公开(公告)号：US20230404461A1

公开(公告)日：2023-12-21

申请号：US18329855

申请日：2023-06-06

申请人： Tata Consultancy Services Limited

发明人： VARSHA SHARMA ,  AYAN MUKHERJEE ,  MURALI PODUVAL ,  SUNDEEP KHANDELWAL ,  ANIRBAN DUTTA CHOUDHURY ,  CHIRAYATA BHATTACHARYYA

IPC分类号： A61B5/346 ,  A61B5/00 ,  G06T1/00

CPC分类号： A61B5/346 ,  A61B5/7267 ,  G06T1/00 ,  G06T2207/20081 ,  G06T2207/20084

摘要： State of art techniques hardly provide data balancing for multi-label multi-class data. Embodiments of the present disclosure provide a method and system for identifying cardiac abnormality in multi-lead ECGs using a Hybrid Neural Network (HNN) with fulcrum based data re-balancing for data comprising multiclass-multilabel cardiac abnormalities. The fulcrum based dataset re-balancing disclosed enables maintaining natural balance of the data, control the re-sample volume, and still support the lowly represented classes there by aiding proper training of the DL architecture. The HNN disclosed by the method utilizes a hybrid approach of pure CNN, a tuned-down version of ResNet, and a set of handcrafted features from a raw ECG signal that are concatenated prior to predicting the multiclass output for the ECG signal. The number of features is flexible and enables adding additional domain-specific features as needed.