ArticleBioengineering (Basel, Switzerland)2020
Early Detection of Atrial Fibrillation Based on ECG Signals.
Article in Bioengineering (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 56 citations in OpenAlex.
- Change-point detection and early warning systems.Scientific reports · 2026Article
- SMART MAT: Fibre Optic Innovation for Bedside Monitoring and Validation of Continuous Vital Signs.Sensors (Basel, Switzerland) · 2025Article
- Extracting Genetically-Imputed Causal Features From ECG Data.Statistical analysis and data mining · 2025Article
- QRS-centric beat-wise atrial fibrillation detection in ECG signals using deep neural networks.Computers in biology and medicine · 2025Article
- TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals.Journal of medical systems · 2025Article
- Integrated portable ECG monitoring system with CNN classification for early arrhythmia detection.Frontiers in digital health · 2025Article
- Early Diagnosis of Problems Related to the Self-Organization of the Cardiovascular System Based on the Interplay between RR and JT Cardiac Intervals.Diagnostics (Basel, Switzerland) · 2024Article
- Recent Advances in the Management of Non-rheumatic Atrial Fibrillation: A Comprehensive Review.Cureus · 2024Review
- Article
- Organic Electronics in Biosensing: A Promising Frontier for Medical and Environmental Applications.Biosensors · 2023Review
- Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals.Bioengineering (Basel, Switzerland) · 2021Article
- AFibNet: an implementation of atrial fibrillation detection with convolutional neural network.BMC medical informatics and decision making · 2021Article
- Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation, often called AF is considered to be the most common type of cardiac arrhythmia, which is a major healthcare challenge. Early detection of AF and the appropriate treatment is crucial if the symptoms seem to be consistent and persistent. This research work focused on the development of a heart monitoring system which could be considered as a feasible solution in early detection of potential AF in real time. The objective was to bridge the gap in the market for a low-cost, at home use, noninvasive heart health monitoring system specifically designed to periodically monitor heart health in subjects with AF disorder concerns. The main characteristic of AF disorder is the considerably higher heartbeat and the varying period between observed R waves in electrocardiogram (ECG) signals. This proposed research was conducted to develop a low cost and easy to use device that measures and analyzes the heartbeat variations, varying time period between successive R peaks of the ECG signal and compares the result with the normal heart rate and RR intervals. Upon exceeding the threshold values, this device creates an alert to notify about the possible AF detection. The prototype for this research consisted of a Bitalino ECG sensor and electrodes, an Arduino microcontroller, and a simple circuit. The data was acquired and analyzed using the Arduino software in real time. The prototype was used to analyze healthy ECG data and using the MIT-BIH database the real AF patient data was analyzed, and reasonable threshold values were found, which yielded a reasonable success rate of AF detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.