ECG images classification using artificial neural network based on several feature extraction methods

Tayel, Mazhar B.; El-Bouridy, M.E.

doi:10.1109/icces.2008.4772977

Cited by 18 publications

(14 citation statements)

References 2 publications

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“…geometric analysis [19], difference operation method [20], dynamic threshold [21], spectral analysis [22], Cumulative Sums of Squares [23] etc. Moreover, researchers showed that the neural networks with different configurations were widely used for the classification of ECG signals [24]- [31]. There are also many other classification techniques used.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

Pandit

Zhang

Aslam

et al. 2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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This paper presents an investigation into the development of an efficient scheme to detect abnormal beat from lead II Electro Cardio Gram (ECG) signals. Firstly, a fast ECG feature extraction algorithm was proposed which could extract the locations, amplitudes waves and interval from lead II ECG signal. We then created 11 customized features based on the outputs of the feature extraction algorithm. Then, we used these 11 features to train an artificial neural network and an ensemble classifier respectively for detecting the abnormal ECG beats. Three manually annotated databases were used for training and testing our system: MIT-BIH Arrhythmia, QT and European ST-T database availed from Physionet databank. The results showed that for an abnormal beat detection, the neural network classifier had an overall accuracy of 98.73% and the ensemble classifier with AdaBoost had 99.40%. Using time domain processing approach, the proposed scheme reduced overall computational complexity as compared to the existing methods with an aim to deploy on the mobile devices in the future to promote early and instant abnormal ECG beat detection.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

Pandit

Zhang

Aslam

et al. 2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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“…Assuming T 0 as period of periodic signal f(t) that could be demonstrated by the Fourier series [7,8]:…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

A Comparative Approach to ECG Feature Extraction Methods

Vaneghi

Oladazimi

Shiman

et al. 2012

2012 Third International Conference on Intelligent Systems Modelling and Simulation

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“…The wavelet coefficients, the approximation, horizontal, vertical and detail coefficients are widely being used as feature vectors in various image classification and image retrieval applications. The statistical moments of wavelet detail coefficients are used for ECG image classification [9]. The study and analysis of electromyography signals have application in sports science.…”

Section: Introductionmentioning

confidence: 99%

ISAR Image Classification with Wavelet and Watershed Transforms

Mamatha

Kumar

2016

IJECE

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<p>Inverse Synthetic Aperture Radar images are playing a significant role in classification of sea and air targets. First we acquire the ISAR images of targets using a sensor like radar and extract the characteristics of targets from the ISAR images in the form of feature vectors. The computed feature vectors are used for classification of targets. In this work, widely used and efficient segmentation tool Watershed transform and the multi resolution technique wavelet transform are explored to derive the target features. An artificial neural network based classifier is used for classification. The Wavelet analysis divides the information of an image into approximation and detail sub signals. The approximate and three detail sub signal values are taken as feature vectors and given as input to the classifier for ship ISAR image classification. The widely used segmentation technique, Watershed transform is applied to the ISAR images. The wavelet coefficients are computed for the segmented ISAR images and used as feature vectors for classification of the ISAR images. Also, the statistical moments mean and standard deviation are computed for the color ISAR images itself, taken in RGB format. These statistical color moments are used as feature vector. The classification accuracy is compared for the feature vectors.</p>

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ECG images classification using artificial neural network based on several feature extraction methods

Cited by 18 publications

References 2 publications

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

A Comparative Approach to ECG Feature Extraction Methods

ISAR Image Classification with Wavelet and Watershed Transforms

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