Heart disease detection using deep learning methods from imbalanced ECG samples

Rath, Adyasha; Mishra, Debahuti; Panda, Ganapati; Satapathy, Suresh Chandra

doi:10.1016/j.bspc.2021.102820

Cited by 71 publications

(41 citation statements)

References 38 publications

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“…The loss function is derived using the output of D, and t hereforeit updates parameters more quickly, whereas G does not always converge. Another drawback of the GAN model is that, for diverse random inputs, G produces similar outputs [ 54 ]. In the original DCGAN model, CNN architecture is integrated into unsupervised learning training to alleviate this problem and boost generative effect [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

et al. 2021

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The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

et al. 2021

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“…In recent years, the DM model plays a substantial role in medical image processing and an emergent field that converts the piece of information into resourceful data 12 . The model provides the authorized perform to get the options and make an appropriate decision during an emergency.…”

Section: Related Workmentioning

confidence: 99%

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

Kannan¹

2022

Concurrency and Computation

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With the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing the healthcare quality and assists in taking better decisions making during emergency times. Most of the existing research concentrates on modeling an automated prediction model for heart disease and the risk factors. Nevertheless, accurate classification is a vital challenge in heart disease diagnosis where the managing of high‐dimensional data increases the execution time of existing classifiers. In this paper, a new ensemble model has been proposed with the aid of random subspace and K‐nearest neighbor (RSS‐KNN) scheme for earlier prediction of heart disease. Primarily, the proposed scheme implements an isolation‐based outlier removal mechanism to eradicate the noises and outliers in the distributed data. Subsequently, the essential features are identified using RSS by varying the testing and training errors in the evaluation phase. The extracted features are then fed into KNN for the accurate classification of heart disease. Finally, an enhanced squirrel optimizer has been employed in the proposed scheme to obtain the global results which balance the exploration as well as exploitation issues and eliminate the over‐fitting problems. The simulation results manifest that the accuracy (without features) of the proposed ensemble RSS‐KNN scheme in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, and specificity is 98.10% when compared with existing state‐of‐the‐art classifiers.

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“…According to WHO, cardiovascular disease (CVD) is a significant reason of death worldwide, with 17.8 million deaths every decade (Rath et al, 2021). The American Cardiac Organization (Zhang and Xu, 2021) specifies detailed indications like sleep disorders, slight pain increase as well as a drop-in heart rate and fast weight improvement (up to 1.5-2.5 kg per 7 days) (Vincent Paul et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network

Saikumar

Rajesh²,

Srivastava³

et al. 2022

Front. Comput. Neurosci.

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Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body parts, however sometimes blood vessels become clogged or restrained due to cardiac issues. Past heart diagnosis applications are outdated and suffer from poor performance. Therefore, an intelligent heart disease diagnosis application design is required. In this research work, internet of things (IoT) sensor data with a deep learning-based heart diagnosis application is designed. The heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the deep graph convolutional network (DG_ConvoNet) deep learning network. The testing data has been collected from the Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis. DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application achieves an accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% implementing the proposed model.

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Heart disease detection using deep learning methods from imbalanced ECG samples

Cited by 71 publications

References 38 publications

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network

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