11 results about "Surface ecg" patented technology

The invention discloses a multi-level and multi-link electrocardiosignal classification and identification method, system and device. The method comprises the following steps: collecting a body surface electrocardiogram signal of a subject, constructing a time domain feature, a frequency domain feature and an electrocardio dynamic feature, respectively inputting the three features into a preset neural network model, and calculating the local confidence of the features; calculating the global confidence coefficient according to the topological difference of the electrocardio dynamics; and judging whether the current classification is used as a final result or not according to the relationship between the global confidence and the local confidence of the corresponding feature, or further judging by adopting the next feature. According to the method, the dynamic change of the electrocardiosignal is more comprehensively described, and the multi-dimensional information of the signal is fully utilized. The multi-level feature extraction strategy can more comprehensively represent the characteristics of the electrocardiosignals in different physiological and pathological states; and a multi-link mechanism is introduced, and cooperative verification and dynamic decision of global confidence and local confidence are utilized, so that the reliability of a classification result is remarkably improved, and misjudgment can be effectively avoided.

The invention discloses an electrocardiogram waveform prediction device, a computer program product, equipment and a medium, and relates to the technical field of data processing, and the electrocardiogram waveform prediction device comprises an information collector which is used for collecting basic information, heart information, body surface electrocardiogram signals and electrolyte information of a target object; the signal processor is connected with the information collector and is used for carrying out electrocardio nonlinear fitting through the basic information, the heart information, the body surface electrocardio signals and the electrolyte information to obtain an in-vivo target electrocardiogram waveform of a target object when the PICC tip reaches a target position; and the display is connected with the information collector and the signal processor and is used for displaying the body surface electrocardiosignal and the in-vivo target electrocardiogram waveform. After the signal processor is used for carrying out electrocardio nonlinear fitting, the in-vivo target electrocardiogram waveform of the target object when the PICC tip reaches the target position can be obtained, the in-vivo target electrocardiogram waveform is made to be matched with the target object, and accurate prediction of the electrocardiogram waveform in PICC catheterization is achieved.

The invention discloses a construction method and application of a spontaneous atrial fibrillation mouse model, and belongs to the technical field of atrial fibrillation animal models. According to the construction method, a whole-body DYNLT1 gene knockout mouse is constructed by using technologies such as gene editing and the like, the DYNLT1 gene knockout mouse is systematically evaluated through methods such as body surface electrocardiogram, echocardiography and histopathologic examination, and the DYNLT1 gene knockout mouse shows typical characteristics of atrial fibrillation in the aspects of body surface electrophysiology, atrial structure remodeling and the like. And in the molecular level, atrial muscle cell apoptosis is increased, gap junction protein expression is reduced, and inflammatory factor level is increased, so that the model is confirmed to be a spontaneous atrial fibrillation mouse model. In addition, the model shows a rapid drug reaction to the atrial fibrillation cardioversion drugs, such as amiodarone and propafenone, and has the potential for screening the atrial fibrillation drugs. The animal model provides a new pathological model for atrial fibrillation mechanism research and drug screening, and has important scientific research and clinical application values.

A system and method include a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.

A system and method for guiding medical device placement. The system includes a console, a portable acquisition unit for receiving intracavitary and surface electrocardiogram (ECG) signals, and connection hardware, including a sterile ECG adapter and cable for fluid-filled catheters or a sterile clip cable for guidewires. Processors display real-time ECG waveforms and, in response to user input, capture static snapshots at different insertion depths. These snapshots are displayed chronologically on a graphical user interface, forming a visual trajectory of the device. The system determines the device's location by detecting ECG features from each snapshot and comparing them to predetermined boundaries. A location indication is displayed for each snapshot, providing objective guidance for placement and confirmation.

A pacing signal processing method, system, and electrocardiogram (ECG) monitor collect raw surface ECG signals through high-sampling rate sampling, obtain pacing signal parameter and position information based on the sampling points, and display the pacing signal morphology and / or parameter information. The pacing signal and interference can be accurately distinguished by the pacing pulse signal's morphological repeatability, randomness of interference, and specificity of the pacing pulse signal morphology. The analyzed pacing position signal and width signal can be used for pacing smoothing of the ECG signal to remove the pacing signal from the ECG signal and prevent the pacing waveform from interfering with ECG algorithm analysis. Because the pacing smoothing process uses a pulse width appropriate to the pacing signal, the appropriate pacing smoothing width can be automatically selected based on different patient pacemakers and actual surface signal conditions to maximize the preservation of the ECG signal's effective components.

To perform cardiac diagnosis and / or arrhythmia monitoring.SOLUTION: An external heart monitoring device may include: a plurality of ECG electrodes to sense surface ECG activity; ECG processing circuitry to process the surface ECG activity to provide at least one ECG signal; a non-transitory computer-readable medium comprising a rhythm change classifier comprising at least one neural network; and at least one processor to receive the ECG signal(s), detect with the rhythm change classifier time data corresponding to a predetermined rhythm change in the ECG signal(s), determine based on the detected time data at least one ECG signal portion corresponding to the predetermined rhythm change, and transmit the at least one determined ECG signal portion to a remote computer system.SELECTED DRAWING: Figure 1A

This invention discloses a multi-level, multi-stage electrocardiogram (ECG) signal classification and recognition method, system, and device. The steps of this invention are as follows: Collecting surface ECG signals from subjects; constructing time-domain features, frequency-domain features, and electrocardiographic dynamic features; inputting these three types of features into a preset neural network model to calculate their local confidence scores; calculating the global confidence score based on electrocardiographic dynamic topological differences; and determining whether the current classification should be used as the final result, or whether to use the next feature for further discrimination, based on the relationship between the global confidence score and the local confidence scores of the corresponding features. This invention more comprehensively depicts the dynamic changes of ECG signals and fully utilizes the multi-dimensional information of the signals. This multi-level feature extraction strategy can more comprehensively characterize the properties of ECG signals under different physiological and pathological states; the introduction of a multi-stage mechanism, utilizing the collaborative verification and dynamic decision-making of global and local confidence scores, significantly improves the reliability of the classification results and effectively avoids misjudgments.

An apparatus for prediction of repeat ablation efficacy, the apparatus including an electrocardiogram device, wherein the electrocardiogram device is configured to detect post-ablation arrhythmic electrocardiogram (ECG) data representative of a post-ablation arrythmia of a patient who has previously undergone an ablation procedure and a processor configured to receive, from the electrocardiogram device, the post-ablation arrhythmic ECG data predict, using a repeat-ablation efficacy machine-learning model, a determination of a pulmonary vein reconnection by identifying features within the post-ablation arrhythmic ECG data that are historically representative of pulmonary vein reconnection determinations and transmit, for display, the determination.

A system and method include a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.

To provide a system and a method for reducing noise of intracardiac electrocardiograms using an autoencoder and for utilizing and refining intracardiac and body surface electrocardiograms using one or more deep learning training loss functions.SOLUTION: A system and a method include a memory storing a processor-executable code for a denoised autoencoder, and one or more processors coupled to the memory. The processors execute the processor-executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise.SELECTED DRAWING: Figure 1