989 results about "Intracardiac Electrogram" patented technology

An electrocardiogram device configured to transmit at least one signal responsive to a wearer's cardiac electrical activity can include a disposable portion and a reusable portion configured to mechanically and electrically mate with each other. The disposable portion can include a base having at least one mechanical connector portion, a plurality of cables and corresponding external ECG electrodes, and a first plurality of electrical connectors associated with the plurality of cables. The reusable portion can include a cover having at least one mechanical connector portion, a second plurality of electrical connectors configured to electrically connect with the first plurality of electrical connectors of the disposable portion, and an output connector port configured to transmit at least one signal responsive to one or more signals outputted by the external ECG electrodes of the disposable portion.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

The invention discloses an electrocardiogram classification system and method fusing multi-scale self-adaptive attention, and relates to the technical field of electrocardiogram classification.The electrocardiogram classification method comprises the steps that electrocardiogram data are collected through a data collection module, and a structured data set is output; the data preprocessing module is used for carrying out data preprocessing on the structured data set; the window data segmentation module obtains a standardized windowed electrocardiosignal; the feature extraction module takes the standardized windowed electrocardiosignals as input, performs three stages of multi-scale convolution feature extraction, liquid neural network dynamic modeling and self-adaptive attention mechanism enhancement, and outputs diagnosis results of nine types of heart diseases; and the training and optimizing module is used for optimizing the extraction and classification capability of the model on the electrocardiosignal features, so that the technical effects of full-process technical upgrading from data acquisition, preprocessing, feature extraction to model training optimization and high-precision electrocardiogram automatic classification are achieved.

A blood pump system includes a catheter, a pump housing disposed distal of a distal end of the catheter, a rotor positioned at least partially in the pump housing, a controller, and an electrode coupled a distal region of the blood pump. The electrode can be used to sense electrocardiogram (EKG) signals and transmit the signals to a controller of the blood pump. The operation of the blood pump can be adjusted based on the EKG signal and on cardiac parameters derived from the EKG signal. Further, the controller can determine a need for defibrillation or pacing of the patient's heart based on the signal and can administer treatment with electrical shocks to the heart via the electrode coupled to the blood pump. The use of an electrode with a blood pump already in place in the heart allows for more efficient and safer treatment of serious cardiac conditions.

Artificial intelligence enabled disease profiling is described. An electrocardiogram analysis module is configured to derive disease vectors for a plurality of diseases using electrocardiogram training data from both disease-negative and disease-positive individuals. A standardized input is generated, via a data preprocessor of the electrocardiogram analysis module, from an electrocardiogram recorded from an individual. The standardized input is encoded, by a deep learning autoencoder of the electrocardiogram analysis module, into an embedding, the embedding being a lower-dimensional latent space representation of features extracted from the standardized input. At least one disease risk score for the individual is generated, by a statistical modeling algorithm of the electrocardiogram analysis module, for the plurality of diseases based on the embedding and the disease vectors.

The invention discloses an electrocardiogram arrhythmia classification method and system based on a residual shrinkage network, and relates to the technical field of arrhythmia classification. According to the method, efficient electrocardiogram arrhythmia classification is achieved through multi-link cooperation, and a fine preprocessing, data balance strategy and multi-attention mechanism fusion model is designed; preprocessing provides high-quality input through wavelet denoising, precise R-wave detection and the like; the under-sampling-over-sampling mixed strategy is used for solving class imbalance and improving minority class recognition; the ResTCL-Net is fused with CNN, GRU, RCA, TSA and CLA modules, and signal features are mined in multiple dimensions; the optimization training strategy gives consideration to efficiency and stability, and is matched with comprehensive evaluation to guarantee performance. According to the scheme, the classification accuracy and generalization ability are remarkably improved, and abnormal heart beat recognition is enhanced.

A method for determining physiological characteristics is provided. The method involves causing light to be emitted by one or more light emitters toward a tissue of a user. The method involves obtaining samples of transmitted and / or reflected light. The method involves determining photoplethysmography (PPG) data based on the obtained samples. The method involves providing the PPG data as an input to a trained machine learning model, wherein the trained machine learning model has been trained, using a training set that comprises matched samples of electrocardiogram (ECG) data and PPG data. The method involves providing a blood pressure of the user based on an output of the trained machine learning model.

The invention discloses an adjustable blood transfusion reaction monitoring system. The system comprises a multi-mode physiological signal acquisition module, a data processing and analysis module, a user interaction and parameter configuration interface, a wireless communication module, an alarm and feedback module and a power management module. By integrating an electrocardiogram (ECG) sensor, an oxyhemoglobin saturation (SpO2) detection probe, a non-invasive blood pressure measuring device, a body temperature sensor, a respiratory rate monitor and a skin impedance change sensor, real-time acquisition of various physical parameters is realized; a rule-based threshold judgment model and a time sequence prediction model are built in the data processing and analysis module, and the data processing and analysis module is used for identifying potential blood transfusion reaction risks; and the user interaction and parameter configuration interface provides a touch display screen and remote terminal equipment to realize information input, data display and operation recording. Continuous dynamic monitoring, intelligent early warning and graded response in the blood transfusion process are achieved, blood transfusion safety and nursing efficiency are improved, and the system has wide clinical application prospects.

Disclosed are a single-arm electrocardiogram (ECG) signal measurement device and a single-arm ECG signal measurement method. The single-arm ECG signal measurement device includes a first sensing electrode, a second sensing electrode, a third sensing electrode, a fourth sensing electrode, a fifth sensing electrode, a sixth sensing electrode, and a computing circuit. The first sensing electrode, the second sensing electrode, the third sensing electrode, the fourth sensing electrode, the fifth sensing electrode, and the sixth sensing electrode receive physiological signals from six different positioning points located on an upper arm of a user. The computing circuit generates ECG signals according to the physiological signals, and generates a 12-lead ECG-like signal according to the ECG signals.

The invention discloses a PICC (Peripherally Inserted Central Catheter) tip precise navigation fixing method and system based on electrocardiogram real-time positioning. High-precision positioning and safe fixing of a catheter tip are realized through a multi-modal data fusion and deep learning technology. The method comprises the following steps: collecting intracavity electrocardiosignals (ECG) in real time through a catheter built-in electrode, and obtaining blood vessel wall contact pressure and temperature data in combination with an optical fiber sensor; an improved wavelet threshold algorithm is adopted to dynamically suppress motion artifacts, and P-wave features are enhanced; the preprocessed ECG signals are input into a spatial-temporal feature fused Transformer model, a time attention layer is used for analyzing P-wave time sequence changes, a space attention layer integrates multi-lead space distribution features, and the positioning precision is optimized in combination with preoperative blood vessel anatomical data; the ECG positioning result and the optical fiber sensing data are fused through Kalman filtering, three-dimensional position information of the tip end of the catheter is generated, and real-time navigation is conducted on an augmented reality interface; and when the P wave amplitude reaches a CAJ threshold value, the contact pressure is safe and no abnormal temperature exists, a shape memory alloy (SMA) fixing ring is triggered to contract, and accurate fixing is achieved. The method solves the problems that a traditional method depends on X rays and is poor in anti-interference performance, has the advantages of being free of radiation, high in real-time performance and adaptive to blood vessel deformation, and is suitable for clinical P catheter implantation.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

An apparatus for generating diagnostic hypotheses based on electrocardiogram (ECG) data, comprising a processor and a memory containing instructions configuring the processor to generate, using a generative model trained on a corpus, a set of diagnostic hypotheses, wherein generating the set of diagnostic hypotheses includes creating labels, each represents a diagnostic feature associated with diagnostic hypotheses, receive a biomedical signal, identify a biomedical feature as a function of the biomedical signal, select a diagnostic hypothesis from the set of diagnostic hypotheses by matching the biomedical feature against the diagnostic feature, query, as a function of at least a matched label, a medical repository to validate the diagnostic hypothesis, wherein the medical repository includes patients' electronic health records (EHRs), and output the diagnostic hypothesis upon a positive validation of the diagnostic hypothesis.

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 millimeter wave electrocardiogram reconstruction system and method based on adaptive MODWT and CNN-BiLSTM-CA, and the method comprises the steps: collecting a tiny phase displacement signal of a target thoracic cavity region, and carrying out the multi-band decomposition and key component adaptive screening of an original signal through combining with a multi-scale stationary wavelet decomposition algorithm; and introducing a channel attention mechanism to enhance key information representation, inputting a reconstruction signal into a deep learning model combining a convolutional neural network and a bidirectional long-short term memory network, completing time sequence modeling and nonlinear mapping, and outputting a reconstruction waveform highly consistent with a standard electrocardiogram. According to the method, the signal reduction capacity under the complex interference condition is remarkably improved, high-precision and privacy-friendly remote physiological signal monitoring can be achieved under the condition of not depending on a lead electrode, and the method is superior to a traditional baseline model in the aspects of waveform reduction precision, signal time sequence consistency, model generalization capacity and the like; the method is suitable for various application scenes such as intelligent medical treatment.

The invention provides a multi-modal emotion recognition method and system based on knowledge distillation. The method comprises a pre-training stage, a knowledge distillation stage, a fine adjustment stage and a prediction stage. The pre-training stage comprises the following steps: independently training a preset neural network by using electroencephalogram, electrocardiogram and facial expression data to obtain three independent teacher models; the knowledge distillation stage comprises the step of migrating the representation learned by the teacher model to the student model through a knowledge distillation technology; the fine tuning stage comprises the step of carrying out joint optimization on the student model by utilizing a small amount of labeled multi-modal data; the prediction stage comprises the steps of dynamically fusing the multi-modal features by adopting an attention mechanism, and inputting the fused features into a classifier to obtain an emotion recognition result. The method is oriented to three modes of electroencephalogram, electrocardio and facial expression, and the characterization ability of the model to complex emotion and state change is improved; and a knowledge distillation mechanism is introduced, so that the complexity of the model is reduced, and the deployment feasibility and the operation efficiency of the model in practical application are improved.

A medical system is disclosed that includes a medical device having an optical fiber, and an interchangeable connection component configured to provide a fiber optic connection between the medical device and capital equipment. The connection component is configured to facilitate cleaning and / or polishing of fiber optic interfaces include therewith. The connection component is also configured for replacement by a medical technician while the capital equipment is operational. The capital equipment may include one or more of an optical interrogator, a patch cable, a medical probe, an ultrasound machine, a display, a magnet sensor, or an electro-cardiogram (ECG) machine. The medical device may include an elongate member configured for insertion within the patient body, where the optical fiber core extends along a length of the elongate member.

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 discloses a coronary artery lesion risk prediction method, a coronary artery lesion risk processing device, a coronary artery lesion risk prediction system and a computer readable storage medium. Obtaining a point sequence according to high-frequency QRS wave group data obtained by the exercise electrocardiogram data; aiming at a point sequence corresponding to any electrocardiogram lead, taking any sampling point in a plurality of sampling points in the point sequence as a current reference sampling point in a traversal cycle, and traversing sampling points in a preset time interval from the current reference sampling point, determining whether a first sampling point and a second sampling point meeting a preset condition exist in the preset time interval, wherein the first sampling point is earlier than the second sampling point in time sequence; and if the preset condition is met, determining that the high-frequency QRS wave group data has waveform characteristics indicating the coronary artery lesion, thereby determining the possibility of the coronary artery lesion. Compared with a previous scheme, the scheme has the advantage that the qualitative accuracy of coronary artery lesion is improved.

In various embodiments, computer-implemented systems and methods for extracting pixel trajectories representing waveforms from a digital image, formed by lines and columns of picture elements, pixels, of a recording of an electrical activity of a human organ detected by on-skin electrodes, such as an electrocardiogram, ECG, or an electroencephalogram, EEG, are provided.

An apparatus for identifying the progression of coronary heart disease has been disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a subject profile associated with a subject, wherein the subject profile comprises a plurality of electrocardiogram (ECG) data. The memory instructs the processor to identify contextual data as a function of the subject profile. The memory instructs the processor to generate a set of cardiac scores as function of the contextual data and the plurality of ECG data using a set of cardiac machine learning models. The memory instructs the processor to select at least one stage of coronary heart disease from a plurality of stages of coronary heart disease of the subject as a function of the set of cardiac scores.

The present application refers to a process and a system for collecting, storing, analyzing and visualizing electrocardiographic (ECG) data collected by portable ECG device (100) with embedded wireless communication technology, additionally containing a portable ECG device (100), a hermetically sealed box (200) of medicines containing a lock system comprising a lock (220), a first device (310) to be accessed by the patient, a second device (320) to be accessed by the medical professional, a platform for graphical display of ECG data, and a remote data storage and processing central. The hardware board of the portable ECG device contains wireless communication technology, global positioning system (GPS), start button (141), rechargeable battery charging port, and an equipment configuration and programming port. The battery (120) is Li-Ion type, containing LEDs indicating device operation, and waiting for connection. The battery status is displayed on the app display. In one embodiment, an electrode (130B) is positioned on the bottom cover (110B) of the housing (110A, 110B, 110C) of the ECG device (100).

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.

Sleep apnea prediction using electrocardiograms and machine learning is described. In one or more implementations, a wearable monitoring device produces electrical potential measurements of a heart of a user during an observation period spanning multiple days. A sleep apnea classification of the user is predicted by providing the electrical potential measurements to one or more machine learning models as input. The one or more machine learning models are trained based on historical electrical potential measurements and historical outcome data of a user population to correlate patterns in electrical potential measurements to sleep apnea classifications. The sleep apnea classification may then be output, such as in a health report, via a user interface, as notification on a computing device, and so forth.

The invention provides an electrocardiograph fault self-diagnosis method and system based on lead wire automatic identification, and the method comprises the steps: triggering a frequency sweep generator according to an equipment starting signal, carrying out the pre-conduction detection of lead wires, then applying an excitation signal in a continuous frequency range to each lead wire, recording the signal collection time length of each frequency point in real time, and carrying out the real-time detection of the signal collection time length of each frequency point; obtaining response voltage and current data to determine a real-time impedance spectrum; performing frequency point alignment processing on the real-time impedance spectrum and a corresponding standard fingerprint, selecting frequency points in a key frequency band of the electrocardiosignal, and comparing amplitudes and phase deviation values of the real-time impedance spectrum and the corresponding standard fingerprint on the key frequency points to obtain a spectrum difference vector; and if the amplitude deviation of the spectrum difference vector exceeds a preset disconnection threshold value, judging that the lead wire has a disconnection fault, performing reverse signal back test on an interface end corresponding to the faulted lead wire, and recording a judgment result in a fault log to obtain a disconnection identification mark.

The embodiment of the invention relates to the field of multivariable data processing, in particular to a processing method and a processing system for multi-dimensional cardiac structure function data. According to the method, the data of the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram are subjected to noise reduction processing through the feedforward neural network, the data of the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram after noise reduction are obtained, and the credibility of the data is higher; fusing the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram of different time sequence segments, and determining characteristic parameters; combining and splicing the respectively determined characteristic parameters according to a time sequence to obtain a multi-parameter matrix; and processing the multi-parameter matrix by using a multi-modal fusion network to obtain a processed multi-dimensional vector, mutually correcting a plurality of parameters through processing, and splicing the plurality of parameters to obtain a multi-dimensional vector to obtain a high-credibility parameter which better conforms to a real physiological law.

The application discloses a method for generating electrocardiogram based on a diffusion model and synthesizing a customizable cardiac cycle, relates to an electrocardiogram generation method, and aims at solving the problems of the existing electrocardiogram generation method, such as data imbalance, poor privacy protection and the incapability of generating specific pathological signals. The application takes electrocardiogram semantic labels as conditional input, simultaneously inputs noise and diffusion time steps into a deep generation model, and generates electrocardiogram; the deep generation model takes a diffusion model as an overall architecture, introduces a converter model to learn long-term dependencies in electrocardiogram signals, and simultaneously introduces a semantic electrocardiogram batch normalization module to accurately learn local ECG semantic features. The electrocardiogram signal generated by the application can accurately follow the provided electrocardiogram semantic information, customize electrocardiogram with real physiological significance, and improve the data imbalance problem and the privacy protection.

A method includes classifying, using a machine learning model, a portion of an electrocardiogram measurement as an artifact. The method further includes normalizing the electrocardiogram measurement except the portion of the electrocardiogram measurement classified as the artifact. The method further includes applying the machine learning model to the normalized electrocardiogram measurement to detect a cardiac event.

The invention discloses a method for extracting an electrocardiogram waveform based on an electrocardiogram, which comprises the following steps of: S1, acquiring the electrocardiogram, and correcting the electrocardiogram to obtain a front view image of the electrocardiogram; s2, acquiring a target selected area image which is selected by a user based on the front view image and is used for extracting an electrocardio waveform; s3, performing binarization processing on the target selected area image to obtain a target selected area binarization image; and S4, extracting electrocardio waveform feature points in the binarized image of the target selected area, and constructing the electrocardio waveform: according to the waveform features on the electrocardiogram, the user can autonomously select the waveform selected area to extract the electrocardio waveform.