基于CNN-LSTM 混合模型的心律失常自动检测

doi:10.6040/j.issn.1672-3961.0.2020.445

摘要/Abstract

摘要： 提出一种卷积神经网络(convolutional neural network, CNN)和长短时记忆(long short-term memory, LSTM)网络混合的心律失常自动检测算法,模型结构共由5层卷积层、5层池化层、1层LSTM层和1层全连接层组成。利用CNN能够自动提取特征和LSTM能够捕捉时间序列前后依赖关系的能力,将简单预处理后的心电信号数据直接输入到混合模型当中。整个模型将特征提取和分类器分类2个步骤结合,从而更加高效、准确地识别5种不同的心律失常疾病。在测试集上进行试验,准确率、敏感性和特异性分别为99.48%、99.47%和99.86%。试验结果表明,本研究提出的方法能够高效、准确地识别不同类型的心律失常疾病。

关键词: 卷积神经网络, 长短时记忆网络, 自动检测, 心电图, 心律失常

Abstract: A hybrid algorithm of convolutional neural network and long short-term memory network was proposed for automatic detection of arrhythmias. The model structure was composed of 5 convolutional layers, 5 pooled layers, 1 LSTM layer and 1 fully connected layer. By taking advantage of CNN's ability to automatically extract features and LSTM's ability to capture dependencies before and after time series, the simple preprocessed ECG signal data were directly input into the hybrid model. The whole model combined the two steps of feature extraction and classifier classification, so as to identify five different arrhythmias more efficiently and accurately. The accuracy, sensitivity and specificity of the test set were 99.48%, 99.47% and 99.86% respectively. The experimental results showed that the proposed method could efficiently and accurately identify different types of arrhythmias.

Key words: CNN, LSTM, automatic detection, electrocardiogram, arrhythmia

中图分类号:

TP391

陶亮,刘宝宁,梁玮. 基于CNN-LSTM 混合模型的心律失常自动检测[J]. 山东大学学报 (工学版), 2021, 51(3): 30-36.

TAO Liang, LIU Baoning, LIANG Wei. Automatic detection research of arrhythmia based on CNN-LSTM hybrid model[J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 30-36.

参考文献 23

[1]	VAN DRIEST S L,WELLS Q S, STALLINGS S, et al. Association of arrhythmia-related genetic variants with phenotypes documented in electronic medical records[J]. JAMA, 2016, 315(1):47-57.
[2]	KANANI P,PADOLE M. ECG heartbeat arrhythmia classification using time-series augmented signals and deep learning approach[J]. Procedia Computer Science, 2020, 171:524-531.
[3]	LI Qiao, CADATHUR R, GARI D C. Ventricular fibrillation and tachycardia classification using a machine learning approach[J]. IEEE Transactions on Bio-medical Engineering, 2014, 61(6):1607-1613.
[4]	SHADNAZ A, ALIREZA M, MARYAM M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine[J]. Computers in Biology and Medicine, 2015, 60:132-142.
[5]	MA Fengying, ZHANG Jingyao, LIANG Wei, et al. Automated classification of atrial fibrillation using artificial neural network for wearable devices[J]. Mathematical Problems in Engineering, 2020, 2020:1-6.
[6]	SAHOO S, SUBUDHI A, DASH M, et al. Automatic classification of cardiac arrhythmias based on hybrid features and decision tree algorithm[J]. International Journal of Automation and Computing, 2020, 17(4):551-561.
[7]	LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 2017, 42:60-88.
[8]	XIA Yong, NAREN W, WANG Kuanquan, et al. Detecting atrial fibrillation by deep convolutional neural networks[J]. Computers in Biology and Medicine, 2018, 93:84-92.
[9]	ROMDHANE T F, ALHICHRI H, OUNI R, et al. Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss[J]. Computers in Biology and Medicine, 2020, 123:103866.
[10]	NIU Jinghao, TANG Yongqiang, SUN Zhengya,et al. Inter-patient ECG classification with symbolic representations and multi-perspective convolutional neural networks[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(5):1321-1332.
[11]	SHARMA A, GARG N, PATIDAR S, et al. Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM[J]. Computers in Biology and Medicine, 2020, 120:103753.
[12]	SAEED S, MOHAMMADHOSEIN O, MATIN H. LSTM-based ECG classification for continuous moni-toring on personal wearable devices[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(2):515-523.
[13]	HAMMAD M, ZHANG Shanzhuo, WANG Kuanquan. A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication[J]. Future Generation Computer Systems, 2019, 101: 180-196.
[14]	FRANCISCO O,DANIEL R. Deep convolutional and LSTMrecurrent neural networks for multimodal wearable activity recognition[J]. Sensors, 2016, 16(1):115.
[15]	GOLDBERGER A L, AMARAL L A N, GLASS L, et al. Physiobank, physiotoolkit, and physionet: com-ponents of a new research resource for complex physiologic signals[J]. Circulation: Journal of the American Heart Association, 2000, 101(23):215-220.
[16]	PAN J, TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Bio-medical Engineering, 1985, 32(3):230-236.
[17]	SOREN D, EMILY J, PETER M. Singular values for ReLUlayers[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019:3594-3605.
[18]	ZHOU Ji, WANG Haide, WEI Jinlong, et al. Adaptive moment estimation for polynomial nonlinear equalizer in PAM8-based optical interconnects[J]. Optics Express, 2019, 27(22):32210-32216.
[19]	SAHOO S, KANUNGO B, BEHERA S, et al. Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities[J]. Measurement, 2017, 108:55-66.
[20]	ELHAJ F A, SALIM N, HARRIS A R, et al. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals[J]. Computer Methods and Programs in Biomedicine, 2016, 127:52-63.
[21]	LI Taiyong, ZHOU Min. ECG classification using wavelet packet entropy and random forests[J]. Entropy, 2016, 18(8):285.
[22]	ACHARYA U R, OH S L, HAGIWARA Y, et al. A deep convolutional neural network model to classify heartbeats[J]. Computers in Biology and Medicine, 2017, 89:389-396.
[23]	张异凡,黄亦翔,汪开正,等.用于心律失常识别的LSTM和CNN并行组合模型[J].哈尔滨工业大学学报,2019,51(10):76-82. ZHANG Yifan, HUANG Yixiang, WANG Kaizheng, et al. LSTM and CNN parallel combination models for arrhythmia recognition[J]. Journal of Harbin Institute of Technology, 2019, 51(10):76-82.

相关文章 15

[1]	董明书,陈俐企,马川义,张珠皓,孙仁娟,管延华,庄培芝. 沥青路面内部裂缝雷达图像智能判识算法研究[J]. 山东大学学报 (工学版), 2025, 55(3): 72-79.
[2]	李伟豪,王苹苹,许万博,魏本征. 结构先验引导的多模态腰椎MRI图像分割算法[J]. 山东大学学报 (工学版), 2025, 55(1): 66-76.
[3]	马翔悦,徐金东,倪梦莹. 基于多尺度特征模糊卷积神经网络的遥感图像分割[J]. 山东大学学报 (工学版), 2024, 54(3): 44-54.
[4]	迟云浩,杨璐,郭杰,郝凡昌,聂秀山. 基于注意力特征融合网络的手指静脉图像质量评价方法[J]. 山东大学学报 (工学版), 2023, 53(6): 56-62.
[5]	那绪博,张莹,李沐阳,陈元畅,华云鹏. 基于ODCG的网约车需求预测模型[J]. 山东大学学报 (工学版), 2023, 53(5): 48-56.
[6]	范海雯,郝旭东,赵康,邢法财,蒋哲,李常刚. 基于卷积神经网络的含分布式光伏配电网静态等值[J]. 山东大学学报 (工学版), 2023, 53(4): 140-148.
[7]	王智伟,徐海超,郭相阳,马炯,褚云龙,陈前昌,卢治. 基于卷积神经网络和层次分析的新能源电源调频能力智能预测方法[J]. 山东大学学报 (工学版), 2022, 52(5): 70-76.
[8]	张学思,张婷,刘兆英,江天鹏. 基于轻量型卷积神经网络的海面红外显著性目标检测方法[J]. 山东大学学报 (工学版), 2022, 52(2): 41-49.
[9]	王心哲,邓棋文,王际潮,范剑超. 深度语义分割MRF模型的海洋筏式养殖信息提取[J]. 山东大学学报 (工学版), 2022, 52(2): 89-98.
[10]	尹旭,刘兆英,张婷,李玉鑑. 基于弱监督和半监督学习的红外舰船分割方法[J]. 山东大学学报 (工学版), 2022, 52(2): 99-106.
[11]	廖毅,罗炜,蒋峰伟,李亚锦,于大洋. 基于LSTM的阀冷系统入水温度及冷却裕度预测[J]. 山东大学学报 (工学版), 2021, 51(4): 124-130.
[12]	廖锦萍,莫毓昌,YAN Ke. 基于C-LSTM的短期用电预测模型和应用[J]. 山东大学学报 (工学版), 2021, 51(2): 90-97.
[13]	刘帅,王磊,丁旭涛. 基于Bi-LSTM的脑电情绪识别[J]. 山东大学学报 (工学版), 2020, 50(4): 35-39.
[14]	廖南星,周世斌,张国鹏,程德强. 基于类激活映射-注意力机制的图像描述方法[J]. 山东大学学报 (工学版), 2020, 50(4): 28-34.
[15]	张海军,陈映辉. 语义分析及向量化大数据跨站脚本攻击智检[J]. 山东大学学报 (工学版), 2020, 50(2): 118-128.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed