Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity.

ABSTRACT:

Introduction

Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting cardiac abnormalities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early improves the quality and efficiency of medical care.

Methods

The paper presents various modern approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincaré representation of ECG signal and deep-learning-based image classifiers. Additionally, the raw signals were processed with the one-dimensional convolutional model while the XGBoost model was facilitated to predict based on the time-se

SUBMITTER: Pham H 

PROVIDER: S-EPMC10424727 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature