538 results about "Intracardiac Electrogram" patented technology

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.

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 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.

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).

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.

A pad carrying an array of ECG electrodes can be used to measure ECG signal amplitudes and AMSA, a signal analyzer determines a cardiac position with respect to the pad, and an indication of cardiac position with respect to indicia can be provided on a top surface of the pad for output to the user to guide in performing chest compressions during CPR. The pad may integrate a defibrillator electrode. The apparatus may be an automated external defibrillator (AED).

A diagnostic electrocardiogram (ECG) apparatus (20) is disclosed herein. The apparatus comprises a body and printed electrodes. The body (110) includes extension members (61-66), with each having an expansion section (61a) and at least one electrode section (61b). Each expansion section (61a) has at least one concertina member (75) and at least one connector member (76). The diagnostic ECG apparatus (20) conforms to American Heart Association guidelines on diagnostic resting ECGs.

A classification model for multimodal human physiological data, and a training method therefor, a classification method for multimodal human physiological data, and a device are provided. The classification model includes: a multi-headed self-attention module, a normalization module, a fusion expert system, and a decision module. The multi-headed self-attention module is configured to perform feature extraction on multimodal synchronous data. The normalization module is configured to generate normalized feature data based on the extracted feature data. The fusion expert system includes: an electroencephalogram expert subsystem, an electrocardiogram expert subsystem, an electrodermal activity expert subsystem, and a multimodal synchronous fusion expert subsystem each configured to perform a classification task based on the corresponding normalized feature data. The decision module is configured to calculate a final classification result based on the above four classification results.

An apparatus for standardization of electrocardiogram signal images, the apparatus having an imaging device, at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive an overlay image from the imaging device, identify a captured fixed background image and a captured primary image within the overlay image, wherein the captured primary image includes a plurality of electrocardiogram signals, compare the captured fixed background image to one or more image quality thresholds, determine an image quality score of the primary image as a function of the captured fixed background image, and output one or more image modification datum as a function of the image quality score.

The invention discloses an electrocardiogram technology synthesized by an auto-encoder, relates to the technical field of crossing of medical image processing and computer vision, in particular to the electrocardiogram technology synthesized by the auto-encoder, and aims to solve the problem that an existing ECG image generation method is difficult to simulate real paper wrinkles and geometric deformation. According to the method, ECG images are input in a partitioned mode to serve as sub-blocks, pixel offset is extracted from style images for geometric deformation, a diffusion model is adopted for style transfer, content feature anchoring and a style mapping enhancement mechanism are combined, and the vivid wrinkle visual effect is injected while it is ensured that ECG waveform features are reserved. The image generated by the method has high structural similarity and visual authenticity, is suitable for medical data enhancement and diagnosis model training, and improves the generalization ability and clinical practicability of downstream tasks.

A method includes receiving—by a first computing system—electrocardiogram (ECG) data and metadata associated with the ECG data, where the metadata includes an initial cardiac event classification and an initial beat classification for beats occurring during a first event associated with the initial cardiac event classification. The method further includes causing the ECG data to be displayed in a user interface (UI) and receiving a command, via the UI, to change the initial beat classifications to subsequent beat classifications. The method also includes automatically modifying, by the first computing system, the initial cardiac event classification to a subsequent cardiac event classification based on the subsequent beat classifications.

The application discloses a physiological signal monitoring method and a wearable device, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of existing electrocardiogram for heart state judgment. The method comprises the following steps: acquiring user basic information and a physiological signal collected in real time, wherein the physiological signal comprises a heart electrocardiogram signal, a muscle electrocardiogram signal and an acceleration signal, the physiological signal is collected based on a multi-modal sensor on a wearable device, and the wearable device is fixed on the user body through a belt type connection assembly; performing multi-modal fusion on the physiological signal based on a multi-modal fusion algorithm to obtain physiological parameter data, and determining a monitoring task, wherein the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; calling a monitoring prediction model matched with the monitoring task and having completed model training to perform monitoring processing on the physiological parameter and the user basic information, obtaining a monitoring result, and performing feature comparison based on the monitoring result.

A method includes receiving a package of data comprising electrocardiogram (ECG) data and metadata. The metadata includes determined RR intervals associated with the ECG data. The method further includes displaying, on a user interface, at least a subset of the determined RR intervals and overriding the determined RR intervals associated with a time period that is greater than a time period associated with a selected RR interval.

An example wearable device includes a first housing, a second housing, a first electrocardiograph (ECG) electrode, a circuit board, and a temperature sensor. The first housing includes a first surface, a second surface. The circuit board is disposed with a contact. The first ECG electrode includes a first detection end, a second detection end, and a connection part. The first detection end is disposed on the first surface, the second detection end is disposed on the second surface. The second detection end is electrically connected to the contact and is in thermally conductive contact with the temperature sensor.

The invention discloses a wearable real-time nursing monitoring device for physiological signs. The wearable real-time nursing monitoring device comprises an electrocardio garment body, a sensor integration module, a micro-processing module, an electrode slice and a heating module, the electrocardio garment body is made of a flexible fabric material, has a shape fitting the curve of a human body and is used for being worn on the trunk of the human body; the sensor integration module is embedded in a fabric of the electrocardiograph garment body, and the sensor integration module comprises an electrode sensor, a heart rate sensor, a respiration sensor, a temperature sensor, a motion sensor, an optical sensor and an electrocardiogram analysis module; the intelligent electrocardiograph garment has the advantages that the electrocardiograph sensor, the heart rate sensor, the breathing sensor, the temperature sensor, the motion sensor and the optical sensor are embedded into the electrocardiograph garment in a fabric integration mode, multiple key vital signs can be collected synchronously for a long time in a non-sensitive mode in the natural living state of a user, and the flexible electrodes are matched with the special electrocardiograph analysis module, so that the user experience is improved. And the continuous and dynamic electrocardiogram monitoring capability is realized in a wearable clothing form.

A method of establishing a model for detecting atrial fibrillation (AF) and sleep apnea (SA) is implemented by a computer system that stores training electrocardiograms, and includes steps of: dividing the training electrocardiograms into training segments, each of which contains a common length of time of recorded electrical activity of a heart; for each of the training segments, labeling the training segment with a symptom indicator that indicates whether the training segment is related to AF and whether the training segment is related to SA; for each of the training segments, performing feature extraction on the training segment to obtain an entry of feature data; and establishing the model by using a machine learning algorithm based on the entries of feature data and the symptom indicators.

A resuscitation system employing a handheld device (60) and a front-end rescue device (80) partitioned from the handheld device (60) and powered by the handheld device (60). In operation, the an electrocardiogram (ECG) module of the front-end rescue device (80) communicates electrocardiogram signals from a heart of a patient to the hand-held device, and a rescue controller (70) of the hand-held device controls a charging of a shock module (100) of the front-end rescue device (80) by the battery and / or power supply (61). Subsequent to the charging of the shock module (100), the rescue controller (70) controls a conditional discharging of the shock module (100) to the heart of the patient based on the electrocardiogram signals received by the rescue controller (70) from the electrocardiogram module (90).

The utility model discloses an electrocardiogram chest electrode assembling device, which relates to the technical field of electrocardiogram chest electrode equipment and comprises a first motor, first rollers arranged on two sides of the first motor, a conveying belt arranged on the outer surfaces of the first rollers, a second roller arranged at the other end of the conveying belt and a limiting plate arranged on the outer side of the conveying belt. The first air cylinder is arranged on one side of the first motor, the supporting block is arranged on one side of the first air cylinder, the second motor is arranged in the supporting block, parts can be continuously conveyed to a designated station through the conveying mechanism, in addition, the pushing mechanism can push the parts to the designated position, and the device can adapt to parts of different sizes; the air suction disc is arranged, center positioning is completed, a follow-up pressing mechanism can conveniently work the air suction disc, the pressing mechanism can prevent the situation that suction force still exists on the parts when the air suction disc stops working, and the arranged anti-falling mechanism can easily prevent falling of the parts and can also prevent falling of connecting wires.

The invention relates to the technical field of traditional Chinese medicines, in particular to a traditional Chinese medicine composition with the effects of treating atrial fibrillation and improving anxiety, a medicine and a preparation method and application thereof. When the traditional Chinese medicine composition provided by the invention is combined with auricular point pressing beans to treat patients with atrial fibrillation and anxiety, clinical symptoms of atrial fibrillation and anxiety can be further improved, specifically, the score of traditional Chinese medicine syndromes is reduced, the effective rate of electrocardiograms and the clinical effective rate of anxiety are improved, the life quality of the patients is improved, and no obvious adverse reaction exists. Meanwhile, a traditional Chinese medicine oral administration and traditional Chinese medicine external treatment combined method is adopted, syndrome differentiation treatment can be carried out from the holism concept, the holographic therapy of external treatment of internal diseases can be utilized to the maximum extent, and a simple, convenient, safe and effective treatment thought is provided for early prevention, early diagnosis and early treatment of more diseases.

Disclosed is an electrocardiogram biometric authentication device and method that allows user addition. An electrocardiogram biometric authentication method performed by a computing device comprising at least a processor includes acquiring a target electrocardiogram signal of a user; generating a plurality of vectors by preprocessing the target electrocardiogram signal; generating an embedding vector by inputting the plurality of vectors to a pretrained embedding model; retrieving a similar embedding vector having a smallest distance from the embedding vector from among prestored embedding vectors; and authenticating the user based on a distance between the embedding vector and the similar embedding vector.

The present disclosure relates to the technical field of automatic intelligent auxiliary detection of electrocardiogram, and proposes a self-supervised neural network learning heartbeat classification method and system based on pre-labeling, which comprises the following steps: preprocessing the acquired electrocardiogram data to obtain heartbeat data; performing time-frequency analysis on the acquired one-dimensional heartbeat data to convert it into a two-dimensional time-frequency graph; inputting the converted two-dimensional time-frequency graph into a trained self-supervised learning network model to classify the heartbeat data and obtain a classification result; the self-supervised learning network model is trained in combination with the SimCLR method and the clustering method, so that the loss function term changes from one item to two items, the learning effect is strengthened, and the classification accuracy of the heartbeat data can be improved.

Electrocardiogram-based deep learning for cardiac prediction is described. An electrocardiogram analysis module may include a data preprocessor configured to normalize an electrocardiogram to generate a standardized input for an electrocardiogram-based cardiac prediction. The electrocardiogram analysis module may further include a deep learning model including a neural network and at least one dense layer, the deep learning model trained to identify features associated with a cardiac condition that reflect underlying cardiac changes that result from or predispose development of the cardiac condition, and generate a cardiac prediction based on the identified features.

The invention aims to provide an electrocardiogram dynamic monitoring and grading early warning system for clinical trials, which comprises a wearable multi-parameter sensing terminal for collecting multi-modal physiological signals of a user; the edge calculation unit is used for receiving a real-time data stream from the wearable multi-parameter sensing terminal and executing local signal synchronization, filtering preprocessing, preliminary anomaly detection and emergency level local alarm; the cloud analysis platform is used for storing the multi-modal data and executing depth feature extraction, abnormal mode recognition, cross-parameter fusion modeling and individualized risk grading evaluation, and the cloud analysis platform is in two-way communication with the edge calculation unit and is used for synchronizing model update feedback and an alarm result; meanwhile, the method has the inter-lead topology perception capability, the unsupervised semantic alignment capability, the multi-modal fusion reasoning capability, the individualized risk discrimination capability and the edge-cloud collaborative closed-loop capability.

Accessories of a medical device, such as a defibrillator, are described. The accessories reduce delays in treating or monitoring a patient by increasing the efficiency of using, and the ease of use of, the medical device. An adjustable kickstand is movable between a collapsed and extended position to recline the medical device. In the reclined position, a display of the medical device can be more easily viewed by the user. Storage bags can be coupled to the medical device to efficiently store accessories for use with the medical device. The stored accessories can be coupled to the medical device while the accessories are stored within the storage bags. A port guard can protect and shield a connection between a cable and a port of the medical device to prevent the cable (e.g., an ECG cable) from being disconnected from the port of the medical device.

Technologies for determining a risk of developing atrial fibrillation may include a compute device. The compute device may include circuitry configured to obtain patient data indicative of an electrocardiogram to be analyzed for a likelihood that a corresponding patient will develop atrial fibrillation. The circuitry may also be configured to determine, based on the patient data and a prediction model that includes an ensemble of gradient boosted weak prediction submodels, the likelihood that the patient will develop atrial fibrillation.

According to one embodiment of the present disclosure, there are disclosed a method, program and apparatus for providing a user interface for electrocardiogram analysis. The method may comprise: obtaining bio-data of an electrocardiogram reading target; and displaying a first control graphic configured to switch a visual representation and state according to the result of the electrocardiogram analysis performed based on the obtained bio-data in the first area of the user interface. Furthermore, the first control graphic may be constructed for each type of disease or electrocardiogram feature that can be read from an electrocardiogram.

The application discloses an ambulance electrocardiogram data transmission method, device, equipment, medium and product, and belongs to the technical field of medical data remote transmission. The method comprises the following steps: an industrial computer in the ambulance receives vital sign monitoring data from a vital sign monitor; the vital sign monitoring data is converted into an electrocardiogram waveform, and the electrocardiogram waveform is converted into coordinate values for storage; an arrhythmia part in the electrocardiogram waveform is identified; in the case that a network state is normal, the converted coordinate values of the electrocardiogram waveform are sent to a cloud server, and the cloud server is used for sending the received electrocardiogram waveform coordinate values to a hospital client; in the case that the network state is abnormal, the coordinate values corresponding to the arrhythmia part in the electrocardiogram waveform are graded and cached and are transmitted in a delayed manner. The method can improve the transmission efficiency of electrocardiogram data and ensure the integrity of the electrocardiogram data in the case that the network state is abnormal.

The invention provides a self-adaptive R-wave detection method and device based on multi-channel electrocardiosignals. The method comprises the steps that the multi-channel electrocardiosignals are obtained; performing sub-channel preprocessing on the multi-channel electrocardiogram signal to obtain a preprocessed multi-channel electrocardiogram signal; performing inter-channel time alignment on the preprocessed multi-channel electrocardiogram signal to obtain an aligned multi-channel electrocardiogram signal; based on signal quality evaluation, performing weighted fusion on the aligned multi-channel electrocardiogram signals to generate a fusion signal; carrying out adaptive R-wave detection on the fused signal to obtain a preliminary R-wave position sequence; and based on the preliminary R-wave position sequence, performing multi-channel fusion positioning verification on an original multi-channel signal, and outputting a final R-wave position sequence. According to the method, the robustness and the accuracy of R wave detection in the pathological electrocardiogram are improved through alignment, quality evaluation weighting and fusion of the multi-channel signals.