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ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts;A Foundation for computerized ECG Image Digitization and Analysis

arXiv

引用

arXiv 2024年

作者： Reyna, Matthew A. Deepanshi Weigle, James Koscova, Zuzana Campbell, Kiersten Shivashankara, Kshama Kodthalu Saghafi, Soheil Nikookar, Sepideh Motie-Shirazi, Mohsen Kiarashi, Yashar Seyedi, Salman Clifford, Gari D. Sameni, Reza Department of Biomedical Informatics Emory University AtlantaGA30322 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30332 United States

Objective: We introduce ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data that exhibit real-world scanning, imaging, and physical artifacts. This dataset addresses the need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of ECG image digitization. Approach: We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series data. We include images with realistic distortions, including noise, wrinkles, stains, and perspective shifts generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These 1,977 unique images were subjected to both programmatic distortions using ECG-Image-Kit and to physical effects, such as soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts, producing a total of 35,595 images. Main results: The resulting dataset includes 35,595 software-labeled ECG images (generated from 1,977 unique records) from different sources (in Germany and the USA) with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the corresponding images, offering a reference for training and evaluating machine and deep learning models for ECG digitization and classification. The images vary in quality, ranging from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable and robust digitization algorithms. Significance: ECG-Image-Database addresses a critical gap in cardiovascular research by supporting the development of machine learning models capable of classifying or converting ECG images int

关键词： Photography

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Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning 11

Prediction of COVID-19 Patients' Emergency Room Revisit usin...

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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Ji, Yuelyu Gao, Yuhe Bao, Runxue Li, Qi Liu, Disheng Sun, Yiming Ye, Ye University of Pittsburgh School of Computing and Information Department of Information Science Pittsburgh United States University of Pittsburgh School of Medicine Department of Biomedical Informatics Pittsburgh United States University of Pittsburgh Swanson School of Engineering Department of Electrical and Computer Engineering Pittsburgh United States State University of New York at New Paltz School of Business New Paltz United States

ISBN: (纸本)9798350302639

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn't consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informat

关键词： COVID-19

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Real-Time Autoregressive Forecast of Cardiac Features for Psychophysiological Applications

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IEEE Journal of biomedical and Health informatics 2025年 PP卷 PP页

作者： Yaldiz, Cem O. Lin, David J. Gazi, Asim H. Cestero, Gabriela Chen, Chuoqi Bracken, Bethany K. Winder, Aaron Lynn, Spencer Sameni, Reza Inan, Omer T. Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Georgia Institute of Technology Department of Biomedical Engineering AtlantaGA30332 United States Charles River Analytics Inc. BostonMA02138 United States Emory University Department of Biomedical Informatics AtlantaGA30322 United States

Forecasting the near-exact moments of cardiac phases is crucial for several cardiovascular health applications. For instance, forecasts can enable the timing of specific stimuli (e.g., image or text presentation in psycholinguistic experiments) to coincide with cardiac phases like systole (cardiac ejection) and diastole (cardiac filling). This capability could be leveraged to enhance the amplitude of a subject's response, prompt them in fight-or-flight scenarios or conduct retrospective analysis for physiological predictive models. While autoregressive models have been employed for physiological signal forecasting, no prior study has explored their application to forecasting aortic opening and closing timings. This work addresses this gap by presenting a comprehensive comparative analysis of autoregressive models, including various forms of Kalman filter-based implementations, that use previously detected R-peak, aortic opening, and closing timings from electrocardiogram (ECG) and seismocardiogram (SCG) to forecast subsequent timings. We evaluate the robustness of these models to noise introduced in both SCG signals and the output of feature detectors. Our findings indicate that time-varying and multi-feature algorithms outperform others, with forecast errors below 2 ms for R-peak, below 3 ms for aortic opening timing, and below 10 ms for aortic closing timing. Importantly, we elucidate the distinct advantages of integrating multi-feature models, which improve noise robustness, and time-varying approaches, which adapt to rapid physiological changes. These models can be extended to a wide range of short-term physiological predictive systems, such as acute stress detection, neuromodulation sensor feedback, or muscle fatigue monitoring, broadening their applicability beyond cardiac feature forecasting. © 2013 IEEE.

关键词： Blood vessels

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Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma

Multimodal Deep Learning to Differentiate Tumor Recurrence f...

引用

IEEE International Symposium on biomedical Imaging

作者： Tonmoy Hossain Zoraiz Qureshi Nivetha Jayakumar Thomas Eluvathingal Muttikkal Sohil Patel David Schiff Miaomiao Zhang Bijoy Kundu Departments of Computer Science University of Virginia USA Radiology and Medical Imaging University of Virginia USA Neurology University of Virginia USA Electrical and Computer Engineering University of Virginia USA Biomedical Engineering University of Virginia USA

Differentiating tumor progression (TP) or recurrence from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. In a preliminary study, a convolution neural network (CNN) was trained to predict classification accuracy between TP and TN for 35 brain tumors from 26 subjects in the PET-MR image space. 3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN. The average test accuracy across all leave-one-out cross-validation iterations adjusting for class weights was 0.56 using only the MR, 0.65 using only the SUV, and 0.71 using only the Ki voxels. Combining SUV and MR voxels increased the test accuracy to 0.62. On the other hand, MR and Ki voxels increased the test accuracy to 0.74. Thus, dPET features alone or with MR features in deep learning models would enhance prediction accuracy in differentiating TP vs TN in GBM.

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Modified CNN VoxNet Based Depthwise Separable Convolution for Voxel-Driven Body Orientation Classification

Modified CNN VoxNet Based Depthwise Separable Convolution fo...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Moch. Iskandar Riansyah Oddy Virgantara Putra Ardyono Priyadi Tri Arief Sardjono Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Telkom University Department of Informatics Universitas Darussalam Gontor Ponorogo Indonesia Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Biomedical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

In this research, LiDAR sensor technology introduces a new representation of point cloud data for tasks in 3D object recognition. Point clouds provide rich information that can be utilized to predict the orientation pose of the human body. The orientation of the human body is crucial for various needs such as autonomous vehicles, robotics, and human activity monitoring. However, the inherently unstructured nature and permutation invariance of point cloud data pose significant challenges in accurately predicting human body orientation using 3D data. In this paper, we propose a solution by introducing a volumetric method based on CNN to effectively recognize human body ori-entation. A novel approach is proposed by embedding depthwise sparse convolution into 2D CNN to process voxel data of human body orientation. We evaluate the performance using KITTI data divided into four orientation classes: east, west, north, and south. Additionally, we compare it with other architectures using the ModelNet10 and ModelNet40 datasets. The comparison with other architectures shows superior performance with accuracies of 84.31 % for the KITTI dataset, 86.67 % for ModelNet10, and 75.20 % for ModelNet40. This evidence strongly supports the effectiveness of our proposed method, highlighting its potential in enhancing 3D object recognition systems significantly.

关键词： Point cloud compression Measurement Three-dimensional displays Laser radar Convolution Imaging Robot sensing systems Object recognition Monitoring Autonomous vehicles

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ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization

arXiv

引用

arXiv 2023年

作者： Shivashankara, Kshama Kodthalu Deepanshi Shervedani, Afagh Mehri Clifford, Gari D. Reyna, Matthew A. Sameni, Reza The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States The Electrical and Computer Engineering Department University of Illinois Chicago ChicagoIL United States The Department of Biomedical Informatics Emory University AtlantaGA United States The Department of Biomedical Engineering Georgia Institute of Technology GA United States

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and the electrocardiogram (ECG) plays a crucial role in diagnosing CVDs. For decades, ECGs have been recorded in printed formats, and their digitization offers significant potential for training machine learning (ML) models in algorithmic ECG diagnosis. However, the valuable information in physical ECG archives risks deterioration, leading to a considerable loss in CVD research, particularly in low and low-middle-income countries where paper-based ECGs are common. Furthermore, merely scanning printed ECGs is inadequate, as most ML models require ECG time-series data. Therefore, digitizing and converting paper ECG archives into time-series data is essential. In this context, deep learning models for image processing are promising. Nonetheless, the scarcity of clinical ECG archives with reference time-series data presents a challenge. To overcome this, data augmentation techniques using digital twins offer a potential solution. We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic ECG images with realistic artifacts from time-series data, and showcase its application in developing algorithms for ECG image digitization. Synthetic data is generated by producing distortionless ECG images on a standard ECG paper background. Subsequently, various distortions, including handwritten text artifacts, wrinkles, creases, and perspective transformations, are applied to these ECG images. The artifacts and text are synthetically generated, excluding personally identifiable information. The toolbox is used for data augmentation in the 2024 PhysioNet Challenge on Digitization and Classification of ECG Images. As a case study, we employed ECG-Image-Kit to create an ECG image dataset of 21,801 records from the PhysioNet QT database [1, 2]. A denoising convolutional neural network (DnCNN)-based model was developed and trained on this synthetic dataset and used to convert the synthetically ge

关键词： Electrocardiograms

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Leveraging Highly Approximated Multipliers in DNN Inference

arXiv

引用

arXiv 2024年

作者： Zervakis, Georgios Frustaci, Fabio Spantidi, Ourania Anagnostopoulos, Iraklis Amrouch, Hussam Henkel, Jörg The Computer Engineering & Informatics Dept. University of Patras Greece The DIMES University of Calabria Italy The Department of Computer Science Eastern Michigan University United States The School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale United States Germany Karlsruhe Institute of Technology Germany

In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our approach does not require retraining and significantly decreases the induced error due to approximate multiplications, improving the overall inference accuracy. As a result, our approach enables satisfying tight accuracy loss constraints while boosting the power savings. Our experimental evaluation, across six different DNNs and several approximate multipliers, demonstrates the versatility of our approach and shows that compared to the accurate design, our control variate approximation achieves the same performance, 45% power reduction, and less than 1% average accuracy loss. Compared to the corresponding approximate designs without using our technique, our approach improves the accuracy by 1.9x on average. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Adaptive Technique of Impulsive Noise Removal in Color Digital Images

Adaptive Technique of Impulsive Noise Removal in Color Digit...

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International Conf on electrical and Electronic engineering (ICEEE)

作者： Bogdan Smolka Monika Mendrela Damian Kusnik Milena Smolka Faculty of Automatics Electronics and Informatics Silesian University of Technology Gliwice Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland

This paper describes a new technique for removing impulsive noise in digital color images. The proposed method uses a measure of the pixel distortion caused by the impulsive noise, expressed as the sum of the Euclidean distances between the central pixel and its closest neighbors in the filtering window. This corruption measure is used to determine a weighted average of the pixels from the local neighborhood that is not affected by the distorted pixels. The filtering output is obtained using a soft switching scheme, where the denoising result is determined as a weighted average of the central pixel of a processing window and its robust estimate. The weighting coefficient is calculated based on the amount of distortion of the central pixel. Thus, if the central pixel is highly distorted, it is replaced by its robust estimate, otherwise it is not significantly changed. The parameters that determine the image enhancement result are adaptively adjusted by using a noise-resistant image contrast measure. The proposed filter design is able to effectively suppress impulsive noise while preserving fine image details and edges. Comparison with existing algorithms shows that the proposed method provides better denoising results than the most efficient filters designed for impulsive noise suppression in color images. Moreover, the new method is very fast and can be used in real-time denoising scenarios.

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Trimmed Non-Local Means Filtering for the Suppression of Mixed Noise in Color Images

Trimmed Non-Local Means Filtering for the Suppression of Mix...

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electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Damian Kusnik Bogdan Smolka Milena Smolka Faculty of Automatics Electronics and Informatics Silesian University of Technology Gliwice Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland

In this paper, a novel approach to the problem of mixed Gaussian and impulsive noise reduction in color images is proposed. The elaborated denoising framework is based on the Non-Local Means technique, which has been shown to effectively suppress Gaussian noise. To overcome the inability of this filter to deal with impulsive distortions, a robust similarity measure between image patches, that is insensitive to the impact of impulsive distortions, was developed. First, the distorted pixels of the image are detected using the concept of a peer group. If the number of neighbors of a pixel at the center of a filtering window, whose Euclidean distance in the RGB color space does not exceed a threshold value, is smaller than a predefined value, then the considered pixel is treated as corrupted. Afterwards, the similarity between image patches are calculated in a standard way omitting the noisy pixels. In this way, only the average of distances between pairs of unaffected pixels are calculated. Thus, the closeness measure of the patches, which serves as the coefficient in weighted averaging of pixels in the processing block, is not affected by impulsive noise and is still able to cope with the Gaussian noise component. The proposed filtering design is capable of reducing even strong mixed noise while preserving image details and edges. Moreover, it is not computationally intensive, so it can be used in real-time image processing scenarios.

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Precision Targeting of Non-Small Cell Lung Cancer: Identifying Optimal Drug Targets and FDA-Approved Combinations for Enhanced Therapeutic Efficacy 23

Precision Targeting of Non-Small Cell Lung Cancer: Identifyi...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Bhattacharjee, Pranabesh Lahiri, Aditya Reeves, Norman Peter Datta, Aniruddha Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States Department of Biomedical and Health Informatics The Children's Hospital of Philadelphia 3501 Civic Center Blvd PhiladelphiaPA19104 United States Sumaq Life LLC East LansingMI48823 United States

ISBN: (纸本)9798350393118

This study proposes a Boolean network model to identify optimal drug targets and select the most effective FDA-approved drug combinations for Non-Small Cell Lung Cancer (NSCLC). The Boolean network models the signaling pathways in NSCLC to capture the intricate molecular interactions driving tumor progression. We evaluate the model by employing the size difference (SD) score, which reflects the degree of cell dysregulation due to gene mutations and allows us to identify optimal drug targets in NSCLC cells to address this dysregulation. Specifically, leveraging the FDA-approved drug database, we identified the robust drug or drug combination for 1, 2, and 3 mutations that maximize tumor cell death and minimize cell proliferation for NSCLC-associated gene mutations. Our findings provide a strong foundation for personalized therapeutic strategies and hold promise for advancing precision oncology to effectively combat NSCLC. © 2023 IEEE.

关键词： Cell death

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