Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (4): 38-44.doi: 10.6040/j.issn.1672-3961.0.2021.306

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ECG signal classification based on feature selection algorithm

YUAN Gaoteng, ZHOU Xiaofeng*, GUO Hongle   

  1. College of Computer and Information, Hohai University, Nanjing 211100, Jiangsu, China
  • Published:2022-08-24

CLC Number: 

  • TP391
[1] HANNUN A Y, RAJPURKAR P, HAGHPANAHI M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network[J]. Nature Medicine, 2019, 25(1): 65-69.
[2] 潘辉, 郑威, 张莹莹. 基于改进残差网络对心电信号的识别[J]. 数据采集与处理, 2020, 4(35): 682-692. PAN Hui, ZHENG Wei, ZHANG Yingying. Recognition of ECG signal base on modified residual network[J]. Journal of Data Acquisition and Processing, 2020, 4(35): 682-692.
[3] SASHIKUMAR S P, SHAN A J, LI Q, et al. A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology[C] //2017 IEEE EMBS International Conference on Biomedical & Health Informations(BHI). Orlando, America: IEEE Press,2017: 141-144.
[4] KUNG B H, HU P Y, HUANG C C, et al. An efficient ECG classification system using resource-saving architecture and random forest[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 99:1-1.
[5] 陈珂锐, 孟小峰. 机器学习的可解释性[J].计算机研究与发展, 2020, 57(9):1971-1986. CHEN Kerui, MENG Xiaofeng. Interpretation and Understanding in Machine Learning[J]. Journal of Computer Research and Development, 2020, 57(9): 1971-1986.
[6] DEO R C. Machine learning in medicine[J]. Circulation, 2015, 132(20): 1920-1921.
[7] YADAV S K, SINHA R, BORA P K. Electrocardiogram signal denoising using non-local wavelet transform domain filtering[J]. IET Signal Processing, 2015, 9(1):88-96.
[8] NARIN A, ISLER Y, OZER M, et al. Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability[J]. Physica A: Statistical Mechanics and Its Applications, 2018, 509: 56-65.
[9] ALQUDAH A M, BADARNEH A, ABUQASMIEH I, et al. Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features[J]. Australasian Physical & Engineering Sciences in Medicine, 2019, 41(1):149-157.
[10] HAN X, HU Y, FOSCHINI L, et al. Deep learning models for electrocardiograms are susceptible to adversarial attack[J]. Nature Medicine, 2020, 26(3):360-363.
[11] PARVANEH S, RUBIN J, RAHMAN A, et al. Analyzing singlelead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation[J]. Physiological Measurement, 2018, 39(8): 084003.
[12] FAUST O, SHENFIELD A, KAREEM M, et al. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals[J]. Computers in Biology and Medicine, 2018, 102: 327-335.
[13] 杨萍, 王丹, 康子健,等. 基于模式识别和集成CNN-LSTM的阵发性房颤预测模型[J]. 浙江大学学报(工学版), 2020,361(54):202-211. YANG Ping, WANG Dan, KANG Zijian, et al. Prediction model of paroxysmal atrial fibrillation based on pattern recognition and ensemble CNN-LSTM[J]. Journal of Zhejiang University(Engineering Science), 2020, 361(54): 202-211.
[14] DIMA S M, PANAGIOTOU C, MAZOMENOS E B, et al. On the detection of myocadial scar based on ECG/VCG analysis[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(12):3399-3409.
[15] KAMALESWARAN R, MAHAJAN R, AKBILGIC O. A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using varying length single lead electrocardiogram[J]. Physiological Measurement, 2018, 39(3): 035006.
[16] LI H, YUAN D, MA X, et al. Genetic algorithm for the optimization of features and neural networks in ECG signals classification[J]. Scientific Reports, 2017, 7:41011.
[17] ERDENEBAYAR U, KIM H, PARK J U, et al. Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal[J]. Journal of Korean Medical Science, 2019, 34(7): e64.
[18] KAMALESWARAN R, MAHAJAN R, AKBILGIC O. A robust deep convolutional neural network for the classification of deep convolutional neural network for the classification of variable length[J]. Physiological Measurement, 2018, 39(3): 035006.
[19] 方红帏, 赵涛, 佃松宜. 基于三域特征提取和GS-SVM的ECG信号智能分类技术研究[J]. 四川大学学报(自然科学版), 2020, 57(2): 297. FANG Hongwei, ZHAO Tao, DIAN Songyi. Research on intelligent classification of ECG signals based on three domain features extraction and GS-SVM[J]. Journal of Sichuan University(National Science Edition), 2020, 57(2):297.
[20] HANBAY K.Deep neural network based approach for ECG classification using hybrid differential features and active learning[J]. IET Signal Processing, 2019, 13(2):165-175.
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