Arrhythmia Detection projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Arrhythmia Detection projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Arrhythmia Detection Projects
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Comparative Analysis of Machine Learning Algorithms With Advanced Feature Extraction for ECG Signal Classification
The project aims to classify ECG heartbeat signals to help detect heart conditions early. It focuses on using machine learning methods to analyze ECG data efficiently and accurately. The MIT-BIH arrhythmia dataset is used, grouped into five main beat types. Different classifiers are tested, and Random Forest achieves the best performance. The work highlights automated ECG analysis as a tool for clinical and remote monitoring applications. -
Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification
This project focuses on building a smart system to monitor heartbeats and detect irregular heart patterns. It uses a new deep learning method to identify abnormal beats more accurately. The system improves learning by balancing data and paying more attention to important heartbeat features. Tests show it works well on real heartbeat data and can help detect arrhythmias effectively.
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