Source : MATLAB

Project Details :

Electrocardiography (ECG) is a vital non-invasive diagnostic technique used to record the electrical activity of the heart. With increasing emphasis on digital healthcare and remote diagnostics, automated and efficient ECG data handling systems are becoming crucial. This work presents a MATLAB-based Graphical User Interface (GUI) framework designed for interactive ECG waveform analysis, segment selection, image generation, and hexadecimal encoding. The system accepts standard ECG data files in .txt format, processes them for visual inspection, and provides an intuitive scrollable interface to examine long-duration signals. A region of interest can be manually selected using a resizable rectangle tool. Upon selection, the user can export the waveform as a clean image (without axis ticks, titles, or grid lines) in a standardized resolution of 256×256 pixels. To accommodate further integration with embedded systems, AI pipelines, or hardware implementations, the application allows users to convert the exported image into either grayscale or RGB hexadecimal representations. The system supports two modes: RGB HEX (outputs R.txt, G.txt, B.txt) and Grayscale HEX (outputs Grayscale.txt), where each pixel’s intensity is encoded in two-digit hexadecimal format. This dual-format capability is controlled via a dropdown menu for easy toggling. The GUI is fully compatible with MATLAB R2018a and includes legacy support by replacing newer functions (such as writematrix) with older equivalents like dlmwrite. The application provides a real-time, interactive ECG visualization platform while also serving as a data preparation tool for machine learning models, microcontroller visualization, and FPGA-based healthcare signal processing. Its ability to convert waveform data into structured visual and hexadecimal forms bridges the gap between clinical signal acquisition and computational processing. This flexible, open-ended tool is particularly beneficial for researchers working in biomedical signal processing, embedded systems, and AI-based ECG classification.

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