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المؤلفZahid M.U.
المؤلفKiranyaz, Mustafa Serkan
المؤلفInce T.
المؤلفDevecioglu O.C.
المؤلفChowdhury M.E.H.
المؤلفKhandakar A.
المؤلفTahir A.
المؤلفGabbouj M.
تاريخ الإتاحة2022-04-26T12:31:17Z
تاريخ النشر2022
اسم المنشورIEEE Transactions on Biomedical Engineering
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/TBME.2021.3088218
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85111016577&doi=10.1109%2fTBME.2021.3088218&partnerID=40&md5=721650c743159a6e7a227d8101bff001
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30584
الملخصObjective: Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Methods: In this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1D segmentation map of R-peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. Results: The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-DB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB). Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision. Significance: Compared to all competing methods, the proposed approach can reduce the false-positives and false-negatives in Holter ECG signals by more than 54% and 82%, respectively. Conclusion: Finally, the simple and invariant nature of the parameters leads to a highly generic system and therefore applicable to any ECG dataset.
اللغةen
الناشرIEEE Computer Society
الموضوعConvolution
Convolutional neural networks
Data streams
Database systems
Electrocardiography
Physiological models
Competing algorithms
Number of false alarms
Physiological signals
Real time monitoring
Segmentation map
Single channel ECG
Verification model
Wearable devices
Biomedical signal processing
algorithm
ambulatory electrocardiography
electrocardiography
heart arrhythmia
human
signal processing
Algorithms
Arrhythmias, Cardiac
Electrocardiography
Electrocardiography, Ambulatory
Humans
Neural Networks, Computer
Signal Processing, Computer-Assisted
العنوانRobust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network
النوعArticle
الصفحات119-128
رقم العدد1
رقم المجلد69
dc.accessType Abstract Only


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