7 results about "Electrocardiogram analysis" patented technology

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.

According to an embodiment of the present disclosure, disclosed is a method for evaluating qualities of electrocardiogram data and diagnosing a disease only with a signal having an excellent quality. Specifically, according to the present disclosure, a computing device evaluates qualities of electrocardiogram data obtained from a plurality of leads, excludes electrocardiogram data obtained from at least one lead among the plurality of leads based on the evaluation of the qualities of the electrocardiogram data, and performs electrocardiogram analysis based on remaining electrocardiogram data by using a pre-learned electrocardiogram analysis model.

The application provides a rare disease occurrence probability prediction method and device and an electrocardiogram analysis system. The method comprises the following steps: acquiring a plurality of to-be-evaluated electrocardiogram data and corresponding to-be-evaluated medical record text data; inputting the to-be-evaluated electrocardiogram data into an electrocardiogram pre-training model to obtain a target electrocardiogram feature vector; inputting the medical record text data corresponding to the to-be-evaluated electrocardiogram data into a medical text pre-training model to obtain a target text feature vector; inputting the to-be-evaluated electrocardiogram data into a preset risk detection model to obtain an electrocardiogram rare disease occurrence probability coefficient; presetting a rare disease feature dictionary; determining the occurrence probability of each rare disease according to the similarity between the target electrocardiogram feature vector and the reference text feature vector of each rare disease, the similarity between the target text feature vector and the reference text feature vector of each rare disease, and the electrocardiogram rare disease occurrence probability coefficient. The occurrence probability of the rare disease can be predicted without training a large number of electrocardiogram samples of the rare disease.

The application discloses an ECG high-frequency feature analysis method for myocardial ischemia and complex heart rate abnormalities, and belongs to the technical field of medical information processing and artificial intelligence, comprising: obtaining an ECG signal and performing preliminary classification based on a support vector machine; adopting a random forest model of a fusion model irrelevant meta-learning and a domain self-adaptive mechanism to dynamically compensate individual physiological differences; generating an individualized dynamic diagnosis threshold sequence through reinforcement learning; mining potential abnormal misjudgment modes by using a variational autoencoder and a generative adversarial network; fusing features and clarifying abnormal state boundaries by means of a graph neural network; and finally realizing gradient quantitative evaluation of the severity of myocardial ischemia through a multi-task learning model; the application effectively solves the problems of poor individual adaptability, fixed threshold and insufficient recognition of complex abnormal patterns of traditional methods, and significantly improves the accuracy, stability and clinical practicability of electrocardiogram analysis in the diagnosis of myocardial ischemia and complex heart rate abnormalities.

The application relates to a paper electrocardiogram digitization method and device, which comprises the following steps: pre-processing an electronic image of a paper electrocardiogram containing twelve lead regions to obtain an electrocardiogram image; separating each lead region of the electrocardiogram image; based on an eight-neighborhood sparse outlier removal algorithm and a pre-stored refinement algorithm, refining the electrocardiogram image of each lead region to obtain an electrocardiogram waveform curve of each lead region; and connecting the electrocardiogram waveform curves of each lead region in the lead dimension to obtain a digitization result of the paper electrocardiogram. The application converts the paper electrocardiogram produced continuously into an electronic electrocardiogram in a manner of digitizing the effective information of the electrocardiogram signal of the paper electrocardiogram, solves the problem of insufficient electronic electrocardiogram volume, and promotes the development of the automatic electrocardiogram analysis field.

The present invention discloses an analysis and identification method, system and storage medium for electrocardiograms. The method includes: constructing a concept tree based on definitions in electrocardiogram and arrhythmia knowledge, and establishing an arrhythmia electrocardiogram data set; performing data enhancement processing on the arrhythmia electrocardiogram data set based on the concept tree to construct prior data; constructing an ECG prior model based on the prior data; optimizing the ECG prior model to obtain a target identification model; and analyzing and identifying an electrocardiogram to be analyzed based on the target identification model, and determining an identification result of the electrocardiogram to be analyzed. The present invention has high accuracy and high efficiency, and can be widely used in the field of computer technologies.

Provide more effective instructions to rescuers. [Solution] An automated external defibrillator 1 performs electrocardiogram analysis and discharge processing on a subject, comprising: a detection unit 134 that detects whether or not a rescuer is in contact with the subject; and a display control unit 141 that changes the flashing cycle or lighting color of display units 102, 103, and 110 provided on the automated external defibrillator 1 according to the detection result by the detection unit 134.