Auf einem Generativen Kontradiktorischen Netzwerk-Ensemble Basierte Simulation von Kardiovaskulären Krankheitsspezifischen Biomedizinischen Signalen

Details

Veröffentlichungs-Nr.

EP4085838

Aktenzeichen

EP21195209

Anmeldetag

7. September 2021

Veröffentlichung

9. Juli 2025

Erteilung

9. Juli 2025

Rechtsraum

EP

IPC

A61B5/024A61B5/327A61B5/352A61B5/361G06N3/044G06N3/045G06N3/047G06N3/048G06N3/084G06N3/088G16H50/20G16H50/70A61B5/319G16H50/50G06N3/082

Abstract

Computer-aided diagnosis algorithms require a large volume of training data. The existing methods for simulating artificial biomedical signals are mostly based on physics driven mathematical models that require too many assumptions, making them challenging to simulate on a large scale. Alternatively, conventional deep learningbased approaches are pure data driven and hence, do not have physiological interpretation. The present disclosure provides a method that effectively combines both physiological domain knowledge and deep learning to enable simulation of realistic cardiovascular disease specific biomedical signals. An ensemble Generative Adversarial Network (GAN) including a Long Short-Term Memory GAN (LSTM-GAN) configured to generate a Heart Rate Variability (HRV) pattern associated with the cardiovascular disease condition and a Deep Convolutional GAN (DCGAN) configured to create a morphology of a representative cardiac cycle is provided. A complete waveform is simulated by combining an output from each GAN.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete