ArticleJournal of medical systems2020
Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.
Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- QRS-centric beat-wise atrial fibrillation detection in ECG signals using deep neural networks.Computers in biology and medicine · 2025Article
- Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study.Sensors (Basel, Switzerland) · 2024Article
- xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.Scientific reports · 2024Article
- Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.PloS one · 2024Article
- Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.Sensors (Basel, Switzerland) · 2023Article
- Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning.Biophysical reviews · 2023Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals.Journal of healthcare engineering · 2022Article
- Accurate detection of atrial fibrillation events with R-R intervals from ECG signals.PloS one · 2022Article
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
- Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming.Journal of healthcare engineering · 2021Article
- Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.Computational intelligence and neuroscience · 2021Article
- A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data.Journal of medical systems · 2020Article
- Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.Sensors (Basel, Switzerland) · 2020Article
- Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings.BioMed research international · 2020Article
- RR Interval-based Atrial Fibrillation Detection using Traditional and Ensemble Machine Learning Algorithms.Journal of medical signals and sensorsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.
Indexed as
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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.