Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network
المؤلف | Zahid M.U. |
المؤلف | Kiranyaz, Mustafa Serkan |
المؤلف | Ince T. |
المؤلف | Devecioglu O.C. |
المؤلف | Chowdhury M.E.H. |
المؤلف | Khandakar A. |
المؤلف | Tahir A. |
المؤلف | Gabbouj M. |
تاريخ الإتاحة | 2022-04-26T12:31:17Z |
تاريخ النشر | 2022 |
اسم المنشور | IEEE Transactions on Biomedical Engineering |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1109/TBME.2021.3088218 |
الملخص | Objective: 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. |
اللغة | en |
الناشر | IEEE Computer Society |
الموضوع | Convolution 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 |
النوع | Article |
الصفحات | 119-128 |
رقم العدد | 1 |
رقم المجلد | 69 |
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