Automatic diagnosis of cardiovascular diseases using wavelet feature extraction and convolutional capsule network

Boujnouni, Imane El; Harouchi, Badr; Tali, Abdelhak; Rachafi, S.; Laaziz, Yassin

doi:10.1016/j.bspc.2022.104497

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…This method classifies ECG segments and diagnose eight cardiovascular conditions. This method seeks to automate ECG analysis, reduce diagnostic errors, and expedite the diagnostic process by integrating wavelet feature extraction with CapsNet, thus pushing the frontiers of cardiovascular disease diagnosis 44 .…”

Section: Related Workmentioning

confidence: 99%

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

Jayaraman Rajendiran,

Ganesh Babu,

Priyadharsini

et al. 2024

Sci Rep

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The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient’s health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

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Section: Related Workmentioning

confidence: 99%

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

Jayaraman Rajendiran,

Ganesh Babu,

Priyadharsini

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Butub et al [27] successfully used CapsNet to automatically learn relevant features from electrocardiogram signals to achieve the automatic detection of coronary artery disease (CAD), further emphasizing the importance of CapsNet in medical applications. El Boujnouni et al [28] combined a capsule network with the wavelet decomposition image method to automatically diagnose cardiovascular diseases, once again demonstrating the superiority of CapsNet in processing small datasets. In addition, a study on bladder cancer detection [29] clearly demonstrated that CapsNet could be trained from smaller datasets, which is very beneficial for medical imaging diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Heart Murmur Classification Using a Capsule Neural Network

Tsai,

Liu,

Zheng

et al. 2023

Bioengineering

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The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over the past few decades, methods for automated segmentation and classification of heart sounds have been widely studied. In many cases, both experimental and clinical data require electrocardiography (ECG)-labeled phonocardiograms (PCGs) or several feature extraction techniques from the mel-scale frequency cepstral coefficient (MFCC) spectrum of heart sounds to achieve better identification results with AI methods. Without good feature extraction techniques, the CNN may face challenges in classifying the MFCC spectrum of heart sounds. To overcome these limitations, we propose a capsule neural network (CapsNet), which can utilize iterative dynamic routing methods to obtain good combinations for layers in the translational equivariance of MFCC spectrum features, thereby improving the prediction accuracy of heart murmur classification. The 2016 PhysioNet heart sound database was used for training and validating the prediction performance of CapsNet and other CNNs. Then, we collected our own dataset of clinical auscultation scenarios for fine-tuning hyperparameters and testing results. CapsNet demonstrated its feasibility by achieving validation accuracies of 90.29% and 91.67% on the test dataset.

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Deep Learning for Predictive Modeling of Cardiovascular Disease: Optimizing Neural Networks

Hossain,

Amin,

Eishah

et al. 2024

Lecture Notes in Networks and Systems

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Automatic diagnosis of cardiovascular diseases using wavelet feature extraction and convolutional capsule network

Cited by 10 publications

References 26 publications

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

Heart Murmur Classification Using a Capsule Neural Network

Deep Learning for Predictive Modeling of Cardiovascular Disease: Optimizing Neural Networks

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