ECG Signal Classification Using Image-Based CNN Deep Learning

2026-05-22 · Source: Deep Learning on Medium · Field: Health & Wellbeing — Medical Devices & Health Technology, Clinical Care & Medical Practice, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

This article explores ECG signal classification using image-based Convolutional Neural Networks (CNNs), contrasting it with traditional signal processing methods that rely on manual feature extraction. It details how deep learning, particularly CNNs, can automatically learn patterns from raw ECG signals or their image representations. Three main strategies for CNN-based ECG analysis are presented: direct "1-D CNN" application, conversion to "2-D waveform images", and transformation into time-frequency images like "Continuous Wavelet Transform (CWT) scalograms". The content includes a practical MATLAB implementation, demonstrating the conversion of 162 labeled ECG recordings from the PhysioNet database (96 ARR, 30 CHF, 36 NSR) into 224x224 pixel CWT scalogram images, structured into class-specific folders for subsequent CNN training. It emphasizes the role of the `cwt` function and parameter choices in generating these visual inputs.

Key takeaway

For Machine Learning Engineers developing cardiac diagnostic tools, converting raw ECG signals into "2-D scalogram images" for "CNN classification" offers a powerful alternative to traditional feature engineering. You should consider employing established "CNN architectures" like ResNet or EfficientNet with "CWT-generated images". Experiment with CWT parameters and colormaps to optimize feature representation, potentially improving diagnostic accuracy for conditions like arrhythmia or congestive heart failure.

Key insights

ECG signals can be effectively classified for cardiac abnormalities by transforming them into "2-D scalogram images" for CNN deep learning.

Principles

• Deep learning automates feature discovery from ECG data.
• Time-frequency analysis captures non-stationary ECG patterns.
• CNNs excel at visual pattern recognition in ECG images.

Method

Convert "1-D ECG signals" into "2-D CWT scalogram images" (e.g., 224x224 pixels). Organize images by class into folders. Use these images as input for CNN architectures like ResNet or EfficientNet for classification.

In practice

• Use PhysioNet databases for ECG deep learning training.
• Apply `cwt` in MATLAB to generate ECG scalograms.
• Experiment with CWT parameters and colormaps.

Topics

• ECG Signal Classification
• Convolutional Neural Networks
• Deep Learning
• Continuous Wavelet Transform
• Scalogram
• PhysioNet Database
• Cardiac Arrhythmia

Best for: Machine Learning Engineer, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.