基于一維卷積特征與手工特征融合的集成超限學習機心跳分類方法

許越凡; 肖文棟; 曹征濤

doi:10.13374/j.issn2095-9389.2021.01.12.005

基于一維卷積特征與手工特征融合的集成超限學習機心跳分類方法

doi: 10.13374/j.issn2095-9389.2021.01.12.005

許越凡^{1, 2},
肖文棟^{1, 2, 3},
曹征濤^4, ,

1.
北京科技大學自動化學院，北京 100083
2.
北京市工業波譜成像工程技術研究中心，北京 100083
3.
北京科技大學順德研究生院，順德 528399
4.
空軍特色醫學中心，北京 100142

基金項目: 國家重點研發計劃課題資助項目（2017YFB1401203）；佛山市科技創新專項資金資助項目（BK20AF005）

詳細信息

通訊作者:
E-mail: czhengtao@126.com

中圖分類號: TP182
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-01-12
- 網絡出版日期: 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，提出了一種集成超限學習機心跳分類方法。手工提取的特征明確地表征了心電信號的特定特性，如相鄰心跳時間間隔反映了心跳信號的時域特性，小波系數反映了心跳信號的時頻特性。同時設計了一維卷積神經網絡對心跳信號特征進行自動提取。基于超限學習機（Extreme leaning machine，ELM），將上述特征融合進行心跳分類。由于ELM初始參數的隨機給定可能導致其性能不穩定，進一步提出了一種基于袋裝（Bagging）策略的多個ELM集成方法，使分類結果更加穩定且模型泛化能力更強。利用麻省理工心律失常公開數據集對所提方法進行了驗證，分類準確率達到了99.02%，實驗結果也表明基于融合特征的分類準確率高于基于單獨特征的分類準確率。
- 心跳分類 /
- 特征融合 /
- 一維卷積神經網絡 /
- 小波變換 /
- 集成超限學習機
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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圖 1 ELM的基本結構

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

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圖 2 心跳分類算法總體結構

Figure 2. Overall structure of the heartbeat classification algorithm

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圖 3 本文提出的1D CNN結構

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

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圖 4 ELM心跳分類結構

Figure 4. ELM heartbeat classification structure

下載: 全尺寸圖片幻燈片

表 1 MIT?BIH數據集的詳細描述和劃分

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 每類心跳的召回率和精確率

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 混淆矩陣

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 提出的方法與其他方法的比較結果

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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