DETECTION OF ACTIVATION IN ELECTROGRAMS USING NEURAL-NETWORK-TRAINED PREPROCESSING OF INTRACARDIAC ELECTROGRAMS

• Main Authors: KIRYASOV ANDREY EVGENYEVICH, RAVUNA ELIYAHU, KATZ NATAN SHARON, SHOVKUN ALEKSEY VLADIMIROVICH, KUZHNAREVA ELENA IGOREVNA
• Format: Patent
• Language: Chinese; English
• Published: 22.02.2022
• Subjects: DIAGNOSIS; HUMAN NECESSITIES; HYGIENE; IDENTIFICATION; MEDICAL OR VETERINARY SCIENCE; SURGERY

The invention provides the detection of activation in electrograms using neural-network-trained preprocessing of intracardiac electrograms. A method includes collecting a plurality of bipolar electrograms and respective unipolar electrograms of patients, the electrograms including annotations in which one or more human reviewers have identified and marked a window-of-interest and one or more activation times inside the window-of-interest. A ground truth data set is generated from the electrograms, for training at least one electrogram-preprocessing step of a Machine Learning (ML) algorithm. The ML algorithm is applied to the electrograms, to at least train the at least one electrogram-preprocessing step, so as to detect an occurrence of an activation in a given bipolar electrogram within the window-of-interest. 本发明题为"使用心内电描记图的受过神经网络训练的预处理检测电描记图中的激活"。本发明公开了一种方法，该方法包括采集患者的多个双极性电描记图和相应的单极性电描记图，所述电描记图包括注释，其中一个或多个人类审查者已识别并标记感兴趣窗口和所述感兴趣窗口内的一个或多个激活时间。根据所述电描记图生成真实标签数据集，以用于训练机器学习(ML)算法的至少一个电描记图预处理步骤。将所述ML算法应用于所述电描记图，以