基于深度学习和模糊C均值的心电信号分类方法

吴志勇; 丁香乾; 许晓伟; 鞠传香

doi:10.16383/j.aas.2018.c170417

基于深度学习和模糊C均值的心电信号分类方法

doi: 10.16383/j.aas.2018.c170417

吴志勇^1,2, ,,
丁香乾^1,,
许晓伟^1,,
鞠传香^2,

1.
中国海洋大学信息科学与工程学院青岛 266100
2.
山东理工大学计算机科学与技术学院淄博 255000

基金项目:

国家重点研发计划 2016YFB1001103

详细信息

作者简介:
丁香乾  中国海洋大学信息科学与技术学院教授.主要研究方向为智能计算.E-mail:dingxq1995@vip.sina.com

许晓伟  中国海洋大学信息科学与技术学院副教授.主要研究方向为智能计算.E-mail:xuxw52@ouc.edu.cn

鞠传香  山东理工大学计算机学院讲师.主要研究方向为模糊数学.E-mail:chuanxiangju@sina.com

通讯作者:
吴志勇中国海洋大学信息科学与技术学院博士研究生, 山东理工大学计算机学院讲师.主要研究方向为智能计算.本文通信作者.E-mail:wuzhiyong_sdut@sina.com

计量
- 文章访问数: 2459
- HTML全文浏览量: 574
- PDF下载量: 1172
- 被引次数: 0
出版历程
- 收稿日期: 2017-07-26
- 录用日期: 2017-12-06
- 刊出日期: 2018-10-20

A Method for ECG Classification Using Deep Learning and Fuzzy C-means

1.
College of Information Science and Engineering, Ocean University of China, Qingdao 266100
2.
School of Computer Science and Technology, Shandong University of Technology, Zibo 255000

Funds:

National Key Research and Development Program of China 2016YFB1001103

More Information

Author Bio:
Professor at the College of Information Science and Engineering, Ocean University of China. His main research interest is intelligent computing

Associate professor at the College of Information Science and Engineering, Ocean University of China. His main research interest is intelligent computing

Lecturer at the School of Computer Science and Technology, Shandong University of Technology. Her main research interest is fuzzy theory

Corresponding author: WU Zhi-Yong Ph. D. candidate at the College of Information Science and Engineering, Ocean University of China. Lecturer at the School of Computer Science and Technology, Shandong University of Technology. His main research interest is intelligent computing. Corresponding author of this paper

摘要

摘要: 针对长时海量心电信号自动分类系统中，心电专家诊断费时、费力和成本高，心电信号形态复杂导致特征提取困难，异常诊断模型适应性差、准确度低等问题，本文提出一种基于深度学习和模糊C均值的心电信号分类方法.该方法主要包括心电信号降噪预处理、心电信号分段和采样点统一化、无监督心跳特征学习、模糊C均值分类4个步骤，给出了模糊C均值深度信念网络FCMDBN模型结构和学习分类算法.仿真实验基于MIT-BIH心率异常数据库表明，与基于传统心电特征人工设计的分类方法相比，本文提出的信号诊断方法具有较高的适应性和准确度.
- 心电信号分类 /
- 深度学习 /
- 模糊C均值 /
- 深度信念网络
Abstract: In the classification system for longtime and massive ECG signals, ECG diagnosis is time-consuming, laborious and costly. It is difficult to extract signal features because of the complex ECG morphology. The diagnosis model has low adaptability and accuracy. To solve the above problem, a novel method for ECG classification using deep learning and fuzzy C-means is proposed. The method includes four steps:ECG signal preprocessing, heartbeat segmentation and sampling point unification, ECG feature deep learning, fuzzy C-means classification. The structure and algorithm of fuzzy C-means deep belief networks (FCMDBN) are shown in the paper. The method is validated on the well-known MIT-BIH arrhythmia database. Experiment results show that the approach achieves higher adaptability and accuracy than traditional hand-designed methods on classification of ECG signals.
- ECG classification /
- deep learning /
- fuzzy C-means /
- deep belief networks (DBNs)
注释:

1) 本文责任编委付俊

HTML全文

图 1 心电信号自动分类系统流程

Fig. 1 The system flow of ECG classification

下载: 全尺寸图片幻灯片

图 2 基于深度学习和模糊C均值的心电信号分类技术流程

Fig. 2 The process of ECG classification using deep learning and fuzzy C-means

下载: 全尺寸图片幻灯片

图 3 模糊C均值深度网络结构

Fig. 3 Fuzzy C-means deep network structure

下载: 全尺寸图片幻灯片

图 4 5类心律波形图

Fig. 4 Five types of cardiac rhythms graph

下载: 全尺寸图片幻灯片

图 5 心律特征值分布范围

Fig. 5 Distribution range of cardiac rhythms features value

下载: 全尺寸图片幻灯片

图 6 随机样本与各类心率中心点欧氏距离

Fig. 6 Euclidean distance between random sample and\\ the center point of heart rate

下载: 全尺寸图片幻灯片

表 1 5类心律MIT-BIH实验数据集

Table 1 The data set of MIT-BIH including five types of cardiac rhythms

数据集	类型	心跳记录	NORM	LBBB	RBBB	PVC	APC	合计
$DS1$	训练数据集	100、105、108、111、114、116、118、201、203、207、208、209、215 219、222、228、233	30 179	3 578	2 251	3 387	892	40 287
$DS2$	测试数据集	103、106、109、119、124、205、214、221、223、231、232	14 560	1 999	3 182	2 247	1 462	23 450

下载: 导出CSV

表 2 5类心律特征中心点

Table 2 The centers of feature vectors of five kinds of cardiac rhythms

特征点	NORM	LBBB	RBBB	PVC	APC
$F1$	$ -$1.8883	$ -$4.9128	$ -$1.8114	3.2888	2.3609
$F2$	$ -$1.8629	0.2611	4.3143	2.0445	2.5327
$F3$	$ -$2.4740	$ -$2.5394	$ -$1.5449	2.5297	2.2344
$F4$	$ -$3.8821	0.3071	0.3587	2.9173	1.6611
$F5$	$ -$2.4890	$ -$0.4371	1.7242	1.1693	$ -$2.5217
$F6$	$ -$0.1716	$ -$2.3649	1.0338	2.7506	2.4165
$F7$	$ -$0.4175	1.6318	1.1534	3.2230	$ -$2.3545
$F8$	1.5320	0.2320	3.5207	2.8277	0.2495
$F9$	4.4273	1.9278	2.1546	2.6333	$ -$1.7905
$F10$	2.7375	0.4500	0.7206	1.4910	$ -$0.8220

下载: 导出CSV

表 3 FCMDBN模型在DS2数据集上的分类混淆矩阵

Table 3 Confusion matrix for ECG arrhythmias classification on DS2 using the FCMDBN

心律类型	NORM	LBBB	RBBB	PVC	APC	Total
NORM	14 316	111	69	39	25	14 560
LBBB	23	1 811	57	51	57	1 999
RBBB	32	12	3 001	86	51	3 182
PVC	36	29	49	2 121	12	2 247
APC	12	20	11	28	1 391	1 462

下载: 导出CSV

表 4 分类结果性能比较

Table 4 Performance comparison of classification results

方法		NORM	LBBB	RBBB	PVC	APC
	Se (%)	98.32	90.59	94.31	94.39	95.14
FCMDBN	PPV (%)	99.28	91.32	94.16	91.22	90.55
	TCA (%)						96.54
	Se (%)	98.28	90.35	86.97	92.19	94.86
FCMM ^[12]	PPV(%)	97.38	90.97	87.07	86.82	93.87
	TCA (%)						93.57
	Se (%)	94.80	58.10	88.50	88.80	74.50
Knn-NN ^[36]	PPV (%)	98.09	74.36	78.86	54.79	78.49
	TCA (%)
	Se (%)	100	48.0	74.6	98.6	99.3
MLP	PPV (%)	92.6	96.0	99.1	81.3	78.8
network ^[37]	TCA (%)						87.6

下载: 导出CSV

参考文献(37)

[1]	World Health Organization. Cardiovascular diseases[Online], Available: http://www.who.int/mediacentre/factsheets/fs317/en/, May, 2017.
[2]	Waktare J E P, Malik M. Holter, loop recorder, and event counter capabilities of implanted devices. Pacing and Clinical Electrophysiology, 1997, 20(10):2658-2669 doi: 10.1111/pace.1997.20.issue-10
[3]	de Chazal P, O'Dwyer M, Reilly R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 2004, 51(7):1196-1206 doi: 10.1109/TBME.2004.827359
[4]	Pan J P, Tompkins W J. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 1985, BME-32(3):230-236 doi: 10.1109/TBME.1985.325532
[5]	de Chazal P, Reilly R B. A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 2006, 53(12):2535-2543 doi: 10.1109/TBME.2006.883802
[6]	Martis R J, Acharya U R, Min L C. ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 2013, 8(5):437-448 doi: 10.1016/j.bspc.2013.01.005
[7]	Kampouraki A, Manis G, Nikou C. Heartbeat time series classification with support vector machines. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(4):512-518 doi: 10.1109/TITB.2008.2003323
[8]	Rodríguez R, Mexicano A, Bila J, Cervantes S, Ponce R. Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. Journal of Applied Research and Technology, 2015, 13(2):261-269 doi: 10.1016/j.jart.2015.06.008
[9]	Ceylan R, Özbay Y. Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Systems with Applications, 2007, 33(2):286-295 doi: 10.1016/j.eswa.2006.05.014
[10]	da S Luz E J, Nunes T M, de Albuquerque V H C, Papa J P, Menotti D. ECG arrhythmia classification based on optimum-path forest. Expert Systems with Applications, 2013, 40(9):3561-3573 doi: 10.1016/j.eswa.2012.12.063
[11]	Dilmac S, Korurek M. ECG heart beat classification method based on modified ABC algorithm. Applied Soft Computing, 2015, 36:641-655 doi: 10.1016/j.asoc.2015.07.010
[12]	Yeh Y C, Lin H J. Cardiac arrhythmia diagnosis method using fuzzy C-Means algorithm on ECG signals. In: Proceedings of the 2010 International Symposium on Computer, Communication, Control and Automation (3CA). Tainan, China: IEEE, 2010. 272-275.
[13]	Hayat M, Bennamoun M, An S J. Deep reconstruction models for image set classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(4):713-727 doi: 10.1109/TPAMI.2014.2353635
[14]	Graves A, Mohamed A R, Hinton G. Speech recognition with deep recurrent neural networks. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Vancouver, BC, Canada: IEEE, 2013.
[15]	Al Rahhal M M, Bazi Y, AlHichri H, Alajlan N, Melgani F, Yager R R. Deep learning approach for active classification of electrocardiogram signals. Information Sciences, 2016, 345:340-354 doi: 10.1016/j.ins.2016.01.082
[16]	Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7):1527-1554 doi: 10.1162/neco.2006.18.7.1527
[17]	Wang Y S, Yao H X, Zhao S C. Auto-encoder based dimensionality reduction. Neurocomputing, 2016, 184:232-242 doi: 10.1016/j.neucom.2015.08.104
[18]	Schmidhuber J. Deep learning in neural networks:an overview. Neural Networks, 2015, 61:85-117 doi: 10.1016/j.neunet.2014.09.003
[19]	Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Transactions on Biomedical Engineering, 2016, 63(3):664-675 doi: 10.1109/TBME.2015.2468589
[20]	Yan Y, Qin X B, Wu Y G, Zhang N N, Fan J P, Wang L. A restricted Boltzmann machine based two-lead electrocardiography classification. In: Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Cambridge, MA, USA: IEEE, 2015. 1-9
[21]	Meng H H, Zhang Y. Classification of electrocardiogram signals with deep belief networks. In: Proceedings of the 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE). Chengdu, China: IEEE, 2014.
[22]	Özbay Y, Ceylan R, Karlik B. Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Systems with Applications, 2011, 38(1):1004-1010
[23]	Yeh Y C, Wang W J, Chiou C W. A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement, 2010, 43(10):1542-1555 doi: 10.1016/j.measurement.2010.08.019
[24]	Sutskever I, Hinton G E. Learning multilevel distributed representations for high-dimensional sequences. Journal of Machine Learning Research, 2007, 2:548-555
[25]	Memisevic R, Hinton G. Unsupervised learning of image transformations. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA: IEEE, 2007.
[26]	Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy layer-wise training of deep networks. In: Proceedings of the 19th International Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2006. 153-160
[27]	Hüsken M, Stagge P. Recurrent neural networks for time series classification. Neurocomputing, 2003, 50:223-235 doi: 10.1016/S0925-2312(01)00706-8
[28]	Polat K. Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering. International Journal of Systems Science, 2012, 43(4):597-609 doi: 10.1080/00207721.2011.581395
[29]	Hwang S H, Kim D, Kang T K, Park G T. Medical diagnosis system of breast cancer using FCM based parallel neural networks. In: Proceedings of ICIC 2007: Advanced Intelligent Computing Theories and Applications with Aspects of Artificial Intelligence. Berlin Heidelberg: Springer, 2007. 712-719
[30]	Ekong V E, Onibere E A, Imianvan. Fuzzy cluster means system for the diagnosis of liver diseases. Journal of Computer Science & Technology, 2011, 2(3):205-209
[31]	Meau Y P, Ibrahim F, Narainasamy S A L, Omar R. Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system. Computer Methods and Programs in Biomedicine, 2006, 82(2):157-168 doi: 10.1016/j.cmpb.2006.03.003
[32]	Özbay Y, Ceylan R, Karlik B. A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Computers in Biology and Medicine, 2006, 36(4):376-388 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ029949063
[33]	Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 1973, 3(3):32-57 doi: 10.1080/01969727308546046
[34]	Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Springer, 1981. 203-239
[35]	Hinton G E. Training products of experts by minimizing contrastive divergence. Neural Computation, 2002, 14(8):1771-1800 doi: 10.1162/089976602760128018
[36]	Jekova I, Bortolan G, Christov I. Assessment and comparison of different methods for heartbeat classification. Medical Engineering & Physics, 2008, 30(2):248-257 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ02961447
[37]	Dokur Z, Ölmez T. ECG beat classification by a novel hybrid neural network. Computer Methods and Programs in Biomedicine, 2001, 66(2-3):167-181 doi: 10.1016/S0169-2607(00)00133-4