An Efficient Heartbeats Classifier Based on Optimizing Convolutional Neural Network Model

Maghawry, Eman A.; Gharib, Tarek F.; Ismail, Rasha M.; Zaki, Mohammed J.

doi:10.1109/access.2021.3128134

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…The high energy components of IMF stage 1 are represented by: 𝐶 21 = 𝐶 31 + 𝐶 32 (12) Finally, the Hilbert Transform is defined as:…”

Section: Figure 5 Steps In Obtaining the Hht Of Pcg Signalmentioning

confidence: 99%

An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

Vinay,

Vidyasagar,

Rohith

et al. 2024

IEEE Access

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The cardiovascular system is responsible for carrying the blood along with nutrition and oxygen throughout the body. This system consists of heart, blood, and blood vessels. The experts, or doctors called as cardiologists, analyze the sounds of heart's (lub-dub) beat and flow of blood to diagnose Cardio Vascular Disease (CVD) using a traditional stethoscope and phonological cardiogram technique. Through the stethoscope, the cardiologist will listen to vibration produced by heart beat and heart beat sound and murmur sound are popularly known as phonocardiogram (PCG) signals, which are being recorded for medical diagnosis purposes using a stethoscope. The development of a technique for the automatic recognition of HVD's assists the experts in recognizing the CVD effectively in the initial stage itself from PCG signals.There are many tools available to help doctors in a clinical setting for the accurate diagnose the CVD in a less time. The main aim of this proposed work is to provide an Artificial Intelligence (AI) based PCG signal analysis for the automatic and early detection of various cardiac conditions using supervised and unsupervised Recurrent Neural Network (RNN) based Bidirectional Long Short-Term Memory (Bi-LSTM) Machine Learning (ML) algorithm. Along with this algorithm, Generative Adversarial Networks (GAN's) is considered because they can create fresh, high-quality, pseudo-real data that resembles their training set which has been demonstrated by using their two unique networks: Discriminator Network (DN) and the Generator Network (GN). The proposed method is tested using heart sound signals from the well-known, freely accessible PhysioNet and Kaggle datasets. The Experimental results are validated based accuracy, precision, F1-score, sensitivity, and specificity.

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“…The high energy components of IMF stage 1 are represented by: 𝐶 21 = 𝐶 31 + 𝐶 32 (12) Finally, the Hilbert Transform is defined as:…”

Section: Figure 5 Steps In Obtaining the Hht Of Pcg Signalmentioning

confidence: 99%

An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

Vinay,

Vidyasagar,

Rohith

et al. 2024

IEEE Access

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show abstract

“…Where, the column vector is denoted as 𝛼. Eqs. ( 10) and (11), consider that the anticipated samples 𝛷(𝑥) are centered in ℜ 𝑓 . The kernel function used by KPCA is the radial basis function.…”

Section: Dimension Reduction Using Hfdrmentioning

confidence: 99%

“…The manual evaluation of ECG curves in a beatby-beat manner is imprecise, tiresome, and timeconsuming [10]. Hence, automatic analysis and classification of heart disease approaches are used for supporting cardiologists to make the best decisions for enhancing disease identification accuracy [11] International Journal of Intelligent Engineering and Systems, Vol. 16, No.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Feature Dimension Reduction and SVM for a Classification of Heart Rate Variability

2023

IJIES

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Heart rate variability (HRV) is the variations in the time interval among heartbeats which indicates the status of the overall cardiac health. Reduced or abnormal HRV is related with an increased death risk. However, the classification of HRV is a challenging task because of the huge number of features processed during the diagnosis. In this research, an effective HRV classification is obtained using the hybrid feature dimension reduction (HFDR) technique along with the support vector machine (SVM) classifier. The HFDR is the combination of kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) which is used to reduce the dimension of the overall feature vector. Three different feature extraction approaches such as continuous wavelet transform (CWT), autoregressive (AR) and spectrogram are used to understand the nonlinear and non-stationary features from the R peak obtained using hybrid R peak (HRP) detection. The reduction in feature dimension using HFDR is used to improve the classification of HRV. The electrocardiogram (ECG) signals from the MIT-BIH arrhythmia database of Physio.Net is used for analyzing the proposed HFDR-SVM. The HFDR-SVM method is evaluated using accuracy, precision, recall, sensitivity, specificity, and F-measure. The existing researches of forward back-propagation neural network (FFBPNN), weight-based ReliefF (WReliefF) with genetic algorithm (GA)-SVM, Alexnet-SVM and long short term memory with angle transform (LSTM-AT) are used for comparing the HFDR-SVM method. The accuracy of the HFDR-SVM is 99.86% that is higher than the FFBPNN, WReliefF-GA-SVM, Alexnet-SVM and LSTM-AT.

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Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach

Jha

2024

Computer Methods in Biomechanics and Biomedical Engineering

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An Efficient Heartbeats Classifier Based on Optimizing Convolutional Neural Network Model

Cited by 4 publications

References 41 publications

An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

A Hybrid Feature Dimension Reduction and SVM for a Classification of Heart Rate Variability

Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach

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