Identifizierung von Herzanomalien in Mehrelektroden-Ekgs unter Verwendung eines Hybriden Neuronalen Netzwerks mit Drehpunktbasiertem Datenneuausgleich

Details

Veröffentlichungs-Nr.

EP4292535

Aktenzeichen

EP23177479

Anmeldetag

6. Juni 2023

Veröffentlichung

23. Oktober 2024

Erteilung

23. Oktober 2024

Rechtsraum

EP

IPC

A61B5/346A61B5/00

Abstract

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.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete