Real-Time ECG Arrhythmia Classifier Kit: Turn Your Arduino Portenta H7 into a Medical AI Device

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Step into the frontier of edge AI and medical electronics with this intensive project kit. You’ll build a portable, battery-operable device that acquires high-fidelity ECG signals at 500 Hz using the AD8232 front-end, then runs a TensorFlow Lite 1D convolutional neural network on the Arduino Portenta H7’s M7 core to classify Normal Sinus Rhythm, Atrial Fibrillation, and Premature Ventricular Contractions — all in real time, without cloud connectivity. The M4 core simultaneously refreshes a vivid TFT waveform display and triggers an audible alarm on detecting arrhythmia, while logging timestamped episodes to an SD card. By the end, you’ll hold a compact, clinically-inspired prototype ready for academic demonstration or rapid health-tech validation.

What You'll Build

You’ll assemble a self-contained ECG classifier that continuously monitors a single-lead heart signal, rendering the PQRST waveform and rhythm label on a 2.4‑inch ILI9341 screen. The device sounds a buzzer the instant AFib or PVC is identified, stores every event with DS3231-verified timestamps on a microSD, and powers from a standard 5‑volt supply. Everything mounts inside the supplied enclosure, giving you a rugged, lab‑ready instrument that connects via electrode patches to a real person within minutes.

What You'll Learn

• Capturing biopotential signals with the AD8232 and designing analog front‑end filters for pristine ECG acquisition
• Training, quantizing, and deploying a 1D‑CNN in TensorFlow Lite Micro for on‑device cardiac rhythm classification
• Dual‑core programming on Arduino Portenta H7: dedicating the M7 core to ML inference while the M4 core handles display, buzzer, RTC, and SD logging
• Integrating RTC, TFT, buzzer, and storage modules under real‑time constraints — the foundation of any medical embedded system

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This advanced kit targets final‑year B.Tech (ECE/EEE/BME) students building their capstone project, participants in Smart India Hackathon or similar national innovation contests, and postgraduate researchers at IIT, NIT, VIT, or BITS prototyping AI‑driven medical devices. If you already have experience with Arduino and basic machine learning, this project will elevate your skills into real‑time embedded AI for healthcare, culminating in a publication‑ready demonstrator.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.