Electrocardiogram Machine Learning

Electrocardiogram machine learning research progress.
created: modified: status: in-progress reading time: 2 mins

This post will be continuously updated throughout the Master’s degree research period.

The task is to extract utility from a large corpus of electrocardiogram (ECG) data using deep learning (DL) and machine learning (ML). Data is in various formats (paper scans, physiobank WFDB, etc.) Dataset may be partially labelled.

Introduction

Electrocardiograms are graphs of voltage over time, indicating the electrical activity of the heart. These measurements are recorded by placing electrodes on the patient’s skin. ECGs can be used to detect abnormalities in the heart, like cardiac arrhythmia, coronary artery disease, and heart attacks.

Wave Schematic

See “Learning the PQRST EKG Wave Tracing”.

ECG Lead Placement

A conventional 12-lead ECG has ten electrodes placed on the patient’s limbs and chest. Six leads are placed on the chest, surrounding the heart, one lead is placed on each of the individual’s four limbs.

Reading an ECG

Following information is derived from “How to read an ECG”.

Heart Rate

Related Work

Links provided to online articles, datasets, source code, misc. resources.

Datasets

Articles

Summary papers

ECG Analysis