ECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset

2026-04-17

AI Summary

This study addresses the application of automated electrocardiogram (ECG) classification in cardiovascular disease diagnosis by systematically evaluating the performance of various machine learning and deep learning models on real-world clinical data. Leveraging the PTB-XL dataset, the authors utilize raw 12-lead ECG signals augmented with stationary wavelet transform (SWT) and compare multiple models—including decision trees, random forests, logistic regression, a simple CNN, LSTM, and a newly proposed complex CNN architecture named ECG-Lens. Experimental results demonstrate that ECG-Lens achieves an accuracy of 80% and a ROC-AUC of 90% without requiring manual feature extraction, significantly outperforming all other evaluated methods. This work establishes ECG-Lens as a new high-performance benchmark for end-to-end automated ECG classification.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Humans and AI: Human-in-the-loop Machine Learning
• Natural Language Processing: Text Classification & Sentiment Analysis

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compares three traditional machine learning algorithms (Decision Tree Classifier, Random Forest Classifier, and Logistic Regression) and three deep learning models (Simple Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Complex CNN (ECGLens)) for the classification of ECG signals from the PTB-XL dataset, which contains 12-lead recordings from normal patients and patients with various cardiac conditions. The DL models were trained on raw ECG signals, allowing them to automatically extract discriminative features. Data augmentation using the Stationary Wavelet Transform (SWT) was applied to enhance model performance, increase the diversity of training samples, and preserve the essential characteristics of the ECG signals. The models were evaluated using multiple metrics, including accuracy, precision, recall, F1-score, and ROC-AUC. The ECG-Lens model achieved the highest performance, with 80% classification accuracy and a 90% ROC-AUC. These findings demonstrate that deep learning architectures, particularly complex CNNs substantially outperform traditional ML methods on raw 12-lead ECG data, and provide a practical benchmark for selecting automated ECG classification models and identifying directions for condition-specific model development.

Problem

• ECG classification
• cardiovascular diseases
• PTB-XL dataset
• automated diagnosis
• 12-lead ECG

Innovation

• ECG-Lens
• Complex CNN
• Stationary Wavelet Transform
• Raw ECG Classification
• PTB-XL Benchmark