Electrocardiography projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Electrocardiography 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 Electrocardiography Projects
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Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation
This project studies slow and deep breathing exercises to improve relaxation and heart function. It compares different breathing styles and measures how they affect the heart using simple signals. The study also uses computer programs to identify breathing patterns automatically. The results show which breathing method works best immediately and which one has longer-lasting benefits. -
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. -
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. -
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study
This project develops a smart system to detect multiple heart diseases from ECG signals. It can learn even with very few labeled examples. The system improves accuracy by creating extra training data and learning patterns of disease combinations. It works well even on new data that the system has not seen before. -
A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography
This project focuses on improving heart monitoring using a wearable device that measures chest vibrations. Walking and movement often create noise that makes the signals unclear. The researchers developed a new method to remove this noise and get accurate heart rate readings. This makes wearable heart monitoring more reliable for everyday use and clinical care. -
CiGNN A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation
This project develops a new method to measure blood pressure without using a cuff. It finds which body signals actually affect blood pressure and then uses a smart computer model to track changes beat by beat. The method works accurately for people of different ages and health conditions. It can help monitor blood pressure continuously and more reliably than current approaches. -
Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis
This project studies how useful surgery biosignal data is for combined analysis of multiple signals. The researchers compared surgery data with sleep study data to check signal quality. They found that most segments of the surgery data are good enough for multimodal analysis. Poor-quality segments showed constant or unrealistic values. -
Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection
This project uses machine learning to study how common heart-related lab tests change over time in patients. It first learns general patterns from many patients and then applies this knowledge to detect specific heart problems in smaller patient groups. The method improves prediction accuracy and helps doctors better plan tests and treatments. It shows potential for use in other diseases too. -
Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference
This project focuses on creating a digital model of the heart to study heart attacks without surgery. It uses heart scans and ECG data to estimate tissue damage. A computer model learns to identify where and how much of the heart is affected. The approach could help doctors plan personalized treatments in the future. -
Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network
This project uses heart signal data to automatically detect sleep apnea. It trains a computer model to learn patterns from ECG signals and identify signs of the disorder. The proposed method works faster and more accurately than other machine learning models. It can help doctors diagnose sleep apnea more quickly and reliably.
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