ECG Signal Analysis for Patient with Metabolic Syndrome based on 1D-Convolution Neural Network

Abstract

Metabolic syndrome (MetS) is a cluster of metabolic disorders associated with medical conditions: abdominal obesity, high blood pressure, insulin resistance, etc. People with MetS have a higher risk of cardiovascular diseases and type 2 diabetes mellitus. Hence, early detection of MetS can be useful in the field of healthcare. In this paper, we propose a 1D-Convolution Neural Network (1D-CNN) model for classifying the electrocardiogram (ECG) signals of the GBBANet online database into two classes: a group of people with the medical condition (MetS [n=15]) and a control group (CG [n=10]). The dataset consists of 5 ECG recordings per person. The proposed 1D-CNN model has achieved an overall accuracy of 88.32%.