12 results about "ECG feature" patented technology

The invention discloses a portable state analysis method and system based on multi-mode electroencephalogram and electrocardio, and belongs to the field of state analysis. The portable state analysis method comprises the steps that electroencephalogram signals and electrocardio signals of a subject are synchronously collected; performing preprocessing and feature extraction on the signals to obtain electroencephalogram features and electrocardio features; according to the values of the electrocardio characteristics in different physiological states, constructing difference value characteristics and ratio characteristics representing differences between the states; the electroencephalogram features, the electrocardio features and the new construction features are fused, and multi-modal features are obtained; and finally, inputting the fusion features into a pre-trained table priori data fitting network model, and outputting a classification result or a quantitative prediction value for representing the state of the subject. According to the method, through systematic state comparison feature engineering and an efficient table data model, the accuracy, generalization ability and automation level of multi-modal physiological signal analysis are improved, and the whole scheme is realized based on portable equipment and is suitable for state evaluation of various non-laboratory scenes.

The invention provides a small-sample multi-label electrocardiogram classification method which comprises the steps that a support set and a query set of an electrocardiogram are obtained, and each of the support set and the query set comprises a plurality of electrocardiogram signals; for each electrocardiogram signal in the support set and the query set, performing feature extraction in various different scales to obtain a plurality of extraction features corresponding to each electrocardiogram signal; carrying out attention enhancement on each extracted feature to obtain a corresponding enhanced feature, and then carrying out cross-scale fusion on all the enhanced features to obtain a fused feature map corresponding to each electrocardiogram signal; and aggregating the fusion feature maps corresponding to the support set into a class prototype, then calculating a similarity matrix between the class prototype and the fusion feature maps corresponding to the query set, and determining the class of each electrocardiogram signal in the query set according to the similarity matrix. The problem that electrocardiogram features cannot be fully extracted and accurately classified in the prior art is solved.

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.

The application discloses an ECG monitoring-oriented SNN learning accelerator, which comprises a register configuration module, an asynchronous global control module, an asynchronous neural network control module, an asynchronous binary classification CNN module, an asynchronous four-classification SNN module, an asynchronous weight update module and a memory management module. Firstly, the accelerator corresponds to a circuit to build a double-layer dynamic network, hierarchical dynamic power management is performed, and a four-classification SNN network of high-precision secondary abnormality detection is selectively activated through a binary classification CNN network of primary abnormality detection. Secondly, the accelerator supports on-chip reasoning and efficient learning, effectively eliminates the ECG feature differences of different individuals under the premise of guaranteeing the privacy and security of user data. Finally, the circuit uses a pulse neural network and an asynchronous logic circuit for control, and the power consumption is greatly reduced compared with a synchronous network.

The invention belongs to the field of machine learning, and discloses a class incremental learning method for ECG identity recognition. Comprising the following steps: preprocessing electrocardiosignals; constructing a memory bank, and introducing a similarity penalty term in sample screening based on class center approximation to ensure that finite memory samples cover the diversity of electrocardiogram waveforms; inheriting old model parameters to construct a new model, and performing joint training by using a memory bank and new data; a double constraint mechanism is introduced in the training process: 1, subspace distillation constraint: strictly aligning geometric structures of new and old model feature subspaces through SVD (singular value decomposition), Grassmann distance and KL divergence; and 2, new and old category separation constraint: utilizing self-attention to enhance prototype features and maximize the minimum distance from a new category to an old category prototype set. According to the method, the problems of disastrous forgetting and category confusion caused by ECG feature drift are effectively solved, and the recognition accuracy in a continuous learning scene is remarkably improved.

The invention relates to a transfer learning driving electrocardio double-state judgment and personalized course optimization system. According to the system, accurate judgment of the attention state in a learning scene is achieved by constructing a specific mapping model of electrocardio rhythm features and the attention state; based on attention early warning features in the electrocardiogram time sequence data, an attention load pre-judgment model is established, and prospective adaptation of a course is achieved; meanwhile, the system is optimized in real time by using an individual electrocardio baseline template library and an edge end, an attention judgment threshold is dynamically calibrated, and individual difference and scene interference are avoided. According to the technical scheme, through electrocardiosignal real-time monitoring and course dynamic generation, accurate perception of learning attention and personalized course optimization are achieved, the learning efficiency and adaptability are effectively improved, and the problems that a traditional course adjustment mode is single, and electrocardiosignal features are not fully used for learning cognitive assessment are solved.

This application proposes a classification model, construction method, and application of myocardial ischemia based on 12-lead ECG. By detecting the ST-T segment beat-by-beat changes using entropy domain, frequency domain, and Lyapunov domain analysis methods, spatiotemporal ECG feature parameters related to myocardial ischemia are obtained. A machine learning model for predicting myocardial ischemia is established, ultimately achieving optimal ECG feature selection related to myocardial ischemia. This addresses the problem that conventional 12-lead ECGs often lack clear ST segment or T wave features related to myocardial ischemia, severely affecting the sensitivity and accuracy of myocardial ischemia diagnosis. The method includes: acquiring a 12-lead ECG; calculating the ST-T segment sample entropy of the 12-lead ECG; converting the 12-lead ECG into a 3-lead ECG vector map; extracting the ST-T segment from the 3-lead ECG vector map and calculating its spatial and temporal feature values; and using a grid search method to select the optimal ECG features reflecting myocardial ischemia. Therefore, the sensitivity and accuracy of myocardial ischemia diagnosis are improved.

The invention discloses a biomedical signal noise reduction method and device based on multi-dimensional information fusion and a medium, and relates to the technical field of biomedical signal processing.The method comprises the steps that an original ECG signal, an original ACC signal and an original GYRO signal of a target user in a dynamic electrocardiogram monitoring scene are preprocessed; performing layered noise reduction on the preprocessed first ECG signal; and according to the preprocessed first ACC signal and the first GYRO signal, carrying out artifact suppression on ECG features extracted from the second ECG signal subjected to layered noise reduction, inversely mapping the target ECG features subjected to artifact suppression to the space of the second ECG signal, and determining a target ECG signal which is high in signal-to-noise ratio and contains a complete P-QRS-T wave group. According to the method and the device, the cooperative suppression of various noise types in dynamic electrocardiogram monitoring is realized, the clinical diagnosis value of the ECG signal is guaranteed, and the noise reduction effect and the signal availability rate of the ECG signal in a dynamic scene are improved.

This invention provides a small-sample, multi-label electrocardiogram (ECG) classification method, comprising: acquiring a support set and a query set of ECGs, each including multiple ECG signals; for each ECG signal in the support set and query set, performing feature extraction at multiple different scales to obtain multiple extracted features corresponding to each ECG signal; performing attention enhancement on each extracted feature to obtain corresponding enhanced features, and then fusing all enhanced features across scales to obtain a fused feature map corresponding to each ECG signal; aggregating the fused feature maps corresponding to the support set into class prototypes, then calculating the similarity matrix between the class prototypes and the fused feature maps corresponding to the query set, and determining the category of each ECG signal in the query set based on the similarity matrix. This invention solves the problem in existing technologies that cannot fully extract ECG features and perform accurate classification.

This application relates to a supervised pre-training-based multimodal electrocardiogram (ECG) signal representation learning method. The method includes: entity extraction and standardized mapping of clinical text reports to obtain structured diagnostic labels; extraction of ECG features from raw ECG data through cross-channel slicing and routing aggregation using a multi-granularity ECG encoder; inputting the structured diagnostic labels and ECG features into a multimodal fusion network to complete text semantic extraction and cross-modal interaction, obtaining fused features; constructing modality consistency loss and classification loss to jointly optimize model parameters; and inputting the ECG data to be tested into the optimized model to complete diagnostic prediction. This method can fully exploit the value of clinical text, achieve fine-grained alignment of cross-modal features, reduce the computational complexity of long sequences, and take into account the multi-scale features of ECG signals, effectively improving the accuracy, robustness, and generalization ability of ECG analysis models.

The invention provides a single-lead electrocardio arrhythmia detection method based on multi-view information decoupling and information bottleneck distillation, which comprises the following steps of: S1, acquiring a multi-lead electrocardio signal and a single-lead electrocardio signal to obtain standardized electrocardio data; s2, constructing a multi-view teacher model, inputting the multi-lead electrocardiosignals as different views, and extracting multi-view electrocardiograph feature representation; s3, carrying out information decoupling on the multi-view electrocardio characteristics; s4, performing feature extraction on the single-lead electrocardiosignals, and migrating consistent information representation and view private information representation to a single-lead student model; s5, adopting a residual self-attention mechanism to carry out adaptive fusion on the information representation to obtain a minimum sufficient feature representation; and S6, outputting a corresponding arrhythmia detection result. On the premise of ensuring light weight of the model, the accuracy and robustness of single-lead electrocardio arrhythmia detection are effectively improved, and the method is suitable for long-term continuous monitoring and intelligent diagnosis application scenes of wearable electrocardio monitoring equipment.

Methods and systems for electrocardiogram (ECG) interpretation based on longitudinal medical data are presented herein. In one example, a method includes, during a development phase of an ECG analysis model, generating a set of longitudinal ECG features (306) from a plurality of electrocardiograms (ECGs) with known diagnostics; extracting a longitudinal criterion from the plurality of ECGs (410); during a deployment phase of the ECG analysis model, obtaining a plurality of ECGs (502, 506) of the patient, where the plurality of ECGs includes a current ECG and one or more historical ECGs; determining a diagnosis (510) based on the longitudinal criterion; and sending the diagnosis to the user device (514).