Atrial Fibrillation Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Atrial Fibrillation ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year 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 Atrial Fibrillation Projects
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Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria
This project studies how deep learning can detect heart problems from ECG signals. The researchers used a pre-trained neural network and applied explainable methods to see what the model learned. They analyzed which parts of the heart signals influenced the predictions the most. The results show that the model learned features similar to what doctors use in practice. -
Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring
This project focuses on using wearable device signals to detect heart problems. It studies PPG data, which can be measured continuously and easily by consumer devices. The goal is to find unusual patterns in PPG that indicate when an ECG test should be taken. The proposed method uses a deep learning model to accurately predict these heart abnormalities.
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