Multi-Label Heart Disease Classification Using Electrocardiograms and Machine Learning

Abstract

Heart disease is at present the leading cause of death globally, and the urgent need exists for accurate, scalable diagnostic tools in a timely manner. Electrocardiograms (ECGs) are a non-invasive means of quickly and easily recording the electrical action of the heart. This enables the determination of abnormalities resulting from myocardial infarction, conduction disorders, hypertrophy, and rhythm disorders. However, manual ECG interpretation is often slow, subjective, and can be misclassified, particularly when minor variations in waveforms are considered. Machine learning provides a powerful framework for automatic analysis of ECG signals with improved diagnostic coherence and the identification of complex patterns that might not be easily identified by a clinician. In this project we develop an automated machine learning pipeline for multi-label heart disease classification using the PTB-XL dataset, which consists of 21,799 annotated 12-lead ECGs from patients with various heart diseases. Each ECG was preprocessed and segmented to identify PQRST components prior to feature extraction.Then, 132 clinically meaningful features (such as PR Ratio and QRS Energy) were extracted that describe both morphological and temporal characteristics of cardiac cycles. To this end, we consider six diagnostic heart conditions: NORM, IMI, ASMI, LVH, NDT, and LAFB, each of which corresponds to a label in our machine learning classifiers. In this work, we used four baseline traditional machine learning models: Logistic Regression, Random Forest, Support Vector Machine, and k-Nearest Neighbors, and three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory, and a CNN + BiLSTM hybrid architecture. According to our experiments, CNN trained on raw ECG signals, though with relatively long training time, has yielded the best overall performance on the test set among all models, showcasing superb discriminative capability to classify different cardiac conditions.