ECG Beats Extraction and Classification Using Radial Basis Function Neural Networks

Belkheiri, Mohammed; Douidi, Zineb; Belkheiri, Ahmed

doi:10.1007/978-81-322-1000-9_12

Cited by 6 publications

(2 citation statements)

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“…As the considered research problem in this thesis is of complex in nature, the developed enhanced GSO is employed to compute the optimal feature subsets and as well to train the RBFNN used for brain tumor classification [11][12][13][14][15], [17][18].…”

Section: End End End Stopmentioning

confidence: 99%

Brain Tumor Classification by EGSO Based RBFNN Classifier

Sharma R*,

Sungheetha,

Nuradis

2020

IJRTE

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Tumor classifier is modelled employing a proposed Enhanced Group Search Optimizer based Radial Basis Function Neural Network model is applied in this research contribution to acquire the ideal instances from the developed VOI instance an as well EGSO is utilized to optimize the weight values of the Radial Basis Function Network classifier by limiting the mean square mistake. The anticipated EGSO based RBFNN classifier brings better characterization precision and accomplished insignificant error with quicker process. The simulation results computed prove the effectiveness of the RBFNN classifier to be better in comparison with the other proposed classifiers in this thesis and that available in the literature. The proposed pattern evaluation technique presents an automatic cancer categorization procedure thru the ultimate facets which fantastic characterizes MRI brain image is benign and malignant cancers. The planned method may perhaps stretch to categorize exceptional classes of tumor (eg. Meningioma, glioma etc.,) and depth of malignancy.

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Section: End End End Stopmentioning

confidence: 99%

Brain Tumor Classification by EGSO Based RBFNN Classifier

Sharma R*,

Sungheetha,

Nuradis

2020

IJRTE

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“…The arrangement of ECG for various cardiac sicknesses is one of the important tasks. Consequently, the trademark rushes of ECG must be recognized for effective characterization [15].The third problem is related to robust and effective feature extraction from ECG wave segments and classification.…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

Goyal

2022

Preprint

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Since the last decade, the Electrocardiogram (ECG) tool has received medical experts and researchers' attention for accurate and fast diagnosis of cardiovascular diseases (CVD). The automatic detection and classification of 1D ECG-based heart disease have become more realistic and efficient solutions using the deep learning technique such as Convolutional Neural Network (CNN). As CNN is designed mainly for 2D or 3D applications therefore, designing CNN to process 1D ECG becomes challenging. We proposed a novel framework for automatic CVD detection and classification from the raw ECG signals using 1D-CNN deep learning technique. The framework consists of pre-processing, automatic feature extraction, feature optimization, and classification. In pre-processing, raw ECG signals are filtered to remove the baseline drift and power line interference using median and notch filters respectively. The pre-processed ECG signals are then used to extract the QRS and ST waves using the dynamic thresholding in the wavelet transfer domain. The fusion of QRS and ST waves have fed to automatic 1D-CNN that consists of layers i.e,1D convolutional layer, ReLU layer, and max-pooling layers. The 1D-CNN is proposed in this paper to extract features automatically with little computing complexity. The high-dimensional raw CNN features are optimized by applying a feature selection and scaling approach. For classification, different soft computing techniques such as Long- Short Term Memory (LSTM), Support Vector Machine (SVM), Naïve Bays (NB), Artificial Neural Network (ANN), and k-nearest neighbor (KNN) are applied. The experimental performances of the proposed model have been investigated on a publicly available research dataset and outperformed recent CNN-based techniques.

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