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- ArticleJuly 2024
A Multi-scale Attention Network for Sleep Arousal Detection with Single-Channel ECG
AbstractArousal detection is of great importance to measure sleep quality. Traditional diagnosis of arousal that relies on polysomnography (PSG) signals is cumbersome and costly, requiring an overnight stay in a sleep laboratory with multiple sensors ...
- ArticleJuly 2024
KUMA-MI: A 12-Lead Knowledge-Guided Multi-branch Attention Networks for Myocardial Infarction Localization
AbstractMyocardial infarction (MI) is a critical cardiovascular disease that requires a timely and accurate diagnosis to prevent severe outcomes. Clinically, MI is located according to the diagnostic criteria of the 12-lead electrocardiogram (ECG). ...
- ArticleJuly 2024
Explanations of Augmentation Methods for Deep Learning ECG Classification
AbstractRecent progress in deep learning has sparked widespread interest in their development and adoption of diverse applications. The effectiveness of deep neural networks, particularly in extracting meaningful patterns from multimedia data, has ...
- research-articleJuly 2024
Cleaning ECG with Deep Learning: A Denoiser Tested in Industrial Settings
AbstractAs the popularity of wearables continues to scale, a substantial portion of the population has now access to (self-)monitorization of cardiovascular activity. In particular, the use of ECG wearables is growing in the realm of occupational health ...
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- ArticleJuly 2024
Visual Explanations and Perturbation-Based Fidelity Metrics for Feature-Based Models
AbstractThis work introduces an enhanced methodology in the domain of eXplainable Artificial Intelligence (XAI) for visualizing local explanations of black-box, feature-based models, such as LIME and SHAP, enabling both domain experts and non-specialists ...
- research-articleJuly 2024
A Novel Method for Design and Implementation of Systolic Associative Cascaded Variable Leaky Least Mean Square Adaptive Filter for Denoising of ECG Signals
Wireless Personal Communications: An International Journal (WPCO), Volume 137, Issue 2Pages 1029–1043https://doi.org/10.1007/s11277-024-11450-3AbstractElectrocardiogram is the most essential diagnostic test for heart disease detection in this era, where it has low frequency and small amplitude, making it vulnerable to a variety of stimuli, including high/low-frequency noises resulting in ...
- ArticleJune 2024
Quantification and Analysis of Stress Levels While Walking Up and Down a Step in Real Space and VR Space Using Electrocardiogram
AbstractThe purpose of this study is to quantify the fear during walking in VR space. We conduct an experiment where subjects walk up and down steps in real and VR spaces. A path with steps of 10cm high was placed in the real and VR spaces. Subjects wore ...
- research-articleJune 2024
An ECG Based CNN Model for Detection of Different Classes of Arrhythmia
AbstractElectrocardiograms (ECGs) use electrodes to monitor heart rates and discover tiny electrical changes in each beating. This test can detect abnormal cardiac activity such as arrhythmias and conduction abnormalities. The goal of this research is to ...
- research-articleJune 2024
PACAC: PYNQ Accelerated Cardiac Arrhythmia Classifier with secure transmission- A Deep Learning based Approach
GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024Pages 670–675https://doi.org/10.1145/3649476.3660372Electrocardiogram (ECG) signals are vital features to identify a healthy body; diagnosing cardiovascular diseases (CVDs) automatically using computer-aided tools has caught a significant attention in the current medical scenario. In recent times with ...
- research-articleJuly 2024
Non-invasive coronary artery disease identification through the iris and bio-demographic health profile features using stacking learning
AbstractThis study proposes a non-invasive method for predicting Coronary Artery Disease (CAD) using iris analysis, patient data, and Machine Learning (ML), primarily with iris images. It involved 281 participants, comprising 155 CAD patients and 126 non-...
Highlights- Novel method integrates iris images & biodemographic data for CAD prediction.
- Comparative analysis of three data scenarios reveals insights.
- Comprehensive evaluation of ML techniques per scenario enhances CAD detection.
- ...
- research-articleJuly 2024
ECG waveform generation from radar signals: A deep learning perspective
- Farhana Ahmed Chowdhury,
- Md Kamal Hosain,
- Md Sakib Bin Islam,
- Md Shafayet Hossain,
- Promit Basak,
- Sakib Mahmud,
- M. Murugappan,
- Muhammad E.H. Chowdhury
Computers in Biology and Medicine (CBIM), Volume 176, Issue Chttps://doi.org/10.1016/j.compbiomed.2024.108555AbstractCardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended ...
Highlights- A new method for generating continuous ECGs from non-contact radar signals is presented.
- The ECG Signals are generated from radar signals using a MultiResLinkNet.
- ECG waveforms were generated for three conditions (Resting, Valsalva,...
- research-articleSeptember 2024
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data
- Vikhyat Agrawal,
- Sunil Vasu Kalmady,
- Venkataseetharam Malipeddi Manoj,
- Manisimha Varma Manthena,
- Weijie Sun,
- MD Saiful Islam,
- Abram Hindle,
- Padma Kaul,
- Russell Greiner
ICMHI '24: Proceedings of the 2024 8th International Conference on Medical and Health InformaticsPages 143–152https://doi.org/10.1145/3673971.3673990This research paper explores ways to apply Federated Learning (FL) and Differential Privacy (DP) techniques to population-scale Electrocardiogram (ECG) data. The study learns a multi-label ECG classification model using FL and DP based on 1,565,849 ECG ...
- research-articleJuly 2024
Electrocardiogram identification based on data generative network and non-fiducial data processing
Computers in Biology and Medicine (CBIM), Volume 173, Issue Chttps://doi.org/10.1016/j.compbiomed.2024.108333AbstractNowadays, the use of biological signals as a criterion for identity recognition has gained increasing attention from various organizations and companies. Therefore, it has become crucial to have a biometric identity recognition method that is ...
Highlights- We propose a generative network-based strategy for electrocardiogram (ECG) data generation to address the issue of insufficient feature extraction by convolutional networks.
- We propose a linear complexity filtering algorithm for ...
- ArticleJuly 2024
Multifaceted ECG Feature Extraction for AFIB Detection: Using Traditional Machine Learning Techniques
Intelligent Information and Database SystemsPages 108–119https://doi.org/10.1007/978-981-97-4982-9_9AbstractThis paper presents a novel approach for the early diagnosis of Atrial Fibrillation (AFIB) using electrocardiogram (ECG) signals. Specifically, the 12-lead ECG, a standard in cardiac monitoring, are exploit to extract meaningful patterns ...
- research-articleJuly 2024
Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals
- Silvia Seoni,
- Filippo Molinari,
- U. Rajendra Acharya,
- Oh Shu Lih,
- Prabal Datta Barua,
- Salvador García,
- Massimo Salvi
Information Sciences: an International Journal (ISCI), Volume 665, Issue Chttps://doi.org/10.1016/j.ins.2024.120383AbstractThis study aims to address the need for reliable diagnosis of coronary artery disease (CAD) using artificial intelligence (AI) models. Despite the progress made in mitigating opacity with explainable AI (XAI) and uncertainty quantification (UQ), ...
- review-articleJuly 2024
ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review
- Pedro A. Moreno-Sánchez,
- Guadalupe García-Isla,
- Valentina D.A. Corino,
- Antti Vehkaoja,
- Kirsten Brukamp,
- Mark van Gils,
- Luca Mainardi
Computers in Biology and Medicine (CBIM), Volume 172, Issue Chttps://doi.org/10.1016/j.compbiomed.2024.108235AbstractCardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different ...
Highlights- Data-driven solutions with ECG as the primary input for CVD diagnosis and prognosis.
- PRISMA methodology followed for paper identification, screening, and review.
- Multidimensional analysis tackling CVD types, data characteristics, ...
- research-articleJuly 2024
One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms
Computer Methods and Programs in Biomedicine (CBIO), Volume 247, Issue Chttps://doi.org/10.1016/j.cmpb.2024.108097Highlights- We propose a framework to improve LVH detection using 12-lead ECG waveforms.
- An innovative oneshot-screening method captured waveforms as 2D images.
- Our 2D-CNN plus metadata model achieved an AUROC of 0.92 per the ASE standard.
Left ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH ...
- research-articleJune 2024
ECGMiner: A flexible software for accurately digitizing ECG
Computer Methods and Programs in Biomedicine (CBIO), Volume 246, Issue Chttps://doi.org/10.1016/j.cmpb.2024.108053Abstract Background and objectiveThe electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but ...
Highlights- ECGMiner is an open-source software that accurately digitizes ECG images.
- ECGMiner graphical interface is simple and easy to use.
- ECGMiner accepts multiple ECG formats and outputs the whole ECG signal data.
- ECGMiner has been ...