Model-based Feature Engineering of Atrial Fibrillation

Atrial Fibrillation (AF) is the most common tachyarrhythmia in the heart. Irregular RR intervals and the absence of a P wave before the QRS complex characterize AF. Although many studies have been done to detect atrial fibrillation, many aspects of this intricate disease need further analysis. AF is often diagnosed by the interpretation of multi...

Using our tools backwards, AF detection by confusing time and frequency

Atrial Fibrillation or AF is the most common heart rhythm anomaly affecting millions of people. This work explores the possibilities of reinterpreting speech processing techniques for use in atrial fibrillation detection. An existing method of modelling single heartbeat, single lead ECG signals by means of an ARMA model's amplitude response as a...

Energy-efficient Spike Encoding for ECG Arrhythmia Classification

Cardiovascular diseases (CVDs) are the top cause of death worldwide, and their diagnosis can be quickly and painlessly achieved through Electrocardiogram (ECG). The diagnosis of electrocardiogram has gradually evolved from manual diagnosis by doctors to one that can be realized using Artificial Intelligence (AI). Early AI still required manual...

ImECGnet: Cardiovascular Disease Classification from Image-Based ECG Data Using a Multi-branch Convolutional Neural Network

Reliable Cardiovascular Disease (CVD) classification performed by a smart system can assist medical doctors in recognizing heart illnesses in patients more efficiently and effectively. Electrocardiogram (ECG) signals are an important diagnostic tool as they are already available early in the patients’ health diagnosis process and contain...

Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation

Background and objective: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the...