How to Perform ECG Image Classification Using Machine Learning: A Step-by-Step Guide

Introduction

Electrocardiogram (ECG) signals are crucial for diagnosing various heart-related conditions. By using machine learning models, we can automate the classification of ECG images, significantly improving diagnostic accuracy and efficiency. This detailed guide will walk you through the entire process of ECG image classification using multiple ML algorithms.

Problem Definition and Objective

The main goal of this project is to classify ECG images into five categories: Normal, Abnormal Heartbeat, history of Myocardial Infarction (MI), active Myocardial Infarction (MI) and COVID-19 abnormalities. Machine learning model can automate this process thus reducing diagnostic errors and speeding up the analysis.

Key Objectives:

• Preprocess and prepare ECG images for model training.

• Build a machine learning model to classify ECG signals accurately.

• Optimize the model to prioritize metrics like recall, given the life-critical nature of the problem.

Project Pipeline

1. Getting Data: For this project, dataset of 12-lead ECG images can be downloaded by downloading this data. Here we have 1937 distinct patients ECG images classified into 5 categories as defined in problem statement.

2. Exploratory Data Analysis: This step involved analyzing the percentage images present in each categorize and visualizing images present. 

3. Data preprocessing: In this step we resize the pixel size and flatten it into 1-D array for the upcoming processes.

4. Splitting train and test set: We will split the data into a training set and a test set for model evaluation purposes. 

5.Building and training machine learning model: We will use ML algorithms such as SVM, Random Forest, Decision Tree and build architecture of Convolutional Neural Network and set the necessary parameters, followed by training the model.

6.Model testing: The model will be tested by calculating its predictions on the test data.

7.Model evaluation and visualizations: The model will be assessed based on its predictions on the test data, and the results will be analyzed. Additionally, the training and validation error will be visualized after each epoch of model training.

8. Saving the model: By saving the model we can use it on other systems as well.

"If you want to see the jupyter notebook follow this link to my GitHub Repository"

Dataset Preparation

The dataset I used contained ECG images labeled as Normal, MI, or other classes. You can find the dataset here. This dataset contains 1937 ECG images for distinct patients. 

Steps to Load and Inspect the Data:

1. Download the dataset and organize it into folders (e.g., train, test, and validation). Ensure the data is labeled correctly.

2. Visualize the images to confirm their quality and check for any anomalies or inconsistencies in labeling.

Preprocessing the Data

Preprocessing ensures that the data is clean and ready for model training. Proper preprocessing not only improves model performance but also ensures that the results are reproducible.

Resizing, Normalizing and Augmenting Images 

ECG images need to be resized to the make sure uniformity in this project we have resized images into 128x128. After resizing we can do normalization but that can lead to data leakage so I have first done splitting into train and test and then did the normalization using MinMax scaler. Image augmentation(rotating, flipping vertically or horizontally) is also an essential task to complete. By augmentation model don't overfit and can understand the pattern more effectively. 

Choosing Initial ML Models

Before diving into CNNs, I explored traditional machine learning models such as Support Vector Machines (SVM), Decision Trees, and Random Forests. While these models performed decently, they struggled with recall, which is an important metric especially in identifying critical cases like Myocardial Infarctions.

Accuracy Comparison of ML models

Building a Custom CNN Model

To address the limitations of traditional machine learning (ML) models, I built a custom Convolutional Neural Network (CNN) from scratch. This model was specifically tailored for ECG image classification and significantly improved recall, demonstrating the power of deep learning in healthcare applications.

Why Choose CNN for Image Classification?

Convolutional Neural Networks (CNNs) are a powerful choice for image classification tasks because they can automatically learn and process spatial features from images. Here’s why I chose a CNN for my project:

1. Automatic Feature Learning: Unlike traditional machine learning models that rely on manually crafted features, CNNs can extract relevant features directly from raw images. This not only saves time on preprocessing but also improves the model’s performance by capturing subtle patterns.

2. Layered Learning: CNNs analyze images in a layered manner, starting with simple patterns like edges in the early layers and progressing to more complex features, such as shapes or objects, in the deeper layers. This step-by-step approach mirrors how humans recognize visual details and contributes to greater classification accuracy.

3. Adaptability to Position Changes: CNNs are resilient to variations in object placement within an image thanks to pooling layers, which reduce the size of feature maps. This quality is especially important in medical imaging, where abnormalities can appear in different locations.

4. Proven Performance: Research and practical applications consistently show that CNNs outperform traditional models in image-related tasks. This makes them a reliable option for projects like ECG analysis or other medical imaging challenges.

Defining the Model Architecture

Here’s how I structured my CNN using Keras:

Compiling CNN model

Rescaling X_test into original dimension and Getting predictions

Visualizing Accuracy curve for Training and Testing Dataset

Saving the model

After training the model, it's essential to save it for future use or deployment. You can do this easily with Keras: This command saves the entire model architecture along with its weights and training configuration into a single HDF5 named ecg_cnn_model.h5

# Save the model model.save('ecg_cnn_model.h5')

Loading the Model

To load the saved model later for inference or further training:

from keras.models import load_model # Load the saved model loaded_model = load_model('ecg_cnn_model.h5')

Conclusion

Machine learning has revolutionized how we approach complex problems in healthcare. This project showcases its transformative power in classifying ECG data into distinct categories—turning raw data into actionable insights.
As we move forward, it's essential to continue improving these models by incorporating domain expertise and ensuring their ethical application in real-world scenarios.