A wavelet optimization approach for ECG signal classification

Daamouche, Abdelhamid; Hamami, Latifa; Alajlan, Naif; Melgani, Farid

doi:10.1016/j.bspc.2011.07.001

Cited by 166 publications

(75 citation statements)

References 23 publications

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“…Since the coefficients of DWT in different levels have different discrimination power, it is significant to select those that can best represent the ECG signals for classification. Daamounche et al proposed a novel algorithm for generating the wavelet that best represents the ECG beats in terms of discrimination ability using a particle swarm optimization framework [12]. Wavelet packet decomposition (WPD) is an extension version of DWT.…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Using Wavelet Packet Entropy and Random Forests

Zhou

2016

Entropy

412

200

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Abstract:The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much existing research did not consider the separation of training and testing samples from the same set of patients (so called inter-patient scheme). To cope with these issues, in this paper, we propose a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme. Specifically, we firstly decompose the ECG signals by wavelet packet decomposition (WPD), and then calculate entropy from the decomposed coefficients as representative features, and finally use RF to build an ECG classification model. To the best of our knowledge, it is the first time that WPE and RF are used to classify ECG following the AAMI recommendations and the inter-patient scheme. Extensive experiments are conducted on the publicly available MIT-BIH Arrhythmia database and influence of mother wavelets and level of decomposition for WPD, type of entropy and the number of base learners in RF on the performance are also discussed. The experimental results are superior to those by several state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification.

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Section: Introductionmentioning

confidence: 99%

ECG Classification Using Wavelet Packet Entropy and Random Forests

Zhou

2016

Entropy

412

200

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“…However, the non-specificity of the mother wavelets for the corresponding application was not accounted. The proposed work establishes the significance of the application specific wavelets with an average accuracy of 85%.An example for application-specific mother wavelet design was proposed in [23] where the wavelet coefficients were optimized for ECG beat classification. The customization was done through Particle Swarm Optimization and the accuracy was around 75%.…”

Section: Discussionmentioning

confidence: 78%

Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

Raja¹,

Gangatharan²

2015

Journal of Electrical Engineering and Technology

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-Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians' effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10-fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent-Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

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“…Daamouche, L. Hamami, N. Alajlan, and F. Melgani [11] presented a wavelet optimization strategy depends on the mixture of the poly phase representation of wavelets and PSO. This strategy finds the wavelets that indicate the beats of discrimination capability calculated through an empirical measure of the classifier efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Arrhythmia Detection Based on Hybrid Features of T-wave in Electrocardiogram

Raghu¹,

Meshram²

2018

IJIES

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An Electrocardiogram (ECG) is used as one of the important diagnostic tools for the detection of the health of a heart. An automatic heart abnormality identification methods sense numerous abnormalities or arrhythmia and decrease the physician's pressure as well as share their work load. In ECG analysis, the main focus is to enhance degree of accuracy and include a number of heart diseases that can be classified. In this research paper, arrhythmia classification is proposed using Hybrid features of T-wave in ECG. The classification system consists of majorly three phases, windowing technique, feature extraction and classification. In feature extraction phase various features are used such as Differential Entropy (DE), Peak-Magnitude RMS ratio, Auto Regressive feature based Yule Walker, Burgs method. In classification phase Deep Neural Network (DNN) classifier is used. This classifier categorizes the normal and abnormal signals efficiently. The experimental analysis showed that, the Hybrid features Arrhythmia classification performance of accuracy approximately 98.3%, Specificity 98.0% and Sensitivity 98.6% using MIT-BIH database.

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A wavelet optimization approach for ECG signal classification

Cited by 166 publications

References 23 publications

ECG Classification Using Wavelet Packet Entropy and Random Forests

ECG Classification Using Wavelet Packet Entropy and Random Forests

Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

Arrhythmia Detection Based on Hybrid Features of T-wave in Electrocardiogram

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