192 results about "Photoplethysmogram" patented technology

The invention relates to a cardiovascular disease risk prediction system, which comprises a user terminal module, an edge computing module, a cloud intelligent module, an application service module and a system support module, wherein the user terminal module comprises an intelligent sensing equipment cluster unit and a mobile terminal data entry unit; and the intelligent sensing equipment cluster unit adopts a millimeter wave radar-PPG fusion sensor, monitors subcutaneous 0.5 mm depth arterial vibration through a 60GHz radar array, is aligned with a photoplethysmogram signal in a space-time manner, and performs user data acquisition based on a surface plasma resonance chip. According to the cardiovascular disease risk prediction method and system, real-time signal processing is realized by adopting JetsonAGX through a heterogeneous calculation architecture, medical image accelerated calculation is performed by using NVIDIAClara, non-out-of-domain joint modeling of data of hospitals is supported through federal learning, privacy protection reasoning based on homomorphic encryption is realized, and full-process closed-loop management is realized by adopting a layered modular architecture. And the accuracy of cardiovascular disease risk prediction is improved.

The invention relates to the technical field of blood pressure measurement, in particular to a cuff-free blood pressure continuous monitoring method based on ICG, PPG and ECG multi-mode physiological signals. According to the method, deep mining and fusion are carried out based on multi-modal physiological signals such as a chest impedance signal ICG, a photoelectric volume pulse wave signal PPG and an electrocardiosignal ECG of a tested object synchronously collected by multi-modal hemodynamics monitoring equipment, and a richer multi-modal feature matrix related to blood pressure changes is constructed; after deep learning fusion training is carried out on the constructed cuff-free blood pressure prediction model, prediction output of a cuff-free blood pressure prediction result can be directly carried out according to ICG, PPG and ECG multi-mode physiological signals of a to-be-detected tested object by means of the cuff-free blood pressure prediction model, the generalization ability and monitoring precision of continuous monitoring of the cuff-free blood pressure can be effectively improved, and the accuracy of continuous monitoring of the cuff-free blood pressure can be improved. The continuous, non-invasive and high-precision blood pressure monitoring requirements are met, and the clinical application value is more remarkable.

A photoplethysmogram system and method are disclosed. The system includes at least three light sources of different frequencies configured to emit light into a subject's skin. A PPG sensor detects the light from the multiple light sources after it has passed through the subject's skin and generates a PPG signal for each light source. A processor, communicatively coupled to the PPG sensor, receives the PPG signals and estimates physiological parameters from each PPG signal. The light sources may include a red light source, an infrared light source, and a green light source, with the green light source used for calibration in the measurement of blood oxygen saturation. The processor may dynamically perform amputation or imputation of the PPG signals based on the consistency of the signals from the different light sources. The physiological parameters may include respiratory effort and pulse rate and pulse rate variability.

Methods, systems, and devices for determining a sleep age metric of a user are described. The system may receive photoplethysmogram (PPG) data throughout a time interval including a plurality of sleep intervals during which the user is asleep, input the PPG data into a first machine learning model, and classify the PPG data into a plurality of sleep stages. The system may input one or more sleep features into a second machine learning model, and the second machine learning model may output a sleep age metric associated with a sleep health of the user relative to a chronological age of the user. In some cases, the system may transmit, to a user device, an instruction to cause a graphical user interface (GUI) to display an indication of the sleep age metric and an insight for a value of the sleep age metric.

The invention relates to the technical field of vital sign measurement, in particular to a wearable underwater vital sign and environment monitoring system, which comprises a physiological signal acquisition module, a photoelectric volume pulse wave sensor, a bioelectrical impedance electrode, a piezoelectric sensor, a signal processing module, a signal processing module and a signal processing module, a wrist photoelectric volume pulse wave signal, a chest bioelectrical impedance signal and a neck piezoelectric signal are collected. According to the method, signal reconstruction is carried out based on energy proportion calculation, an electrocardiogram R wave peak value point position and an R-R interval time sequence are extracted, variability indexes are generated through frequency domain decomposition and frequency band energy statistics, time domain and frequency domain index values are calculated, weight distribution is carried out, and therefore comprehensive monitoring on the underwater physical sign state is achieved. The signal acquisition precision is improved by means of a multi-source signal acquisition mode, noise interference is eliminated through signal track matrix decomposition operation, and the signal reconstruction quality is ensured through feature vector energy proportion calculation.

The invention belongs to the technical field of computer vision, and discloses a human face video blood pressure prediction method based on multi-scale attention constraint, which comprises the following steps: step 1, extracting a remote photoplethysmography (rPPG) signal; firstly, a face is detected in a video through an MTCNN face detection algorithm, a region of interest (ROI) is divided, an effective face region is extracted, then an rPPG signal carrying noise is preliminarily extracted in combination with optical flow estimation and median filtering, in a blood pressure prediction part, baseline drift removal, secondary filtering and other processing are carried out on the rPPG signal to obtain a relatively clean signal, and then the effective face region is extracted. Performing window segmentation on the rPPG signal; and 2, designing an EBP-Net model, and inputting the rPPG signal into the EBP-Net model to predict systolic pressure and diastolic pressure. According to the invention, the types of signals required by blood pressure measurement are simplified, only rPPG signals are required, the measurement error is minimum, and the cost of signal acquisition is reduced.

A multi-sensor remote patient monitoring platform combines multiple technologies on a patient's own device to dramatically reduce the number of devices a patient needs while also reducing device distribution logistics on health care providers. Continuous temperature monitors can be used to collect frequent temperature data over large periods of time. Additionally, a rPPG device can obtain at least one vital sign data by a remote photoplethysmogram (rPPG) comprising a plethysmogram that is optically obtained via a camera by detection of blood volume changes in the microvascular bed of tissue of the patient. The temperature data and vital sign data can be input into a remote server-side computer system that can analyze the received data for indication of a fever, and provide an alert to the patient or a clinician when the result of the analysis indicates the fever. Such alerts can be clinically beneficial for disease states in which fever can be fatal, particularly for those fevers having fast onset.

A wearable electronic device and method for the same are disclosed. The wearable device includes a memory, housing, photoplethysmogram (PPG) sensor, display and processor. The processor implements the method, including: receiving a PPG signal via the PPG sensor, detecting blood pressure values for a plurality of consecutive time periods based on characteristic information detected from the received PPG signal, in response to detecting termination of an exercise event, generating an exercise parameter determination model based on blood pressure characteristic information generated from the detected blood pressure values after the detected termination, and control the display to display information generated from the exercise parameter determination model.

Methods, systems, and devices for cardiovascular health metric determination are described. A system may be configured to receive a photoplethysmogram (PPG) signal representative of a pulse waveform for a user. The pulse waveform may include a first local maximum, a downward slope following the first local maximum, and a curved feature representative of a transition from a systolic phase to a diastolic phase of a cardiac cycle. Additionally, the system may extract one or more morphological features from the pulse waveform and compare the one or more morphological features with one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages. The system may determine a cardiovascular health metric that indicates a cardiovascular health of the user relative to a chronological age of the user and cause a graphical user interface to display an indication of the cardiovascular health metric.

The invention discloses a real-time multi-mode physiological signal analysis method for myocardial ischemia, and belongs to the technical field of data analys.The technical scheme includes that electrocardiogram, photoelectric volume pulse waves and thoracic impedance signals are synchronously acquired, and phase difference caused by motion displacement is compensated in real time by the aid of a triaxial accelerometer; constructing a multi-dimensional quality evaluation matrix containing an electrocardiogram signal-to-noise ratio, a photoelectric volume pulse wave perfusion index and a chest impedance variance, and jointly judging whether signal reconstruction is triggered or not based on a dynamic threshold value and accelerometer data; a multi-channel signal block which is judged to be low in quality through the quality evaluation matrix and comprises the electrocardiogram and photoelectric volume pulse waves and chest impedance synchronized with the electrocardiogram is input into a generative adversarial network with electrophysiological constraints to be reconstructed, and a reconstructed high-quality electrocardiogram signal is output; and myocardial ischemia key indexes are extracted from the reconstructed signals, and multi-level risk early warning is carried out. The real-time multi-modal physiological signal analysis method for myocardial ischemia has the beneficial effect that the real-time multi-modal physiological signal analysis method for myocardial ischemia is provided.

Systems and methods are provided for non-invasively measuring one or more hemodynamic variables using a peripheral arterial region wearable device having a strain sensor and a photoplethysmogram sensor. Different line fit profiles are determined from device collected signals indicative of the strain response versus externally applied cuff pressure and the blood volume and / or the blood circulation changes versus externally applied cuff pressure. From these line fit profiles, external cuff pressure affected line fit features are determined, from which hemodynamic variables for the peripheral arterial region such as blood pressure and / or a systemic vascular resistance are then determined.

The invention relates to a continuous noninvasive physiological parameter detection method and device, and belongs to the technical field of physiological parameter detection. According to the method, on the basis of photoelectric volume pulse wave detection data under multiple wavelengths, absorbance related characteristic parameters and pulse wave related characteristic parameters are extracted, a hemoglobin concentration detection result is obtained through calculation of a hemoglobin concentration calculation model, and the absorbance related characteristic parameters comprise the adjacent surface difference, the relative difference, the adjacent surface ratio, the relative ratio and the progressive surface ratio. Extracting based on a normalized absorbance fluctuation amplitude curve; the pulse wave characteristic parameters comprise rising branch maximum slope, rising time, falling time, dicrotic wave slope, dicrotic amplitude ratio, rising branch time ratio, low-high frequency energy ratio and harmonic component ratio, and extraction is carried out based on normalized pulse waves. The detection device for implementing the method comprises a probe, a data processing circuit and the like. The accuracy of the noninvasive hemoglobin concentration detection result can be effectively improved.

The invention discloses a diabetes detection system based on multi-sensor pulse characteristics, and relates to the field of medical signal processing, and the diabetes detection system comprises a pulse wave data acquisition module which uses a pressure sensor and a photoelectric sensor to collect wrist pressure pulse waves and fingertip photoelectric volume pulse waves of a user; the preprocessing module is used for obtaining usable single-cycle pulse wave data; the signal fusion module is used for converting the pressure pulse wave signal and the photoelectric volume pulse wave signal into two-dimensional image signals through continuous wavelet transformation so as to reserve time-frequency domain information, and processing two two-dimensional images by using non-subsampled shear wave transformation and a pulse coupling neural network; the feature extraction and calculation module is used for extracting pulse wave image features through a ResNet network and finally performing classification through a random forest; according to the detection system, the fusion effect is improved under the condition that the calculated amount is not increased, the effect of noninvasive detection of diabetes by using multiple sensors is achieved, and the detection accuracy is improved.

The invention discloses a method for reconstructing electrocardiosignals through photoelectric volume pulse waves based on deep learning, and aims at solving the problems that a traditional ECG collection mode is complex, an electrode is uncomfortable to wear, and an existing signal mapping method is low in precision. PPG signals are collected through a portable device and subjected to denoising and normalization processing, a generative adversarial network is constructed, a generator maps the PPG signals into ECG signals, and a discriminator evaluates the authenticity of the generated signals. The generator loss function is combined with the mean square error and the adversarial loss, and the discriminator loss function optimizes the identification degree of the signal; a multi-scale loss function of a time domain and a frequency domain is introduced, the consistency of the generated signals in dynamic characteristics and spectral characteristics is improved, and finally, the generated ECG signals are transmitted back to the portable equipment to be displayed. Real-time signal generation can be achieved on portable equipment such as an intelligent bracelet and a watch, the generated ECG signals have high fidelity, PQRST waveforms can be accurately restored, and the method is suitable for heart rate and heart rhythm variability analysis and abnormal heart rhythm detection.

A source stochastic signal is deconstructed into one or more components using a decomposition process. The decomposition process deconstructs the source stochastic signal into at least one component which encodes the oscillatory behavior of the source stochastic signal. The one or more components are mapped, using one or more trained models, to a pair of target components which encode the estimated oscillatory behavior and estimated steady-state behavior of a target stochastic signal. The target stochastic signal is then reconstructed from the oscillatory behavior target signal by shifting the oscillatory behavior target signal so that its average value aligns with the average value of the steady-state behavior target signal. The source stochastic signal and the target stochastic signals can be biological signals having a related or common origin, such as photoplethysmogram (PPG) signals and arterial blood pressure (ABP) waveforms.

A computing system, a computer implemented method, and a wearable computing device to detect pulselessness in a wearer of the wearable computing device while minimizing the risk of false positives is provided. A series of passive, semi-active, and / or active algorithms utilizing time domain analysis, gate-based analysis, and / or a machine-learned model can be implemented to analyze motion and photoplethysmogram sensor data to verify if a pulselessness event associated with the wearer has occurred and to alert a third party of such an event.

An electronic fitness device comprises a housing, a first optical transmitter, a second optical transmitter, an optical receiver, and a processor. The housing is positioned partly in contact with a user's skin. The first optical transmitter emits a first optical signal. The second optical transmitter emits a second optical signal. The optical receiver receives the optical signals and generates a first photoplethysmogram (PPG) signal resulting from the received first optical signal and a second PPG signal resulting from the received second optical signal. The processor receives the PPG signals, determines a pressure metric value based on a characteristic of a waveform of the first PPG signal and a characteristic of a waveform of the second PPG signal, and determines a pressure compensation factor based on the determined pressure metric value, the first wavelength corresponding to the first optical signal and the second wavelength corresponding to the second optical signal.

The invention relates to the technical field of biomedical signal processing, in particular to an electrocardiogram reconstruction method and system based on machine learning, and the method comprises the following steps: carrying out iterative initialization; respectively carrying out data preprocessing on the PPG signal and the ECG signal; based on the convolutional neural network and a Transform encoder, constructing a signal conversion model; inputting the preprocessed data into a signal conversion model, carrying out model training, and outputting a predicted electrocardiogram signal; calculating a loss function between the predicted electrocardiogram signal and the target real signal, and optimizing the model parameters based on the loss function; when the number of iterations is smaller than a threshold value, continuously updating model parameters according to a gradient descent method and circularly training, and evaluating model performance and calculating performance indexes in a verification stage; after iteration is completed, the trained network parameters are stored, and reconstructed electrocardiogram signals are output. The electrocardiogram reconstruction method provided by the invention aims to solve the technical problem of accurately reconstructing the electrocardiogram through the photoplethysmogram.

An apparatus for estimating blood pressure may include: a photoplethysmogram (PPG) sensor configured to measure a PPG signal from a user; and a processor configured to: extract a cardiovascular feature from the PPG signal; calculate a first variation in the extracted cardiovascular feature compared to a reference cardiovascular feature measured at a reference time; determine a measurement posture of the PPG signal based on a time interval between two waveform components of the PPG signal; in response to the measurement posture corresponding to the reference posture, estimate the blood pressure based on the first variation; and in response to the measurement posture not corresponding to the reference posture, obtain a second variation by correcting the first variation; and estimate blood pressure based on the second variation.

The invention discloses an immersive cinema dynamic content adaptation control method based on multi-modal fusion, and the method comprises the steps: collecting at least one non-contact physiological signal of a target audience seat area in real time, the physiological signal comprising a respiratory wave signal collected by a millimeter wave radar or a photoplethysmography signal collected by a near-infrared photoelectric sensor; performing time-frequency analysis on the physiological signal, and extracting a physiological wake-up characterization value in a current time window; the physiological wake-up characterization value comprises a heart rate variability change rate and a heart rate deviation value; constructing a dynamic damping adjustment model, and calculating a virtual damping coefficient of the seat movement system at the current moment based on the physiological wake-up characterization value; and the virtual damping coefficient is converted into an execution instruction of a seat motion controller, and damping force output of the motion platform is adjusted in real time. The technical problem that the pre-programmed motion feedback is disjointed with the real-time physiological and psychological state of the audience can be solved, the experience adaptability and comfort of the cinema seat are improved, and the system safety is improved.

The invention discloses a pressure monitoring method and device based on wearable equipment and electronic equipment. The method comprises the following steps: acquiring current photoelectric plethysmography (PPG) signals corresponding to a plurality of channels collected by wearable equipment; based on the current PPG signals corresponding to the multiple channels, candidate pulse wave sequences corresponding to the multiple channels are determined, and the candidate pulse wave sequences comprise multiple pulse waves of the corresponding channels; determining a target pulse wave sequence from the candidate pulse wave sequences corresponding to the multiple channels based on the pulse wave continuity evaluation results and the pulse wave quality evaluation results corresponding to the multiple channels; performing feature extraction on the target pulse wave sequence to obtain current heart rate variability features; and determining a current pressure monitoring result according to the current heart rate variability characteristic. According to the invention, the technical problem of low pressure monitoring accuracy and reliability of wearable equipment in a dynamic environment in the prior art is solved.

The embodiment of the invention discloses a blood pressure detection method and device which are applied to electronic equipment. The method comprises the following steps: acquiring a first photoplethysmography (PPG) signal of a user, and determining a first electrocardio (ECG) signal of the user based on the first PPG signal; performing peak detection on the first PPG signal and the first ECG to determine first pulse transit time of the user; and performing first quality detection on the first ECG signal and the first pulse conduction time, and if it is determined that the result of the first quality detection is normal, determining the blood pressure value of the user based on the first pulse conduction time. By adopting the embodiment of the invention, the accuracy of a blood pressure detection result can be improved, and the blood pressure detection cost is reduced.

The invention discloses a remote photoplethysmography (rPPG) signal extraction method based on a 3D convolutional neural network (3D CNN), and belongs to the technical field of image processing and physiological signal detection. In order to solve the problem that space-time information utilization is insufficient in a complex scene (illumination change and motion interference) in a traditional method, space-time feature fusion is carried out through 3D CNN, and feature expression is enhanced in combination with an encoder-decoder structure and an attention mechanism. The method comprises the following steps: extracting continuous time difference features of a face video, generating space-time fusion features through a 3D CNN, enhancing the space-time fusion features through a codec, and outputting rPPG signals. A self-supervised training method is innovatively proposed, and the generalization ability of the model is improved by adopting data enhancement, time period alignment and positive and negative sample comparison. Experiments show that according to the method, the heart rate estimation error is reduced to be within 1 BPM, the signal-to-noise ratio is increased to 1.8 dB, and the motion robustness is superior to that of a traditional algorithm. The method is suitable for non-contact heart rate monitoring, respiratory rate detection and other scenes, has the characteristics of high precision and strong anti-interference, and provides a reliable technical scheme for intelligent health monitoring.

The invention provides a non-invasive continuous blood glucose monitoring system based on radio frequency wave and biosensor fusion, and aims at solving the problems of pain and inconvenience of a traditional fingertip blood sampling monitoring method. The system core comprises a radio frequency wave transmitter which is used for monitoring the glucose level in blood in real time through interaction of radio frequency waves with specific frequency and glucose molecules; meanwhile, biosensor technologies such as electrocardiogram (ECG) and photoplethysmogram (PPG) are combined, physiological signals are analyzed to estimate the blood glucose level, and the monitoring accuracy and reliability are improved. Besides, the developed wearable device integrates a radio frequency wave transmitter and a biosensor, can continuously monitor the blood glucose level, and transmits data to a mobile device or a medical center in a wireless mode. And a deep learning algorithm is adopted to perform fusion processing on the multi-modal signals, so that the accuracy of blood glucose monitoring is further improved. The system is non-invasive, real-time and accurate, improves the compliance and life quality of diabetic patients, and is of great significance to management and treatment of diabetes.

The invention discloses a space behavior recognition access control system based on UWB and PPG fusion, and mainly relates to the technical field of intelligent security and protection. Comprising a UWB positioning unit used for acquiring spatial position data of a user in an access control area through an ultra wide band technology; the PPG acquisition unit is used for acquiring physiological signals of a user through a photoelectric volume pulse wave technology; the data fusion unit is used for performing multi-source data fusion on the spatial position data and the physiological signal to generate a fusion feature vector; the behavior recognition unit is used for recognizing a behavior mode of the user through a mode recognition algorithm based on the fused feature vector; and the access control unit is used for outputting a control instruction according to the identified behavior mode so as to operate the opening, closing or alarming of access control equipment. The access control system has the beneficial effects that non-contact accurate identification of spatial behaviors is realized through multi-source data fusion, and the identification accuracy, the anti-interference capability and the user experience of the access control system are effectively improved.

The invention provides an intelligent toilet seat life information parameter monitoring system and method, the system is applied to a toilet, the system comprises an optical fiber sensing module and a piezoelectric sensing module, the optical fiber sensing module and the piezoelectric sensing module are used for collecting ballistocardiogram and phonocardiogram signals during defecation so as to monitor the heart rate and the respiration rate in real time; the infrared sensor or the microwave radar sensor and the infrared sensing module or the microwave radar bed module comprise defecation detection and fall monitoring probes and are used for defecation frequency detection and iterative judgment alarm; the blood oxygen monitoring module comprises a photoelectric detector and is used for monitoring a photoplethysmogram and blood oxygen fluctuation change of the heart cycle of the user in real time; the non-inductive transmission module is used for providing wireless transmission, uploading data monitored by the intelligent toilet seat to the data management system and displaying the data on a display screen; and the master controller is used for uploading the data monitored by the sensing module to a data management system. According to the invention, automatic monitoring of the defecation process is realized, and invisible health monitoring of old people is realized.

An electronic device comprises a housing configured to contact a user's skin, an optical transmitter array, an optical receiver, a memory element and a processing element. The optical transmitter array outputs a plurality of optical signals that pass through the user's skin, each of the plurality of optical signals having one of three wavelengths. The optical receiver receives the optical signals and generates corresponding photoplethysmogram (PPG) signals. The memory element stores absorption constants for deoxyhemoglobin blood content, oxyhemoglobin blood content and glycated hemoglobin blood content, each absorption constant associated with one of the first wavelength, the second wavelength or the third wavelength. The processing element determines a glycated hemoglobin blood level for the user based on a ratio of a determined glycated hemoglobin blood content to a sum of determined deoxyhemoglobin blood content, a determined oxyhemoglobin blood content and a determined glycated hemoglobin blood content.

The present invention relates a multimodal emotion recognition system with edge AI accelerator. The system employs a Fuzzy Long-Short Recurrent Convolutional Network (Fuzzy LRCN) for multiple emotion classifications. The algorithm strategically utilizes only eight channels of electroencephalogram (EEG) signals and combines electrocardiogram (ECG) and photoplethysmogram (PPG) through a fuzzification engine to improve emotion recognition accuracy with minimal hardware resource consumption. The goal is to optimize hardware resource utilization, reduce system power consumption, and maximize emotion recognition accuracy. The fuzzy algorithm provides a lightweight multimodal AI model to address the issue of increased computational load caused by recognizing emotions from multiple signals.

The invention discloses a method for classifying various arrhythmia signals, belongs to the technical field of arrhythmia signal classification methods, and the accurate classification of arrhythmia has important significance for disease diagnosis and clinical treatment. However, the traditional detection method is often interfered by various factors such as signal non-stationarity, feature non-specificity, artifacts and noise caused by arrhythmia when processing various arrhythmia, and the classification precision and efficiency are difficult to meet the requirements. Therefore, the invention provides an improved power normalized cepstrum coefficient (SPNCC) method based on singular spectrum analysis (SSA), which is used for extracting key features from a photoplethysmogram (PPG) signal and reducing the influence of noise, and constructing a deep learning classification model in combination with a convolutional neural network and a bidirectional long short-term memory network (CNN-BiLSTM). Therefore, accurate identification of various arrhythmias can be realized.

Methods, systems, and devices for optical signal measurement are described. A wearable electronic device may activate a first combination of optical sensors, the first combination of optical sensors including a set of transmitter sensors and a set of receiver sensors. In some cases, one or more optical sensor of the first combination of optical sensors may be positioned under a protrusion on an inner surface of the wearable electronic device. The device may measure, at the set of receiver sensors at a first time, one or more signals from the set of transmitter sensors, determine a signal quality metric associated with the one or more signals, and select a second combination of optical sensors for use at a second time based on the signal quality metric.