NDLI: Automatic ECG arrhythmias classification scheme based on the conjoint use of the multi-layer perceptron neural network and a new improved metaheuristic approach

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Automatic ECG arrhythmias classification scheme based on the conjoint use of the multi-layer perceptron neural network and a new improved metaheuristic approach

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IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Automatic ECG arrhythmias classification scheme based on the conjoint use of the multi-layer perceptron neural network and a new improved metaheuristic approach

Content Provider	IET Digital Library
Author	Bouaziz, Fatiha Oulhadj, Hamouche Boutana, Daoud Siarry, Patrick
Abstract	The authors have proposed a new automatic classification scheme based on the conjoint use of the multi-layer perceptron (MLP) neural network and an enhanced particle swarm optimisation (EPSO) algorithm for its training. In this work, six predominant categories of heartbeats from MIT-BIH database are considered, which are: normal, premature ventricular contraction, atrial premature contraction, right bundle branch block, left bundle branch block and paced beats. First, the authors have applied the standard particle swarm optimisation (PSO) algorithm to select the network structure for each features vector. Then, the relevant electrocardiogram (ECG) features to the studied arrhythmias were chosen, which suited to the optimised training performance of the classifier. The recognition performance of the proposed EPSO-MLP classification system is evaluated considering two different versions of the EPSO algorithm. In the first version (EPSOw), the inertia weight factor of the PSO algorithm is proposed to be a variable with iterations. However, two PSO parameters are taken to be variables in the second version of the improved learning algorithm (EPSOwc). The obtained experimental results prove the enhancement of the convergence ability of the MLP neural network and confirm the superiority of the proposed EPSO-MLP classification scheme on comparison with the other last published classification systems.
Starting Page	726
Ending Page	735
Page Count	10
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 8, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5465
Journal	IET Signal Processing
Publisher Date	2019-08-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Atrial Premature Contraction Automatic Classification Scheme Bioelectric Signal Biology And Medical Computing Digital Signal Processing Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique Enhanced Particle Swarm Optimisation Algorithm EPSO Algorithm EPSO-MLP Classification Scheme EPSO-MLP Classification System Feature Vector Improved Learning Algorithm Improved Metaheuristic Approach Knowledge Engineering Technique Learning in AI Left Bundle Branch Block Medical Signal Processing MIT-BIH Database MLP Neural Network Multilayer Perceptron Neural Network Multilayer Perceptrons Network Structure Neural Computing Technique Normal Contraction Optimisation Technique Particle Swarm Optimisation Premature Ventricular Contraction PSO Algorithm PSO Parameter Published Classification System Recognition Performance Signal Classification Signal Processing And Detection Standard Particle Swarm Optimisation Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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