基于多重分形理论的心电诊断系统设计与应用

doi:10.11959/j.issn.2096-109x.2017.00204

Abstract

Abstract:

An automatic segmentation algorithm for ECG data using differential threshold method was designed,which could identify the various ECG cycles of continuous ECG data.And it could obtain the multifractal features of multiple fractal and generalize hurst index feature of ECG data,these features were used to train artificial neural network in order to classify ECG data,the accuracy of the classifier could reach 97%.An ECG diagnosis system was implemented,which can automatically identify ECG sequences that contain multiple ECG cycles,and can automatically ignore the incomplete ECG cycle data,and could annotate every cycle of ECG data.

Key words: MFDFA, scale-free interval, multifractal, neural network

CLC Number:

TP315

Chun-kai ZHANG,Ao,ZHANGJing-wang YIN,Pan-bo TIAN,Ying ZHOU,Chang-qing QI. Design and application of electrocardiograph diagnosis system based on multifractal theory[J]. Chinese Journal of Network and Information Security, 2017, 3(10): 62-71.

Figures/Tables 15

References 22

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信号	最小值	最大值	差值
NB	0.287	2.338	2.050
LB	0.575	2.134	1.559
RB	0.380	2.552	2.171
PB	0.439	3.763	3.325
AB	0.296	1.855	1.559

特征列表	迭代次数平均值	准确度	准确度的标准差
F₀	412	86.93%	0.161 5
F ₀∪F₁	461	91.47%	0.236 7
F ₀∪F₂	495	89.17%	0.793 1
F ₀∪F₁∪F₂	498	92.36%	0.578 3

隐藏层神经元数目	迭代次数的平均值
隐藏层神经元数目	F₀	F ₀∪F₁	F ₀∪F₂	F ₀∪F₁∪F₂
10	412	461	495	498
20	520	551	501	532
30	539	573	546	672
40	610	683	661	714

隐藏层神经元数目	准确度的平均值
隐藏层神经元数目	F₀	F ₀∪F₁	F ₀∪F₂	F ₀∪F₁∪F₂
10	86.93%	91.47%	89.17%	92.36%
20	87.46%	94.69%	93.67%	95.93%
30	89.72%	95.34%	94.82%	97.53%
40	90.12%	94.13%	93.87%	97.02%

隐藏层神经元数目	准确度的标准差
隐藏层神经元数目	F₀	F ₀∪F₁	F ₀∪F₂	F ₀∪F₁∪F₂
10	0.161 5	0.236 7	0.793 1	0.578 3
20	0.172 9	0.376 4	0.912 3	0.627 3
30	0.167 3	0.276 7	0.847 1	0.557 3
40	0.156 5	0.251 8	0.792 7	0.491 8

Design and application of electrocardiograph diagnosis system based on multifractal theory

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Figures/Tables 15

References 22

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模型	准确率
模型	NB	LB	RB	PB	AB
分形模型	95.97%	94.04%	96.64%	98.68%	93.33%
混沌模型	95.33%	95.33%	94.00%	95.33%	91.33%
小波模型	92.66%	92.00%	94.00%	94.67%	90.66%

信号	TP	FP	FN	P	R	F1
NB	143	6	7	0.959 7	0.953 3	0.956 5
LB	142	9	7	0.940 4	0.953 0	0.946 7
RB	144	5	6	0.966 4	0.960 0	0.963 2
PB	150	2	4	0.986 8	0.974 0	0.980 4
AB	140	10	8	0.933 3	0.945 9	0.939 6

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