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ECG data compression using a neural network model based on multi-objective optimization

Bo Zhang, Jiasheng Zhao, Xiao Chen, Jianhuang Wu, Quan Zou
2017 PLoS ONE  
OPEN ACCESS Citation: Zhang B, Zhao J, Chen X, Wu J (2017) ECG data compression using a neural network model based on multi-objective optimization. PLoS ONE 12(10): e0182500.  ...  Upon testing, we demonstrated that the proposed ECG data compression method based on multi-objective optimization neural network is effective and efficient in clinical practice.  ...  Fig 8 . 8 Average percentage root-mean-squared difference (PRD) results based on different ECG data compression ratios using transform and neural network approaches (A); Average encoding time versus ECG  ... 
doi:10.1371/journal.pone.0182500 pmid:28972986 pmcid:PMC5626036 fatcat:ow7crqdwxvdbvmzgxgrttnaige

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Neha Arora, Biswajit Mishra
2021 IEEE Access  
[255] presented a method for automatic diagnosis of the 12-lead ECG using a deep neural network.  ...  An arrhythmia detection from a 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network was presented in [252] .  ...  This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see  ... 
doi:10.1109/access.2021.3119630 fatcat:kbxghskiu5f5hc3vrmi6yrysba

ART Neural Networks for Medical Data Analysis and Fast Distributed Learning [chapter]

Gail A. Carpenter, Boriana L. Milenova
2000 Artificial Neural Networks in Medicine and Biology  
expert systems and neural networks, in order to create a hybrid method for medical diagnosis.  ...  ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas.  ...  Neural networks that employ enor-based leaming include back propagation [10] and other multilayer perceptrons (MLPs).  ... 
doi:10.1007/978-1-4471-0513-8_2 dblp:conf/annimab/CarpenterM00 fatcat:2mx25gfjcve63kgealmhwtl6ha

Automated ECG Diagnosis

Upasani D.E.
2012 IOSR Journal of Engineering  
They are based on different methodological approaches, which include digital signal analysis, rule-based techniques, fuzzy logic methods and artificial neural networks, with each one of them exhibiting  ...  Myocardial ischemia & other cardiac disorder diagnosis using long duration electrocardiographic recordings is a simple and non-invasive method that needs further development in order be used in the everyday  ...  ., (2005) used block-based neural networks to classify ECG Signals. Fira et al., (2008) proposed an ECG compressed technique and its validation using NN"s.  ... 
doi:10.9790/3021-020512651269 fatcat:ihuvjetstveyhgxuybquzbh2oe

ECG Feature Extraction Techniques - A Survey Approach [article]

S. Karpagachelvi, M.Arthanari, M. Sivakumar
2010 arXiv   pre-print
The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and other Signal Analysis techniques.  ...  ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves.  ...  Jen et al. in [20] formulated an approach using neural networks for determining the features of ECG signal. They presented an integrated system for ECG diagnosis.  ... 
arXiv:1005.0957v1 fatcat:elga44wuwngw3cumj2zsk33fhe

Origins of ECG and Evolution of Automated DSP Techniques: A Review [article]

Neha Arora, Biswajit Mishra
2021 arXiv   pre-print
This review focuses on the evolution of the ECG, and covers the most recent signal processing schemes with milestones over last 150 years in a systematic manner.  ...  It also provides recommendations for the inclusion of certain important points based on the review.  ...  Disease Diagnosis in MITDB Frequency based 80% lesser ECG diagnosis Frequency domain Neural Networks time at 5% Accuracy lose compared to temporal methods Table 5 : 5 Time-Frequency domain  ... 
arXiv:2105.08938v1 fatcat:4bf6ojrlqrg5hlhdwe2gbi2xua

Research and Development of Electrocardiogram P-wave Detection Technology

Zhang Hongjun
2015 Open Automation and Control Systems Journal  
In this study, a brief overview on the ECG P-wave automatic detection was presented, including the P-wave of ECG preprocessing techniques and several common detection methods.  ...  Finally ECG P-wave direction was identified.  ...  Neural Network Filtering Neural network approach has been used to explain many complex physiological phenomena, such as ECG and EEG recognition, ECG signal compression, recognition and processing of medical  ... 
doi:10.2174/1874444301507011981 fatcat:zgfdxuqskzcwfctgq522mmyq2u

A Survey Approach on ECG Feature Extraction Techniques

Shalini Sahay, A.K.Wadhwani A.K.Wadhwani, Sulochana Wadhwani
2015 International Journal of Computer Applications  
With the implementation of artificial neural network (ANN) the compression ratio increases as the number of ECG cycle increases.  ...  Tayel and Bouridy together in [12] put forth a technique for ECG image classification by extracting their feature using wavelet transformation and neural networks.  ...  The feature extraction technique or algorithm developed for ECG must be highly accurate and should ensure fast extraction of features from the ECG signal.  ... 
doi:10.5120/21268-4002 fatcat:d65ogcwvn5clzkveabwus4agiy

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

Junjun Chen, Hong Pu, Dianrong Wang, Khin Wee Lai
2021 Journal of Healthcare Engineering  
This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing  ...  In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed.  ...  [17] proposed a neural network classification method based on the time characteristics of the EEG signal. Katheria et al.  ... 
doi:10.1155/2021/6284035 pmid:34306595 pmcid:PMC8272660 fatcat:s2xalff52bdevfhphtj6k4sgvy


2014 Journal of Computer Science  
Frequency domain extracted features are classified using Radial Basis Function (RBF) and proposed Spline Activated-Feed Forward Neural Network (SA-FFNN).  ...  ECG classification for arrhythmia is investigated in this paper based on soft computing techniques.  ...  Diagnosis is based on long term data through an ECG recorder like the Holter recorder.  ... 
doi:10.3844/jcssp.2014.1582.1590 fatcat:g4j5mgt2kne57lc5ipgknwfu6q

Bagged tree classi cation of arrhythmia using wavelets for denoising, compression, and feature extraction

2018 Turkish Journal of Electrical Engineering and Computer Sciences  
After deciding the features, the performance of the basic classification methods and spiking neural network was checked to determine whether there was a better classifier to be used for our research.  ...  Data were compressed and preprocessed (denoising, trend elimination, baseline correction, and normalization) before being sent to the system for feature calculation.  ...  The kernel scale was chosen as automatic for linear, quadratic, and cubic SVM; therefore, to select the scale value, a heuristic procedure was used.  ... 
doi:10.3906/elk-1706-247 fatcat:kuuvugjr5jguvcjbisfqiqabki

Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification

V. S. R. Kumari
2015 Communications  
This study proposes multi-layer perceptron neural network optimization using Genetic Algorithm (GA) to classify ECG arrhythmia.  ...  ECG arrhythmia detection accuracy improves by using machine learning and data mining methods.  ...  The author suggested a method to classify heart arrhythmia from ECG signals by using Block-based Neural Networks (BbNN).  ... 
doi:10.11648/ fatcat:oxwvx6ehnzdyfk7jzqid73q55a

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [article]

Shenda Hong, Yuxi Zhou, Junyuan Shang, Cao Xiao, Jimeng Sun
2020 arXiv   pre-print
Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results.  ...  Methods:We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between Jan. 1st of 2010 and Feb. 29th of 2020 from Google Scholar, PubMed, and the DBLP  ...  However, there is some controversy regarding the use of multi-head ECG or time-frequency ECG spectrograms extracted using the WT, fast Fourier transform (FFT), or short-term Fourier transform.  ... 
arXiv:2001.01550v3 fatcat:ho7qhyqivzgn3hmitmg45stali

An Approach of Neural Network For Electrocardiogram Classification

Mayank Kumar Gautam, Vinod Kumar Giri
2020 APTIKOM Journal on Computer Science and Information Technologies  
The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier  ...  It acts as a vital physiologicalparameter, which is being used exclusively to know the state of the cardiac patients.  ...  Hence, the neural networks proved a milestone in the analysis of the ECG and provide great achievement in the diagnosis of cardiac diseases.  ... 
doi:10.34306/csit.v1i3.57 fatcat:kogtng5zjnfshmctsjpc7mpvny

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Liping Xie, Zilong Li, Yihan Zhou, Yiliu He, Jiaxin Zhu
2020 Sensors  
Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years.  ...  Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here.  ...  A deep convolutional auto-encoder (CAE) based compression model combined with LSTM networks to recognize ECG beats, which significantly reduced the training from 4.5 h to 0.6 h.  ... 
doi:10.3390/s20216318 pmid:33167558 pmcid:PMC7664289 fatcat:echda3mznbekrclhwj3e774gc4
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