The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 잡음 허용 오차가 높은 R-피크 검출 방법을 제시합니다. 이 방법은 맞춤형 심전도 신경망(DCNN)을 활용하여 슬라이스 심전도(ECG) 신호에서 형태학적 및 시간적 특징을 추출합니다. 제안된 네트워크는 다양한 시야에서 특징을 분석하기 위해 다중 병렬 확장 컨볼루션 레이어를 채택합니다. 슬라이딩 윈도우는 원래 ECG 신호를 세그먼트로 분할한 다음 네트워크는 한 번에 하나의 세그먼트를 계산하고 모든 포인트가 R-피크 영역에 속할 확률을 출력합니다. 이진화 및 디버링 작업 후 R-피크의 발생 시간을 찾을 수 있습니다. MIT-BIH 데이터베이스를 기반으로 한 실험 결과는 높은 강도의 전극 운동 인공물 또는 근육 인공물 잡음 하에서 R-피크 검출 정확도가 크게 향상될 수 있음을 보여 주며 이는 최첨단 방법보다 더 높은 성능을 나타냅니다.
Menghan JIA
Zhejiang University
Feiteng LI
Zhejiang University
Zhijian CHEN
Zhejiang University
Xiaoyan XIANG
Fudan University
Xiaolang YAN
Zhejiang University
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부
Menghan JIA, Feiteng LI, Zhijian CHEN, Xiaoyan XIANG, Xiaolang YAN, "High Noise Tolerant R-Peak Detection Method Based on Deep Convolution Neural Network" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 11, pp. 2272-2275, November 2019, doi: 10.1587/transinf.2019EDL8097.
Abstract: An R-peak detection method with a high noise tolerance is presented in this paper. This method utilizes a customized deep convolution neural network (DCNN) to extract morphological and temporal features from sliced electrocardiogram (ECG) signals. The proposed network adopts multiple parallel dilated convolution layers to analyze features from diverse fields of view. A sliding window slices the original ECG signals into segments, and then the network calculates one segment at a time and outputs every point's probability of belonging to the R-peak regions. After a binarization and a deburring operation, the occurrence time of the R-peaks can be located. Experimental results based on the MIT-BIH database show that the R-peak detection accuracies can be significantly improved under high intensity of the electrode motion artifact or muscle artifact noise, which reveals a higher performance than state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8097/_p
부
@ARTICLE{e102-d_11_2272,
author={Menghan JIA, Feiteng LI, Zhijian CHEN, Xiaoyan XIANG, Xiaolang YAN, },
journal={IEICE TRANSACTIONS on Information},
title={High Noise Tolerant R-Peak Detection Method Based on Deep Convolution Neural Network},
year={2019},
volume={E102-D},
number={11},
pages={2272-2275},
abstract={An R-peak detection method with a high noise tolerance is presented in this paper. This method utilizes a customized deep convolution neural network (DCNN) to extract morphological and temporal features from sliced electrocardiogram (ECG) signals. The proposed network adopts multiple parallel dilated convolution layers to analyze features from diverse fields of view. A sliding window slices the original ECG signals into segments, and then the network calculates one segment at a time and outputs every point's probability of belonging to the R-peak regions. After a binarization and a deburring operation, the occurrence time of the R-peaks can be located. Experimental results based on the MIT-BIH database show that the R-peak detection accuracies can be significantly improved under high intensity of the electrode motion artifact or muscle artifact noise, which reveals a higher performance than state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2019EDL8097},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - High Noise Tolerant R-Peak Detection Method Based on Deep Convolution Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 2272
EP - 2275
AU - Menghan JIA
AU - Feiteng LI
AU - Zhijian CHEN
AU - Xiaoyan XIANG
AU - Xiaolang YAN
PY - 2019
DO - 10.1587/transinf.2019EDL8097
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2019
AB - An R-peak detection method with a high noise tolerance is presented in this paper. This method utilizes a customized deep convolution neural network (DCNN) to extract morphological and temporal features from sliced electrocardiogram (ECG) signals. The proposed network adopts multiple parallel dilated convolution layers to analyze features from diverse fields of view. A sliding window slices the original ECG signals into segments, and then the network calculates one segment at a time and outputs every point's probability of belonging to the R-peak regions. After a binarization and a deburring operation, the occurrence time of the R-peaks can be located. Experimental results based on the MIT-BIH database show that the R-peak detection accuracies can be significantly improved under high intensity of the electrode motion artifact or muscle artifact noise, which reveals a higher performance than state-of-the-art methods.
ER -