5 results about "Surface ecg" patented technology

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.

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.

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