Research on Arrhythmia of College Students Based on Convolutional Neural Network
Authors: 
Lei Qi, Fuchun Zhang, Baiyang Wang, Meng LiAuthors Info & Claims
Lei Qi, Fuchun Zhang, Baiyang Wang, Meng LiAuthors Info & ClaimsPages 71 - 75
Abstract
Arrhythmia is a group of common diseases related to irregular heart rate. Accurate classification of electrocardiogram is very important for college students to detect heart disease. Experts will spend a major expenditure of time and effort in the examination and analysis of college students' ECG waveform. In order to solve this problem, this paper proposes a method of college students' arrhythmia detection based on deep learning, which classifies different types of arrhythmias. Firstly, 1-D ECG signals are converted into 2-D ECG images. Then the signals of different channels are fused and finally input into the network. In this work, we do experiments on the basis of residual network. For verifying the feasibility of this method, it is trained and verified in the public data set MIT-BIH, and the classification accuracy reaches 99.00%. The experimental results proved that it can effectively and reliably identify ECG signals, and has potential clinical application value. It can assist doctors to screen college students for arrhythmias.
References
[1]
Liu X, Wang H, Li Z, Deep learning in ECG diagnosis: A review[J]. Knowledge-Based Systems, 2021, 227: 107187.
[2]
Fan W, Si Y, Yang W, Active broad learning system for ECG arrhythmia classification[J]. Measurement, 2021, 185: 110040.
[3]
Ji Y, Zhang S, Xiao W. Electrocardiogram classification based on faster regions with convolutional neural network[J]. Sensors, 2019, 19(11): 2558.
[4]
Luo X, Yang L, Cai H, Multi-classification of arrhythmias using a HCRNet on imbalanced ECG datasets[J]. Computer Methods and Programs in Biomedicine, 2021, 208: 106258.
[5]
Wang G, Chen M, Ding Z, Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation[J]. Neurocomputing, 2021, 454: 339-349.
[6]
Huynh P, Warner G, Lin H. Effects of EMD and Feature Extraction on EEG Analysis[J]. Journal of Advances in Information Technology Vol, 2020, 11(1).
[7]
Chen Y, Du W, Duan X, Squeeze-and-Excitation Convolutional Neural Network for Classification of Malignant and Benign Lung Nodules[J]. Journal of Advances in Information Technology Vol, 2021, 12(2).
[8]
Mohan H M, Anitha S. Real Time Audio-Based Distress Signal Detection as Vital Signs of Myocardial Infarction Using Convolutional Neural Networks[J]. Journal of Advances in Information Technology Vol, 2022, 13(2).
[9]
Zhao Y, Cheng J, Zhang P, ECG classification using deep CNN improved by wavelet transform[J]. Computers, Materials and Continua, 2020.
[10]
Park J, Kim J, Jung S, ECG-signal multi-classification model based on squeeze-and-excitation residual neural networks[J]. Applied Sciences, 2020, 10(18): 6495.
[11]
Yao Q, Wang R, Fan X, Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network[J]. Information Fusion, 2020, 53: 174-182.
[12]
Gan Y, Shi J, He W, Parallel classification model of arrhythmia based on DenseNet-BiLSTM[J]. Biocybernetics and Biomedical Engineering, 2021, 41(4): 1548-1560.
[13]
Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. The Journal of physiology, 1962, 160(1): 106.
[14]
He K, Zhang X, Ren S, Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
[15]
Goldberger A L, Amaral L A N, Glass L, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals[J]. circulation, 2000, 101(23): e215-e220.
[16]
Izci E, Ozdemir M A, Degirmenci M, Cardiac arrhythmia detection from 2d ecg images by using deep learning technique[C]//2019 Medical Technologies Congress (TIPTEKNO). IEEE, 2019: 1-4.
[17]
Atal D K, Singh M. Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network[J]. Computer Methods and Programs in Biomedicine, 2020, 196: 105607.
[18]
Jing E, Zhang H, Li Z G, ECG heartbeat classification based on an improved ResNet-18 model[J]. Computational and Mathematical Methods in Medicine, 2021, 2021.
[19]
Ma H, Chen C, Zhu Q, An ECG signal classification method based on dilated causal convolution[J]. Computational and Mathematical Methods in Medicine, 2021, 2021.
[20]
Cui J, Wang L, He X, Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia[J]. Neural Computing and Applications, 2021: 1-15
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- Research on Arrhythmia of College Students Based on Convolutional Neural Network
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Published In
September 2022
215 pages
ISBN:9781450397551
DOI:10.1145/3568199
Copyright © 2022 ACM.
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Published: 06 March 2023
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- The fund supporting this work is research on the management of safety risk awareness of College Students' activities.
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MLMI 2022
MLMI 2022: 2022 5th International Conference on Machine Learning and Machine Intelligence
September 23 - 25, 2022
Hangzhou, China
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