Arrhythmias Classification Using Short-Time Fourier Transform and GAN Based Data Augmentation

Lan, Tianjie; Hu, Qihan; Liu, Xin; He, Kaiyue; Yang, Cuiwei

doi:10.1109/embc44109.2020.9176733

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…1. Utilize additional sample augmentation methods, such as enhanced SMOTE algorithms [17] and Generative Adversarial Networks (GANs) [18,19], to tackle the issue of imbalanced data; 2. Collect more data from diverse races and regions for external validation and generalization testing of the model; 3.…”

Section: Discussionmentioning

confidence: 99%

“…Data were collected from the NHANES datasets spanning 2009 to 2016, encompassing 19,998 individuals aged between 18 and 59 years. A total of 7,945 individuals were excluded due to their responses to the Sexual Behavior Questionnaire, speci cally those who provided answers other than "yes" or "no" regarding whether a doctor had ever informed them of having HPV, genital herpes, genital warts, gonorrhea, or chlamydia, or those who refused to answer the questions.…”

Section: Data Sourcementioning

confidence: 99%

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Building Gender-Specific Sexually Transmitted Infection Risk Prediction Models Using CatBoost Algorithm and NHANES Data

Peng

Zhang

et al. 2023

Preprint

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Aims Sexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010 to 2019. Machine learning (ML) presents significant advantages in disease prediction, with several studies exploring its potential for STI prediction. The objective of this study is to build males-based and females-based STI risk prediction models based on the CatBoost algorithm using data from the National Health and Nutrition Examination Survey (NHANES) for training and validation, with sub-group analysis performed on each STI. The female sub-group also includes human papilloma virus (HPV) infection. Methods The study utilized data from the National Health and Nutrition Examination Survey (NHANES) program to build males-based and females-based STI risk prediction models using the CatBoost algorithm. Data was collected from 12,053 participants aged 18 to 59 years old, with general demographic characteristics and sexual behavior questionnaire responses included as features. The SMOTE algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the CatBoost algorithm. The SHAP method was employed to enhance interpretability by identifying feature importance in the model's STIs risk prediction. Results The CatBoost classifier achieved AUC values of 0.7891, 0.6558, 0.6607, 0.6118 and 0.6932 for predicting chlamydia, genital herpes, genital warts, gonorrhea, and overall STIs infections among males.The CatBoost classifier achieved AUC values of 0.7082, 0.647, 0.6767, 0.8459, 0.6929 and 0.7005 for predicting chlamydia, genital herpes, genital warts, gonorrhea, HPV and overall STIs infections among females.

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Section: Discussionmentioning

confidence: 99%

Section: Data Sourcementioning

confidence: 99%

Building Gender-Specific Sexually Transmitted Infection Risk Prediction Models Using CatBoost Algorithm and NHANES Data

Peng

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Based on the application of DL models trained to approximate real data distributions [28,29], DGMs can generate realistic multivariate time series that mimic the characteristics of real data [30]. Increasingly used to analyze ECG data [31][32][33][34][35][36], they comprise different DL architectures such as long short-term memory (LSTM), convolutional neural networks (CNNs), or recurrent neural networks (RNNs) [37].…”

Section: Generative Methods and Deep Generative Modelsmentioning

confidence: 99%

A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging

Mehri,

Calmon,

Odille

et al. 2023

Sensors

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Recently, deep learning (DL) models have been increasingly adopted for automatic analyses of medical data, including electrocardiograms (ECGs). Large, available ECG datasets, generally of high quality, often lack specific distortions, which could be helpful for enhancing DL-based algorithms. Synthetic ECG datasets could overcome this limitation. A generative adversarial network (GAN) was used to synthesize realistic 3D magnetohydrodynamic (MHD) distortion templates, as observed during magnetic resonance imaging (MRI), and then added to available ECG recordings to produce an augmented dataset. Similarity metrics, as well as the accuracy of a DL-based R-peak detector trained with and without data augmentation, were used to evaluate the effectiveness of the synthesized data. Three-dimensional MHD distortions produced by the proposed GAN were similar to the measured ones used as input. The precision of a DL-based R-peak detector, tested on actual unseen data, was significantly enhanced by data augmentation; its recall was higher when trained with augmented data. Using synthesized MHD-distorted ECGs significantly improves the accuracy of a DL-based R-peak detector, with a good generalization capacity. This provides a simple and effective alternative to collecting new patient data. DL-based algorithms for ECG analyses can suffer from bias or gaps in training datasets. Using a GAN to synthesize new data, as well as metrics to evaluate its performance, can overcome the scarcity issue of data availability.

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“…Consequently, the wavelet transform is limited in real-world industrial applications owing to issues related to real-time processing and memory constraints. Certainly, research utilizing similar frequency analysis methods has been conducted using multivariate time series data as well [29,30]. However, in most studies, each signal is often treated as an independent entity, applying separate transformations to each signal and subsequently concatenating them on a channel-by-channel basis [31,32].…”

Section: Frequency Feature Extraction Using Waveletmentioning

confidence: 99%

BiVi-GAN: Bivariate Vibration GAN

Jeong,

Jeung,

Lee

et al. 2024

Sensors

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In the domain of prognosis and health management (PHM) for rotating machinery, the criticality of ensuring equipment reliability cannot be overstated. With developments in artificial intelligence (AI) and deep learning, there have been numerous attempts to use those methodologies in PHM. However, there are challenges to applying them in practice because they require huge amounts of data. This study explores a novel approach to augment vibration data—a primary component in traditional PHM methodologies—using a specialized generative model. Recognizing the limitations of deep learning models, which often fail to capture the intrinsic physical characteristics vital for vibration analysis, we introduce the bivariate vibration generative adversarial networks (BiVi-GAN) model. BiVi-GAN incorporates elements of a physics-informed neural network (PINN), emphasizing the specific vibration characteristics of rotating machinery. We integrate two types of physical information into our model: order analysis and cross-wavelet transform, which are crucial for dissecting the vibration characteristics of such machinery. Experimental findings show the effectiveness of our proposed model. With the incorporation of physics information (PI) input and PI loss, the BiVi-GAN showed a 70% performance improvement in terms of JS divergence compared with the baseline biwavelet-GAN model. This study maintains the potential and efficacy of complementary domain-specific insights with data-driven AI models for more robust and accurate outcomes in PHM.

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Arrhythmias Classification Using Short-Time Fourier Transform and GAN Based Data Augmentation

Cited by 8 publications

References 5 publications

Building Gender-Specific Sexually Transmitted Infection Risk Prediction Models Using CatBoost Algorithm and NHANES Data

Building Gender-Specific Sexually Transmitted Infection Risk Prediction Models Using CatBoost Algorithm and NHANES Data

A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging

BiVi-GAN: Bivariate Vibration GAN

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