Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network

Du, Chaofan; Liu, Peter; Zheng, Minhua

doi:10.1016/j.cmpb.2021.106483

Cited by 21 publications

(10 citation statements)

References 28 publications

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“…T. Araujo and colleagues used a convolutional neural network to diagnose breast cancer based on hematoxylin and eosin-stained breast biopsy images, and the model achieved 83.3% accuracy and 95.6% sensitivity ( 26 ). C. Du and colleagues applied a neural network to classify imbalanced electrocardiosignal data, achieving 98.45% accuracy and 97.03% sensitivity ( 27 ). T. Dratsch developed and validated a neural network to identify the 30 most common categories of plain radiographs, and the model showed 90.3% overall accuracy ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network

Gao

et al. 2023

Front. Immunol.

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BackgroundRemarkably, the anti-cancer efficacy of immunotherapy in lung adenocarcinoma (LUAD) has been demonstrated. However, predicting the beneficiaries of this expensive treatment is still a challenge.Materials and methodsA group of patients (N = 250) diagnosed with LUAD and receiving immunotherapy were retrospectively studied. They were randomly divided into a training dataset (80%) and a test dataset (20%). The training dataset was utilized to train neural network models to predict patients’ objective response rate (ORR), disease control rate (DCR), responders (progression-free survival time > 6 months), and overall survival (OS) possibility, which were validated by both the training and test datasets and packaged into a tool later.ResultsIn the training dataset, the tool scored 0.9016 area under the receiver operating characteristic (AUC) curve on ORR judgment, 0.8570 on DCR, and 0.8395 on responder prediction. In the test dataset, the tool scored 0.8173 AUC on ORR, 0.8244 on DCR, and 0.8214 on responder determination. As for OS prediction, the tool scored 0.6627 AUC in the training dataset and 0.6357 in the test dataset.ConclusionsThis immunotherapy efficacy predictive tool for LUAD patients based on neural networks could predict their ORR, DCR, and responder well.

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Section: Discussionmentioning

confidence: 99%

Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network

Gao

et al. 2023

Front. Immunol.

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“…Importantly, this work discussed an additional challenge of the imbalance problem in private or publicly available arrhythmia datasets, which may significantly impact the accuracy of arrhythmia diagnosis in real-life applications. Some interesting contributions have been demonstrated in the literature for resolving the challenge [12,[41][42][43]. We have used the weighted categorical cost function [44] to handle the imbalanced data in this study due to the function's several advantages.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Ullah

Heyat

Akhtar

et al. 2022

Computational Intelligence and Neuroscience

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Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.

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“…The RNN comprised layers of a CNN, LSTM and gated recurrent unit (GRU). Du et al [45] proposed a variational autoencoder (VAE) and auxiliary classifier generative adversarial network (ACGAN) to learn data distribution and synthesize images from minority class. CNN classifiers were employed to recognize arrhythmias using 2D ECG images.VAE and ACGAN required to be trained separately highlighting higher computational cost.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

et al. 2022

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Potentially lethal heart abnormalities can be detected/spotted with recent evolution in continuous, long-term cardiac health monitoring using wearable sensors. However, the huge data accumulated presents a challenge in terms of storage, knowledge extraction and computing time. Moreover, manual examination of long-term ECG recordings presents various problems like huge time and work demand, inter-observer variations and difficulty classifying complex non-linear single-lead ECG signal. To address these problems, we propose an automatic heartbeat classification system that uses the optimized minimum number of features using ECG time-series amplitude directly as input, without feature extraction and provides a primary classification and diagnosis for 1 normal and 14 types of arrhythmic heartbeats. Multi-objective particle swarm optimization (MOPSO) is used to achieve the best feature fitness. A novel fitness function is designed to be the sum of macro F1 loss and normalized dimension, with the optimization objective calculated as the minimum of the fitness function. Multi-layer perceptron (MLP), k-nearest neighbor, support vector machine, random forest and extra decision tree classifiers are trained using the selected features. For the targeted 15-class classification problem, MOPSO-optimized features with MLP consistently performed best with significantly reduced number of features. The proposed method proves to be an efficient and effective arrhythmia identification system for continuous, long-term cardiac health monitoring using single-lead ECG signal.

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Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network

Cited by 21 publications

References 28 publications

Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network

Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

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