7 results about "Arrhythmia detection" patented technology

This invention discloses a real-time arrhythmia detection method based on morphological fidelity consistency constraints, comprising the following steps: S1, preprocessing continuous electrocardiogram (ECG) signals and dividing them into overlapping sliding time windows; S2, inputting each time window into an ECG signal encoder obtained through joint training to obtain a latent representation vector for that time window; the joint training is specifically defined as: simultaneously optimizing the encoder parameters using supervised classification signals, contrast consistency signals, and morphological fidelity signals during the training phase; S3, inputting the latent representation vector into a classifier and outputting the class probability of the time window belonging to each arrhythmia category; S4, based on the class probability, outputting real-time detection results using an online decision strategy of threshold hysteresis and cross-window consistency. This invention can simultaneously achieve robust identification under dynamic interference, accurate preservation of clinically critical waveform details, and efficient real-time deployment at the edge. This invention also provides a real-time arrhythmia detection system and device based on morphological fidelity consistency constraints.

A cardiac treatment device, including:stimulation circuitry configured to generate a non-excitatory electrical signal which, when applied to ventricular tissue during a ventricular refractory period thereof improves a condition of heart failure in human patients;atrial arrhythmia detection circuitry; anddecision circuitry which controls the stimulation circuitry to delivery said signal, also when said atrial arrhythmia detection circuitry detects an atrial arrhythmia.

The present application relates to the technical field of non-contact vital sign monitoring, and specifically discloses a heart signal detection method based on feature decoupling millimeter wave radar based on contrast learning. The present application realizes the automatic separation of the rhythm feature and the morphological feature of the heart signal from the millimeter wave radar signal by constructing an adaptive filtering module, a sample generator, a multi-scale feature extraction module, a double-path feature decoupling module, a physical constraint module and a contrast learning loss function. The present application uses contrast learning to construct a positive sample pair, guides the model to learn a feature representation that is invariant to heart rate changes and waveform deformation, and combines physiological prior constraints to improve the robustness and interpretability of the features. The present application effectively solves the shortcomings of traditional methods in signal separation, noise suppression and generalization ability, and can be widely applied to heart rate variability analysis, arrhythmia detection and other heart health monitoring tasks.

A medical device is configured to sense one or more cardiac electrical signals and sense a ventricular event signal from the one or more cardiac electrical signals. The medical device can determine that the one or more cardiac electrical signals satisfy a tachyarrhythmia detection criterion, and in response to satisfying the tachyarrhythmia detection criterion, apply a delay criterion to the one or more cardiac electrical signals sensed during each of a plurality of groups of a plurality of sensed ventricular event signals. In response to satisfying the detection delay criterion, the medical device can delay detection of a tachyarrhythmia.

The application discloses an arrhythmia detection method based on cross-modal data enhancement. In view of the problems of serious imbalance of class distribution of existing ECG data sets and limited effect of multi-modal feature fusion, the method comprises the following steps: db6 wavelet denoising and heartbeat segmentation are performed on the original electrocardiogram signal, and categories are merged according to the AAMI standard; a one-dimensional heartbeat time sequence signal is converted into a two-dimensional polyline waveform image with a pixel size of 224*224, and a metadata CSV file containing signal indexes, image paths and category labels is constructed; signal data and images are sequentially matched sample by sample, and a multi-modal paired data set is constructed; an intra-class multi-modal reorganization (ICMR) enhancement strategy is proposed, signal and image are independently and randomly sampled from the same category sample pool under the constraint of maintaining category consistency, and are re-paired, and the imbalance problem of categories is relieved through differential amplification rate; a double-flow multi-modal fusion classification network composed of a one-dimensional CNN-bidirectional LSTM signal encoder, a ResNet18 image encoder, a gating fusion module and a classification head is constructed, and two-way features of signal and image are adaptively integrated; cross-entropy loss function and Adam optimizer are used for end-to-end training and evaluation. The application effectively improves the recognition performance of the minority class arrhythmia.

Techniques are disclosed for using both characterization and machine learning to detect cardiac arrhythmias. A computing device receives a patient's electrocardiogram data sensed by a medical device. The computing device obtains a first classification of the patient's arrhythmia through feature-based characterization of the electrocardiogram data. The computing device applies a machine learning model to the received electrocardiogram data to obtain a second classification of the patient's arrhythmia. As one example, the computing device uses the first and second classifications to determine whether an arrhythmia episode has occurred in the patient. As another example, the computing device uses the second classification to verify the patient's first classification of the arrhythmia. The computing device outputs a report indicating that an arrhythmia episode has occurred and one or more cardiac features consistent with the arrhythmia episode.