Work

Deep Transfer Learning for Detection of Atrial Fibrillation Using Holter ECG Color Maps

Year: 2024

Type: conference-paper

Abstract: This study aims to explore the potential of deep transfer learning of pretrained image recognition deep neural networks (DNN) in a new diagnostic modality of atrial fibrillation (AF) using Holter ECG color maps.These maps consist of stacked color bars, transforming the ECG waveform amplitude of sequential heartbeats over 30 s into a color code, similar to ECHOView TM (Schiller AG, Switzerland).Using 13,536 training, 6,624 validation and 11,400 test ECHOView color maps evenly split between AF and non-AF cases from the IRIDIA-AF Holter ECG monitoring database, we retrained, fine-tuned, and tested 18 image classification DNNs.Test accuracy of retrained DNNs (93.1-95.8%)was improved after fine-tunning (96.3-97.6%).Retrained EfficientNetV2B1 and finetuned MobileNetV2(α=1.4) were both accurate and fast.ECHOView colormap images, composed of stacked heartbeat amplitudes, provide an interpretable visual modality for pretrained image classification networks that can be effectively retrained and fine-tuned to identify short AF episodes in Holter-ECG recordings.

Source: Computing in cardiology

DOI: 10.22489/cinc.2024.283

Authors: Todor Stoyanov, Vessela Krasteva, Stefan Naydenov Naydenov, Ramun Schmid, Irena Ilieva Jekova

Institutions: Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, Medical University of Sofia

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: Bulgarian National Science Fund