[논문]Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network

Zahid, Muhammad Uzair; Kiranyaz, Serkan; Ince, Turker; Devecioglu, Ozer Can; Chowdhury, Muhammad E. H.; Khandakar, Amith; Tahir, Anas; Gabbouj, Moncef

doi:10.1109/tbme.2021.3088218

Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network 원문보기

IEEE transactions on bio-medical engineering, v.69 no.1, 2022년, pp.119 - 128

Zahid, Muhammad Uzair (Qatar University, Electrical Engineering, College of Engineering, Qatar) , Kiranyaz, Serkan (Qatar University, Electrical Engineering, College of Engineering, Qatar) , Ince, Turker (Izmir University of Economics, Electrical and Electronics Engineering Department, Turkey) , Devecioglu, Ozer Can (Tampere University, Department of Computing Sciences, Finland) , Chowdhury, Muhammad E. H. (Qatar University, Electrical Engineering, College of Engineering, Qatar) , Khandakar, Amith (Qatar University, Electrical Engineering, College of Engineering, Qatar) , Tahir, Anas (Qatar University, Electrical Engineering, College of Engineering, Qatar) , Gabbouj, Moncef (Tampere University, Department of Computing Sciences, Tampere, Finland)

Abstract ▼ AI-Helper

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.

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Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network 원문보기

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