interference is eliminated, and breathing management safety and practicability are improved. In the actual application process, the abnormal breathing function caused by abnormal breathing machine state or abnormal laryngeal mask position of a user can be found in time, the purpose of early discovery and early intervention can be achieved, and malignant events are avoided.

A system includes one or more sensors to detect activities of a mobile object; and a processor coupled to the sensor and the wireless transceiver to classify sequences of motions into groups of similar postures each represented by a model and to apply the models to identify an activity of the object.

The present description relates generally to methods and systems for power management of a digital (e.g., electronic) stethoscope. In one example, an electronic stethoscope includes a chestpiece configured to be positioned on a patient, the chestpiece including a sensor, wherein the chestpiece further includes a computer processing unit (CPU) operatively coupled to a memory storing instructions that, when executed by the CPU, cause the CPU to automatically activate the electronic stethoscope in response to detecting a touch input via the sensor.

The invention provides an MEMS heart sound and electrocardio detection system, method and equipment, and relates to the technical field of medical instruments, the MEMS heart sound and electrocardio detection system comprises a bionic cilium heart sound sensor, an electrocardio acquisition circuit and a signal fusion model; the bionic cilia heart sound sensor comprises hollow bullet type bionic cilia and a cantilever beam microstructure; the hollow bullet type bionic cilia and the cantilever beam microstructure are mechanically coupled, and the cantilever beam microstructure comprises a Wheatstone bridge formed by arranging a plurality of piezoresistors and is used for collecting heart sound signals; the electrocardio acquisition circuit and the bionic cilium heart sound sensor are packaged in an acoustic impedance matching mode, and the electrocardio acquisition circuit comprises an electrocardio acquisition electrode plate, a power supply voltage stabilization module, an instrument amplification module, a band-pass filtering module and a notch filter and is used for acquiring and conditioning electrocardio signals; the signal fusion model comprises a Resnet18 feature extractor, an encoder and a mutual cross attention mechanism, and is used for performing feature extraction, linear transformation and feature deep fusion on the collected electrocardiosignals and electrocardiosignals and outputting a signal classification result.

The invention provides a fetal hypoxia early risk identification method and system based on a large pulse pressure phenotype, and relates to the technical field of biomedical signal processing and heart sound signal analyse.The method comprises the steps that fetal heart sound signals and body surface vibration signals at the abdominal wall of a pregnant woman are collected through a multi-channel sensing array; a preprocessing means is adopted to improve the signal-to-noise ratio of a target signal and suppress an interference signal; in the sliding time window, a large pulse pressure phenotypic feature set and heart sound time phase parameters are extracted; based on the combined criterion, fetal hypoxia risk judgment is carried out; and outputting the graded early warning information, and supporting the playback of the risk event fragment. According to the method, fetal real heart sound mechanical vibration serves as an information source, hypoxia early warning is achieved based on a large pulse pressure phenotype, individualized base lines (including day and night layering) and gestational week self-adaptive correction are supported, the anti-interference capacity is high, the 7 * 24-hour continuous monitoring requirement in a hospital / family can be met, and the sensitivity and specificity of hypoxia recognition are improved.

The utility model relates to a sputum excretion vest which comprises a vest body, a sputum excretion mechanism and a control host. The inner side of the vest body is covered with a plurality of air bags communicated with one another, one air bag is connected with an inflation connector, and the inflation connector is externally connected with inflation equipment; the sputum excretion mechanism comprises a front plate, a rear plate and two side plates, the front plate and the rear plate are detachably connected, the two side plates are connected to the two sides of the front plate and the two sides of the rear plate respectively, a plurality of ultrasonic probes are arranged on the inner sides of the front plate and the two side plates in an array mode respectively, and a plurality of vibration modules are arranged on the inner sides of the front plate, the rear plate and the two side plates in an array mode respectively. After a patient wears the vest body, the air bag is inflated through inflation equipment, the ultrasonic probes and the vibration modules are attached to the body of the patient, then the multiple ultrasonic probes are started to detect different parts of the lungs of the patient, and when the area where a certain ultrasonic probe is located detects secreta in the lungs, the vibration intensity of the vibration modules in the corresponding area is adjusted; the vibration sputum excretion treatment is more accurate, and the sputum excretion efficiency is higher.

The invention provides a multi-modal data fusion intelligent children asthma prediction system, which comprises a data acquisition and preprocessing unit, a feature extraction unit, a data fusion and intelligent prediction unit and a decision and feedback unit, and is characterized in that the data acquisition and preprocessing unit is used for acquiring multi-modal physiological signal data; the feature extraction unit is used for extracting features of the multi-modal physiological signals; the data fusion and intelligent identification unit is used for carrying out feature fusion on the extracted multi-modal physiological signal data; the decision and feedback unit is used for comprehensive judgment and dynamic feedback adjustment; in the dynamic feedback and self-adaptive adjustment part, a correction coefficient and the weight of the model are adjusted according to real-time data, so that the prediction probability is more accurate. The system effectively overcomes the problem that the existing system depends on a single data source or medical staff depends on subjective evaluation.

The present invention relates to a system and method for acquiring and analyzing physiological data from a user. The system includes a plurality of interconnected devices, which may communicate sensor data to a personal mobile electronic device. Each interconnected device includes at least one sensor to acquire physiological data. In addition, at least one sensor is operably connected to the body of the user. Further, the interconnected biometric devices may be implanted medical devices and / or wearable electronic devices. The personal mobile electronic device is wirelessly connected to each of the plurality of interconnected biometric devices. In addition, the personal mobile electronic device is configured to receive and analyze physiological data acquired by each of the plurality of interconnected devices and to compute the difference between the values of the same physiological parameter measured at a different location of the user's body.

The invention discloses a non-invasive continuous dynamic blood pressure detector data analysis method, and relates to the technical field of cardiovascular and cerebrovascular diseases, skin bio-electricity signals, heart sound signals and micro-motion signals are collected, the signals collected in the step S1 are preprocessed and sampled and aligned, a deep neural network is utilized to extract time sequence features, a convolutional neural network is utilized to extract spatial features, and the time sequence features are extracted; according to the method, blood pressure related features are extracted through a multi-source signal acquisition mechanism in combination with a deep neural network and a convolutional neural network, the blood pressure related features are fused into a high-precision blood pressure prediction model, and continuous dynamic blood pressure prediction can be realized without depending on a cuff and an invasive sensor; the system is suitable for home health management, chronic disease follow-up visit and remote medical treatment, and has wide clinical application and industrialization potential.

The utility model relates to the technical field of medical diagnostic tools, in particular to a medical stethoscope, a stethoscope head is provided with a stethoscope end face, the stethoscope end face is used for abutting against and being attached to the skin surface of a human body, a marking inner shell is in sliding fit with the outer side wall of the stethoscope head, and the bottom end of the marking inner shell is coated with medical dye. The marking inner shell is in sliding fit with the inner wall of the shielding outer shell, the multiple connecting pieces are connected between the shielding outer shell and the auscultation head, and the elastic piece is configured to drive the marking inner shell to have the trend of moving towards the position away from the auscultation head all the time, so that the bottom end of the marking inner shell does not exceed the auscultation end face. In the auscultation process, when auscultation marking is needed, a doctor presses the marking inner shell to overcome the elastic force of the elastic pieces, so that the bottom end of the marking inner shell coated with medical dye extends out of the auscultation end face and makes contact with the skin surface to complete marking, and the convenience of auscultation marking is effectively improved; and the accuracy and repeatability of auscultation diagnosis are further improved.

The invention relates to the technical field of health monitoring, in particular to an electrocardiograph monitoring intelligent diagnosis system based on deep learning and multi-modal fusion technology, which is characterized in that a multi-modal synchronous sensing unit synchronously acquires ECG, PCG and PPG data streams with aligned timestamps, and an edge intelligent diagnosis unit operates a lightweight model with the parameter smaller than 1MB, so that the ECG monitoring intelligent diagnosis system is obtained. The cloud fusion analysis unit is used for dynamically fusing multi-source data by adopting a cross-modal attention mechanism to generate a ventricular fibrillation probability PVF, and the dynamic collaborative decision-making unit is used when R is equal to III or Cilt, and the cloud fusion analysis unit is used for dynamically fusing the multi-source data by adopting a cross-modal attention mechanism to generate a ventricular fibrillation probability PVF; deep diagnosis, PVFgt, is triggered within 5 seconds at 0.9; and when 0.95, a third-level audible and visual alarm is triggered, and the closed-loop optimization unit continuously optimizes the model by distilling and compressing the misdiagnosis sample. According to the intelligent diagnosis system for electrocardiograph monitoring, rapid edge diagnosis is achieved through a lightweight model, critical cases can respond rapidly through cloud cooperation, and the accuracy and timeliness of heart disease diagnosis are improved.

The invention discloses cross-scene reusable multi-modal physiological intelligent monitoring equipment and system. The equipment comprises a multi-modal physiological acquisition sensing array, an acquisition control module, a placement detection module, a self-adaptive parameter adjustment module and a signal processing module. The multi-modal physiological acquisition sensing array comprises an ambient light sensor, a photoelectric volume pulse wave sensor, a temperature sensor, a sensing electrode, a pressurizing electrode, a sound collector, an inertial measurement unit and the like; the placement detection module is used for calculating the comprehensive confidence coefficient of the current physiological electric signal belonging to each physiological signal type for each signal acquired by the multi-modal physiological acquisition sensing array, so as to judge the physiological signal type to which the physiological electric signal belongs; the adaptive parameter adjusting module is used for generating a sampling rate, an amplification factor, a filtering bandwidth and an ADC bit depth corresponding to the current physiological signal type; and the signal processing module is used for carrying out signal processing according to the parameters of the self-adaptive parameter adjusting module. Multi-modal physiological signal monitoring can be realized by adopting one set of equipment.

The invention provides a breath sound signal processing and evaluating method and system. The method comprises the following steps: firstly, collecting breath sound signals of a plurality of preset parts of the chest of a user through an electronic stethoscope, and collecting clinical symptom data through a user interface; a first feature set and a second feature set are then extracted from the breath sound signal, where the first feature set is extracted from six sites for preliminary analysis and the second feature set is extracted from four sites for in-depth analysis. And performing analysis based on the first feature set and the first machine learning model, and outputting a first indication value. Decision is made in combination with clinical symptom indication values, and deep analysis is started when any indication value reaches a threshold value. The deep analysis outputs a second indication value based on a second feature set and a second machine learning model. And finally, fusing a plurality of indication values to generate a comprehensive evaluation result.

The application discloses a diagnosis data processing method and device and a storage medium, relates to the technical field of data processing, and comprises the following steps: acquiring a heart-lung auscultation sound signal collected by a collection device and a traditional Chinese medicine (TCM) diagnosis standard description received based on an interactive interface; determining a western medicine quantitative diagnosis index according to the heart-lung auscultation sound signal, and correlating a TCM syndrome differentiation type according to the TCM diagnosis standard description and the western medicine quantitative diagnosis index; and generating and outputting a TCM medication recommendation scheme corresponding to a diagnosis result according to the western medicine quantitative diagnosis index and / or the TCM syndrome differentiation type. The application provides strong objective evidence and quantitative reference by differentiating the TCM syndrome differentiation type according to the auscultation sound. When diagnosing and treating diseases, the TCM highly depends on and integrates the auscultation result, and takes the auscultation result as one of key bases for differentiation, prescription selection, treatment effect evaluation and prognosis.

A health-device adapter for handheld electronic devices comprises a holder (20) with a base (22), longitudinally extending so as to define broad front wall (24) and back wall (26), and a gripping assembly (30, 32) to hold, in use, a handheld device, and connected to the base (22). The adapter further comprises a health-device support (50) connected to the holder (20) and suitable to support a health-device (100). The health-device support (50) is movably engaged to the gripping assembly of the holder (20).

The invention provides an active noise reduction stethoscope. The active noise reduction stethoscope comprises a sound receiving device, a sound processing device and a control unit, the sound receiving device is provided with a main microphone and an auxiliary microphone, the main microphone is configured to receive and output a first sound signal, and the auxiliary microphone is configured to receive and output a second sound signal. The sound processing device is coupled to the sound receiving device to receive the first sound signal and the second sound signal, and converts the first sound signal and the second sound signal into a third sound signal and a fourth sound signal in a digital mode respectively. The control unit is coupled to the sound processing device and calculates a correlation between the third sound signal and the fourth sound signal for determining whether to execute an active noise reduction operation.

The present disclosure relates to a heart sound signal processing method, device and wearable device, the method comprising: in the case that the signal quality of a first heart sound signal does not meet a preset requirement, continuously segmenting the first heart sound signal into a plurality of first sub-signals and continuously segmenting a second heart sound signal into a plurality of second sub-signals; determining a weight coefficient of the frequency domain signal corresponding to each frequency value of the first sub-signals and the second sub-signals according to the phase difference of the frequency domain signal corresponding to each frequency value; determining a fused frequency domain signal corresponding to each frequency value based on each window, according to the frequency domain signal corresponding to each frequency value of the first sub-signals and the second sub-signals and the weight coefficient of the frequency domain signal corresponding to each frequency value; determining a time domain signal of each sampling point based on each window, according to the fused frequency domain signal corresponding to each frequency value; and generating a reconstructed heart sound signal according to the time domain signal of each sampling point of each window.

The present invention provides a device (100) and method for measuring and / or monitoring auscultation and other biomedical signals of a body part of a subject, the device (100) comprising: a plurality of sonographic sensors sensitive to surface vibrations-PG sensors (110) configured to measure acoustic activity of the body part, and providing a PG signal (115) indicative of acoustic activity of the body part, the PG sensor (110) being adapted to be arranged on the body part to cover different independent auscultation areas of the body part. The device further comprises at least one other biomedical sensor-BIO sensor (120) configured to independently measure a respective activity of the body part according to the sensor type and to provide at least one BIO signal (125) indicative of the respective activity of the body part. The device further comprises a signal processing unit (160) configured to acquire a signal (115, 125) from each sensor (110, 120) and to collectively perform a signal source specific analysis by combining information of the PG signal (115) with corresponding arrangement information of the PG sensor (110) on the body part and information of the BIO signal (125).

A method and apparatus for predicting blood pressure, an electronic device, and a readable storage medium, relating to the technical field of medical treatment. The method for predicting blood pressure comprises: synchronously acquiring physiological signals, wherein the physiological signals comprise an electrocardiographic signal, a pulse signal, and a heart sound signal; performing feature extraction on the physiological signals to obtain signal features, wherein the signal features comprise an pre-ejection period duration and a pulse transmission time; and inputting the signal features into a pre-trained blood pressure prediction model, so that the blood pressure prediction model outputs a blood pressure prediction result, thereby improving the prediction accuracy of blood pressure.