基于一维卷积特征与手工特征融合的集成超限学习机心跳分类方法

许越凡; 肖文栋; 曹征涛

doi:10.13374/j.issn2095-9389.2021.01.12.005

基于一维卷积特征与手工特征融合的集成超限学习机心跳分类方法

doi: 10.13374/j.issn2095-9389.2021.01.12.005

许越凡^{1, 2},
肖文栋^{1, 2, 3},
曹征涛^4, ,

1.
北京科技大学自动化学院，北京 100083
2.
北京市工业波谱成像工程技术研究中心，北京 100083
3.
北京科技大学顺德研究生院，顺德 528399
4.
空军特色医学中心，北京 100142

基金项目: 国家重点研发计划课题资助项目（2017YFB1401203）；佛山市科技创新专项资金资助项目（BK20AF005）

详细信息

通讯作者:
E-mail: czhengtao@126.com

中图分类号: TP182
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- 被引次数: 0
出版历程
- 收稿日期: 2021-01-12
- 网络出版日期: 2021-03-10
- 刊出日期: 2021-09-18

Ensemble extreme learning machine approach for heartbeat classification by fusing 1d convolutional and handcrafted features

XU Yue-fan^{1, 2},
XIAO Wen-dong^{1, 2, 3},
CAO Zheng-tao^{4
, ,}

1.
School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China
3.
Shunde Graduate School, University of Science and Technology Beijing, Shunde 528399, China
4.
Air Force Medical Center, PLA, Beijing 100142, China

More Information

Corresponding author: E-mail: czhengtao@126.com

摘要

摘要: 融合手工特征和深度特征，提出了一种集成超限学习机心跳分类方法。手工提取的特征明确地表征了心电信号的特定特性，如相邻心跳时间间隔反映了心跳信号的时域特性，小波系数反映了心跳信号的时频特性。同时设计了一维卷积神经网络对心跳信号特征进行自动提取。基于超限学习机（Extreme leaning machine，ELM），将上述特征融合进行心跳分类。由于ELM初始参数的随机给定可能导致其性能不稳定，进一步提出了一种基于袋装（Bagging）策略的多个ELM集成方法，使分类结果更加稳定且模型泛化能力更强。利用麻省理工心律失常公开数据集对所提方法进行了验证，分类准确率达到了99.02%，实验结果也表明基于融合特征的分类准确率高于基于单独特征的分类准确率。
- 心跳分类 /
- 特征融合 /
- 一维卷积神经网络 /
- 小波变换 /
- 集成超限学习机
Abstract: Arrhythmia is a common cardiovascular disease whose occurrence is mainly related to two factors: cardiac pacing and conduction. Some severe arrhythmias can even threaten human life. An electrocardiogram (ECG) records the changes in electrical activity generated during each cardiac cycle of the heart, which can reflect the human cardiac health status and help diagnose arrhythmias. However, because of the brevity of conventional ECGs, arrhythmias, which occasionally occur in daily life, cannot be detected easily. Automatic ECG analysis-based long-term heartbeat monitoring is of great significance for the effective detection of accidental arrhythmias and then for taking indispensable measures to prevent cardiovascular diseases in time. An ensemble extreme learning machine (ELM) approach for heartbeat classification that fuses handcrafted features and deep features was proposed. The manually extracted features clearly characterize the heartbeat signal, where RR interval features reflect the time-domain characteristic, and the wavelet coefficient features reflect the time–frequency characteristic. A 1D convolutional neural network (1D CNN) was designed to automatically extract deep features for heartbeat signals. These features were fused by an ELM for heartbeat classification. Because of the instability caused by the random assignment of ELM hidden layer parameters, the bagging ensemble strategy was introduced to integrate multiple ELMs to achieve stable classification performance and good generalization ability. The proposed approach was validated on the MIT-BIH arrhythmia public dataset. The classification accuracy reaches 99.02%, and the experimental results show that the performance of the proposed approach with fused features is better than those with only deep features and only handcrafted features.
- heartbeat classification /
- feature fusion /
- 1D convolutional neural network /
- wavelet transform /
- ensemble extreme learning machine

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图 1 ELM的基本结构

Figure 1. Basic structure of an extreme learning machine (ELM)

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图 2 心跳分类算法总体结构

Figure 2. Overall structure of the heartbeat classification algorithm

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图 3 本文提出的1D CNN结构

Figure 3. Structure of the proposed 1D convolutional neural network

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图 4 ELM心跳分类结构

Figure 4. ELM heartbeat classification structure

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表 1 MIT−BIH数据集的详细描述和划分

Table 1. Detailed description and division of the MIT−BIH dataset

Heartbeat type (abbreviation)	Annotation	Total number of samples	Number of training samples	Number of testing samples
Normal beat (NOR)	N	75023	9753	65270
Left bundle branch block (LBBB)	L	8072	3229	4843
Right bundle branch block (RBBB)	R	7255	2902	4353
Atrial premature contraction (APC)	A	2546	1018	1528
Premature ventricular contraction (PVC)	V	7129	2852	4277
Paced beat (PACE)	/	7026	2810	4216
Aberrated atrial premature beat (AP)	a	150	75	75
Ventricular flutter wave (VF)	!	472	236	236
Fusion of ventricular and normal beat (VFN)	F	802	401	401
Blocked atrial premature beat (BAP)	x	193	96	97
Nodal (junctional) escape beat (NE)	j	229	114	115
Fusion of paced and normal beat (FPN)	f	982	491	491
Ventricular escape beat (VE)	E	106	53	53
Nodal (junctional) premature beat (NP)	J	83	42	41
Atrial escape beat (AE)	e	16	8	8
Unclassifiable beat (UN)	Q	33	16	17
Total	16	110117	24096	86021

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表 2 每类心跳的召回率和精确率

Table 2. Recall and precision for each heartbeat class

Heartbeat type	Number of test samples	Recall/%	Precision/%
N	65270	99.58	99.43
L	4843	99.71	99.67
R	4353	99.61	99.34
A	1528	86.45	94.90
V	4277	97.80	95.57
/	4216	99.69	99.41
a	75	65.33	92.45
!	236	93.64	92.47
F	401	79.55	87.40
x	97	86.60	94.38
j	115	87.83	71.13
f	491	91.65	96.98
E	53	94.34	98.04
J	41	90.24	97.37
e	8	12.50	100.00
Q	17	5.88	50.00
Total	86021	99.02	99.02

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表 3 混淆矩阵

Table 3. Confusion matrix

	Predict labels
		N	L	R	A	V	/	a	!	F	x	j	f	E	J	e	Q
True labels	N	64996	9	3	58	126	1	0	8	28	4	32	4	0	0	0	1
	L	6	4829	0	0	7	0	0	0	0	0	0	0	1	0	0	0
	R	8	0	4336	6	2	0	1	0	0	0	0	0	0	0	0	0
	A	169	1	20	1321	9	0	0	1	0	0	6	1	0	0	0	0
	V	62	4	1	2	4183	0	1	5	18	0	0	1	0	0	0	0
	/	5	0	0	0	1	4203	0	0	0	0	1	6	0	0	0	0
	a	11	2	0	4	7	0	49	2	0	0	0	0	0	0	0	0
	!	8	0	0	0	5	0	1	221	0	1	0	0	0	0	0	0
	F	54	0	1	0	27	0	0	0	319	0	0	0	0	0	0	0
	x	4	0	0	1	4	0	1	2	0	84	1	0	0	0	0	0
	j	12	0	1	0	0	0	0	0	0	0	101	0	0	1	0	0
	f	16	0	0	0	1	24	0	0	0	0	0	450	0	0	0	0
	E	1	0	0	0	2	0	0	0	0	0	0	0	50	0	0	0
	J	1	0	2	0	0	0	0	0	0	0	1	0	0	37	0	0
	e	7	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
	Q	10	0	1	0	3	0	0	0	0	0	0	2	0	0	0	1

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表 4 提出的方法与其他方法的比较结果

Table 4. Comparison results of the proposed approach with other approaches

Reference	Features + Classifier	Accuracy/ %
Manual features only	DWT, RR + ELM (Single)	98.28
Deep feature only	1D CNN	98.50
Feature fusion (Without ensemble)	DWT, RR, 1D Convolution + ELM (Single)	98.81
Ye^[9]	ICA, Wavelet, RR + SVM (One-against-one)	98.72
Our proposed approach	DWT, RR, 1D Convolution + ELM (Bagging ensemble)	99.02

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