ArticleSensors (Basel, Switzerland)2021
Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Enhancing ECG Classification Generalization Through Unified Multi-Dataset Training.Sensors (Basel, Switzerland) · 2026Article
- Artificially intelligent nasal perception for rapid sepsis diagnostics.NPJ digital medicine · 2025Article
- Wear Your Heart on Your Sleeve: Smart Textile ECG Wearables for Comfort, Integration, Signal Quality and Continuous Monitoring in Paroxysmal Atrial Fibrillation.Sensors (Basel, Switzerland) · 2025Review
- An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models.Sensors (Basel, Switzerland) · 2024Article
- An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG).PloS one · 2024Article
- Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Best Research Papers in the Field of Sensors, Signals, and Imaging Informatics 2021.Yearbook of medical informatics · 2022Review
- Trusted Data Storage Architecture for National Infrastructure.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length V, word-size W, and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA's performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (
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Registered trials
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