Neural network and wavelet average framing percentage energy for atrial fibrillation classification

Daqrouq, Khaled; Alkhateeb, Abdulhameed F.; Ajour, Mohammed N.; Morfeq, Ali

doi:10.1016/j.cmpb.2013.12.002

Cited by 39 publications

(20 citation statements)

References 29 publications

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“…The nonterminating and the short-time terminating AF were successfully differentiated via the difference of log-energy entropies of two types of AF. In this paper, we use the percentages of energy obtained from the terminal nodes of the WP tree for CHF arrhythmias feature vector construction (from an ECG) to be used for diagnosing [ 1 ]. The proposed feature extraction method is summarized as follows: Preprocessing and normalization: prior to the stage of feature extraction, the ECG data are preprocessed and normalized to remove prospective fluctuations of baseline, interferences, noises, and so forth [ 1 ].…”

Section: Methodsmentioning

confidence: 99%

“…The percentages of energy corresponding to the terminal nodes of the WP tree ( E ) for the Z frames of u q ( t ) are utilized to extract a wavelet subsignal feature vector as follows:

where e ( u qz ( t )) is the percentage of energy of u qz ( t ). The feature vector of the whole given ECG signal is

To calculate the percentages of energy, the following equation is used:

where S is the signal and C is the wavelet decomposition vector [ 1 ]. The extracted features of wavelet average framing percentage energy will be added for classification.…”

Section: Methodsmentioning

confidence: 99%

“…These dilemmas make the heart disease detection very hard. Therefore, the using of pattern classifier techniques can improve the ECG arrhythmia diagnoses [ 1 – 4 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

Daqrouq

Dobaie

2016

Computational and Mathematical Methods in Medicine

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An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB.

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Section: Methodsmentioning

confidence: 99%

“…The percentages of energy corresponding to the terminal nodes of the WP tree ( E ) for the Z frames of u q ( t ) are utilized to extract a wavelet subsignal feature vector as follows:

where e ( u qz ( t )) is the percentage of energy of u qz ( t ). The feature vector of the whole given ECG signal is

To calculate the percentages of energy, the following equation is used:

where S is the signal and C is the wavelet decomposition vector [ 1 ]. The extracted features of wavelet average framing percentage energy will be added for classification.…”

Section: Methodsmentioning

confidence: 99%

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Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

Daqrouq

Dobaie

2016

Computational and Mathematical Methods in Medicine

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“…Patricia Melia et al [52] take the 15 class of data. K. Daqrouq et al [53] implement their classification technique on four type of arrhythmias diseases.…”

Section: Class Of Datamentioning

confidence: 99%

ECG Arrhythmia Classification Algorithms

Nainwal¹,

Kumar²,

Jha³

2019

IJRTE

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In last two decades lot of work is introduced on ECG classification. Authors took different database, features of ECG, class of data, learning and training algorithms to classify ECG signal. Normally class of data mentioned in source of database. Mainly three classification techniques which are discussed in this paper, these are support vectored machine (SVM), artificial neural network (ANN) and linear discriminate (LD). In this paper all the ECG classification based papers are analyzed and try to find out loophole and future challenges. This paper also discusses the different database of ECG signal

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“…A neural network algorithm has been studied for AF detection by Daqrouq et al (2014). The algorithm employs probabilistic neural network and utilises a wavelet feature of electrocardiogram.…”

Section: Introductionmentioning

confidence: 99%

Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

Nuryani

Harjito

Yahya

et al. 2017

IJBET

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This paper proposes a new technique for detecting atrial fibrillation (AF). The method employs electrocardiographic features and support vector machine (SVM). The features include descriptive statistics of electrocardiographic RR interval. The RR interval is the distance in time between two consecutive R-peaks of electrocardiogram. AF detections using SVM with different electrocardiographic features and different SVM free parameters are explored. Employing SVM with the optimal free parameters and all the proposed electrocardiographic features, we find an AF detection technique with a comparable performance. The best performance obtained by the technique is 98.47% and 97.84%, in terms of sensitivity and specificity.

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Neural network and wavelet average framing percentage energy for atrial fibrillation classification

Cited by 39 publications

References 29 publications

Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

ECG Arrhythmia Classification Algorithms

Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

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