ECG-based heartbeat classification for arrhythmia detection: a step-by-step AI exploratory process

Abstract

This document presents the thesis of “ECG-based heartbeat classification for arrhythmia detection: A step-by-step AI Exploratory Process” for the degree of Master in Computer Science at Tecnológico de Monterrey. One of the biggest causes of death around the world (including third and first world countries) are Cardiovascular Diseases. Arrhythmia is one of those diseases in which the heart beats at an inconsistent and abnormal rhythm due to a malfunction in the electrical system of the heart. The detection, diagnosis, and classification are very challenging tasks for doctors as time is a crucial factor on the table. If it is not done in time, the patient’s life can be at risk. This proposal explores different Data Pre-processing and Feature Generation techniques to create an efficient and accurate binary classification model capable of distinguishing normal from abnormal heartbeats with an Accuracy and Sensitivity ranging in the 80-90% with a 10% increase when compared to a RAW feature vector. One of the most important ideas discussed throughout this thesis includes decomposing the ECG signal in Frequency and Time domains usingDual Tree Complex Wavelet Transform to create a Feature Vector. Another important highlight of this thesis is database manipulation, including the exclusion and the correct distribution of subjects across the training and testing sets. The approach aims to test the feature vectors by training different Supervised Learning Models including K Nearest Neighbours, Random Forest, and X-Gradient Boosting. We will be using the MIT-BIH Arrhythmia Database for the experimentation process.