ECG Decision Support System based on feedforward Neural Networks

Hela Lassoued; Raouf Ketata; Slim Yacoub

Open Access

ECG Decision Support System based on feedforward Neural Networks

Hela Lassoued

Raouf Ketata

and

Slim Yacoub

| Sep 12, 2018

International Journal on Smart Sensing and Intelligent Systems

Volume 11 (2018): Issue 1 (January 2018)

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Published Online: Sep 12, 2018

Page range: 1 - 15

DOI: https://doi.org/10.21307/ijssis-2018-029

Keywords
Machine learning, Decision Support System, ECG, Arrhythmia classification, Neural Networks

© 2018 Hela Lassoued et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Block diagram of an ECG arrhythmia classification topic.

Selected attributes.

Selected Features	Max-Q	Std-a6	Std- a5	Std- a7	Std- a4	Std- a3	Std- a2	Std-a1	std- a8	Moy-d8
Correlation rank	0.350	0.326	0.318	0.314	0.311	0.310	0.307	0.306	0.304	0.300

RBF performance using different spread values.

Basic function	Spread	ACC(%)	Test_time(s)	Time_response (s)	MSE
Inverse_multiquadric	10	99.9	0.121	0.643	0.250 e-30
	1	99.9	0.128	0.668	0.250 e-30
	0.1	99.9	0.140	0.676	0.250 e-30

MLP, RBF, and PNN Feed.Forward neural networks.

Criteria types	Criterion	MLP	RBF	PNN
Structural	Architecture	An input layer, one or more hidden layer and an output layer	An input layer, one hidden layer and an output layer	An input layer, one hidden layer, a summation layer and an output layer
	Activation function	The activation function is non-linear (sigmoid, log-sigmoid, tan-sigmoid.)	The activation function is a radial basis function which computes the Euclidian distance of the input vector and its weights	The activation function is based on the probability density function
	Number of hidden neurons	no defined principle for determining the number of neurons	no defined principle for determining the number of neurons	The number is equal to the number of instance
	Output layer	The final layer uses the activation function before linearly combining it	The final layer doesn’t use activation function, it rather linearly combines the output of the previous neuron	The final layer is a competitive output layer. It picks the maximum of the computed probabilities
	Training Process	Backpropagation training algorithms	Backpropagation or clustering algorithms	There is no computation of weights. The Bayesian decision rule
Parametric	Parameters	Momentum factor, learning rate, parameters according to the training algorithm	Number of centers, spread of radial function	Spread value of the probability density function

Performances of the PNN network according to the spread parameter.

Spread	ACC(%)	Test_time(s)	Time_response (s)	MSE
0.1	79.5	0.081	0.218	0.162
1	79.5	0.070	0.218	0.162
10	79.5	0.074	0.218	0.162

ANNs Performances.

ANN	N0/HL	ACC(%)	Tr_time(s)	Test_time(s)	Time_response (s)	MSE
MLP_opt	10	86.4	0.294	0.096	0.390	0.064
RBF_opt	20	99.9	0.755	0.121	0.876	0.250 e-30
PNN_opt	22	79.5	0.070	0.218	0.288	0.162

Comparative study with related works.

ANN	Pre-processing	Test conditions	Division Datasets	ACC(%)
(Abhinav-Vishwa et al., 2011)	R peak	MIT_BIH database of 48 signals of 30 min	50% for training and 50% for testing	96.8
(Rai et al., 2013)	Morphological and DWT coefficients	45 ECG signal of 1 min from MIT-BIH database	26 signals for training and 19 for testing	97.8
(Tomar et al., 2013)	Morphological, DWT coefficients, power spectral density and Energy of Periodogram	62 ECG signals of 10 s from MIT-BIH database and Normal Sinus Rhythm (NSR)	Cross validation division (70%, 30%)	98.4
(Savalia et al., 2017)	R peaks, the heart beats/min, the duration of complex QRS	66 ECG signal from MIT_BIH arrhythmia database and NSR database	Cross validation division (70%, 30%).	82.5
(Dalvi et al., 2016)	QRS complex, RR interval and the beat waveform morphology. PCA for feature selection	MIT_BIH database of 48 signals of 30 min.	18 ECG records for test dataset and 30 ECG for train dataset	96.9
Proposed work	Morphological and DWT coefficients	44 ECG signals of 1 min. recording from MIT_BIH database	22 signal for training and 22 for testing following the AAM.I recommendations	99.9

MLP performance with different learning algorithms.

Learning algorithms types	Learning algorithms	ACC(%)	Tr_time(s)	Test_time(s)	Time_response (s)	MSE
Jacobian derivatives	trainlm	86.4	0.222	0.024	0.246	0.094
	trainbr	74.5	0.229	0.031	0.260	0.124
Gradient derivatives	trainscg	86.4	0.553	0.355	0.908	0.077
	traingda	81.8	0.416	0.218	0.634	0.078
	trainrp	86.4	0.294	0.096	0.390	0.073
	traingdx	81.8	0.543	0.345	0.888	0.076
	trainbfg	86.4	0.330	0.132	0.462	0.087
	traincgb	81.8	0.316	0.118	0.434	0.070

MLP performance by learning rate.

Lr	ACC(%)	Time_response (s)	MSE
0.01	86.4	0.390	0.130
0.1	86.4	0.399	0.064
1	86.4	0.392	0.073

RBF performance using different N/HL.

NO/HL	ACC (%)	Tr_time(s)	Test_time(s)	Time_response (s)	MSE
5	54.5	0.380	0.015	0.395	0.204
10	59.1	0.436	0.013	0.449	0.150
15	81.8	0.501	0.014	0.515	0.080
20	99.9	0.640	0.014	0.654	1.266e-30
25	99.9	0.699	0.017	0.716	2.411e-30

PNN performance.

NO/HL	ACC(%)	Tr_time(s)	Test_time(s)	Time_response (s)	MSE
22	79.5	0.081	0.218	0.299	0.162

MLP performance using different number of hidden layer.

	NO/HL
N_HL	H1	H2	ACC(%)	Tr_time(s)	Test_time(s)	Time_response (s)	MSE
	5	0	72.7	0.221	0.113	0.334	0.026
	10	0	86.4	0.222	0.094	0.316	0.024
1	15	0	81.8	0.380	0.099	0.479	0.021
	20	0	81.8	0.390	0.073	0.463	0.016
	25	0	81.8	0.406	0.071	0.477	0.013
	10	5	81.8	0.315	0.238	0.553	0.019
2	10	10	81.8	0.319	0.253	0.572	0.021
	10	15	72.7	0.383	0.281	0.664	0.018
	10	20	72.7	0.423	0.317	0.740	0.017

RBF performance using different basic functions.

RBF functions	ACC(%)	Tr_time(s)	Test_time(s)	Time_response (s)	MSE
Gaussian	99.9	0.380	0.014	0.654	1.266e-30
Polyharmonic	99.9	0.436	0.051	0.639	1.221e-30
Inverse_multiquadric	99.9	0.501	0.121	0.676	0.250e-30
Multiquadric	99.9	0.640	0.132	0.698	0.891e-30
Biharmonic	99.9	0.699	0.002	0.640	0.891e-30

eISSN:: 1178-5608
Language:: English

Publication timeframe:: Volume Open
Journal Subjects:: Engineering, Introductions and Overviews, other

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ECG Decision Support System based on feedforward Neural Networks

Article Category: Research-Article

Published Online: Sep 12, 2018

Page range: 1 - 15

DOI: https://doi.org/10.21307/ijssis-2018-029

Keywords
Machine learning, Decision Support System, ECG, Arrhythmia classification, Neural Networks

© 2018 Hela Lassoued et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

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Figure 4

Figure 5

Figure 6

Selected attributes.

RBF performance using different spread values.

MLP, RBF, and PNN Feed.Forward neural networks.

Performances of the PNN network according to the spread parameter.

ANNs Performances.

Comparative study with related works.

MLP performance with different learning algorithms.

MLP performance by learning rate.

RBF performance using different N/HL.

PNN performance.

MLP performance using different number of hidden layer.

RBF performance using different basic functions.

ECG Decision Support System based on feedforward Neural Networks

Article Category: Research-Article

Published Online: Sep 12, 2018

Page range: 1 - 15

DOI: https://doi.org/10.21307/ijssis-2018-029

KeywordsMachine learning, Decision Support System, ECG, Arrhythmia classification, Neural Networks

© 2018 Hela Lassoued et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Selected attributes.

RBF performance using different spread values.

MLP, RBF, and PNN Feed.Forward neural networks.

Performances of the PNN network according to the spread parameter.

ANNs Performances.

Comparative study with related works.

MLP performance with different learning algorithms.

MLP performance by learning rate.

RBF performance using different N/HL.

PNN performance.

MLP performance using different number of hidden layer.

RBF performance using different basic functions.

Keywords
Machine learning, Decision Support System, ECG, Arrhythmia classification, Neural Networks