ECG Arrhythmia Detection System Using AI

Problem statement

Electrocardiogram (ECG) signals are commonly used to diagnose heart conditions, but interpreting ECG waveforms requires trained cardiologists. In many areas, there is limited access to specialists, which delays diagnosis. An automated arrhythmia detection system can help flag abnormal patterns early and assist clinicians in decision making.

Abstract

This project focuses on designing an automated ECG analysis system that detects arrhythmias using AI. Raw ECG signals are collected from a sensor module or public datasets and preprocessed with filtering and segmentation. Features such as R-R intervals, QRS width and morphology are extracted, or the raw segments are fed directly into a 1D-CNN or LSTM-based deep learning model. The model classifies beats as normal or one of several arrhythmia types (e.g., PVC, AFib). A simple user interface displays ECG plots and classification results, enabling real-time or offline analysis in clinical or remote health setups.

Components required

• ECG sensor module with electrodes (AD8232 or similar)
• Microcontroller or data acquisition device
• Laptop/PC with Python for processing
• Python libraries: NumPy, SciPy, Scikit-Learn, TensorFlow/PyTorch
• ECG datasets (e.g. MIT-BIH Arrhythmia Database)
• Simple GUI (Tkinter, PyQt, or web dashboard)

Block diagram

Working

The ECG sensor picks up electrical activity of the heart and sends an analog signal to a microcontroller or data acquisition board, which forwards digitized samples to a PC. A preprocessing stage removes baseline wander and high-frequency noise using filters. The cleaned signal is segmented into heartbeats based on R-peaks. Either hand-crafted features are computed or small windows of the signal are passed into a trained ML/DL model. The classifier outputs the beat type or arrhythmia category. The UI shows live or recorded ECG signals and highlights abnormal beats, optionally raising an alert when potentially dangerous arrhythmias are detected.

Applications

• Clinical decision support tools
• Remote cardiac monitoring systems
• Wearable ECG analysis prototypes
• Academic research in biomedical signal processing