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Que W, Bian Y, Chen S, Zhao X, Ji Z, Hu P, Han C, Shi L. Efficient electrocardiogram generation based on cardiac electric vector simulation model. Comput Biol Med 2024; 177:108629. [PMID: 38820778 DOI: 10.1016/j.compbiomed.2024.108629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/27/2024] [Accepted: 05/18/2024] [Indexed: 06/02/2024]
Abstract
This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.
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Affiliation(s)
- Wenge Que
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yingnan Bian
- School of Logistics, Henan College of Transportation, Zhengzhou, 450000, China.
| | - Shengjie Chen
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Xiliang Zhao
- Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
| | - Zehua Ji
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Pingge Hu
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Chuang Han
- School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, 100084, China; Beijing National Research Center for Information Science and Technology, Beijing, 100084, China.
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2
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Zachariou M, Arandjelović O, Dombay E, Sabiiti W, Mtafya B, Ntinginya NE, Sloan DJ. Localization and phenotyping of tuberculosis bacteria using a combination of deep learning and SVMs. Comput Biol Med 2023; 167:107573. [PMID: 37913616 DOI: 10.1016/j.compbiomed.2023.107573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/09/2023] [Accepted: 10/11/2023] [Indexed: 11/03/2023]
Abstract
Successful treatment of pulmonary tuberculosis (TB) depends on early diagnosis and careful monitoring of treatment response. Identification of acid-fast bacilli by fluorescence microscopy of sputum smears is a common tool for both tasks. Microscopy-based analysis of the intracellular lipid content and dimensions of individual Mycobacterium tuberculosis (Mtb) cells also describe phenotypic changes which may improve our biological understanding of antibiotic therapy for TB. However, fluorescence microscopy is a challenging, time-consuming and subjective procedure. In this work, we automate examination of fields of view (FOVs) from microscopy images to determine the lipid content and dimensions (length and width) of Mtb cells. We introduce an adapted variation of the UNet model to efficiently localising bacteria within FOVs stained by two fluorescence dyes; auramine O to identify Mtb and LipidTox Red to identify intracellular lipids. Thereafter, we propose a feature extractor in conjunction with feature descriptors to extract a representation into a support vector multi-regressor and estimate the length and width of each bacterium. Using a real-world data corpus from Tanzania, the proposed method i) outperformed previous methods for bacterial detection with a 8% improvement (Dice coefficient) and ii) estimated the cell length and width with a root mean square error of less than 0.01%. Our network can be used to examine phenotypic characteristics of Mtb cells visualised by fluorescence microscopy, improving consistency and time efficiency of this procedure compared to manual methods.
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Affiliation(s)
- Marios Zachariou
- School of Computer Science, University of St Andrews, St Andrews, KY16 9SX, United Kingdom.
| | - Ognjen Arandjelović
- School of Computer Science, University of St Andrews, St Andrews, KY16 9SX, United Kingdom
| | - Evelin Dombay
- School of Medicine, University of St Andrews, St Andrews, KY16 9AJ, United Kingdom
| | - Wilber Sabiiti
- School of Medicine, University of St Andrews, St Andrews, KY16 9AJ, United Kingdom
| | | | | | - Derek J Sloan
- School of Medicine, University of St Andrews, St Andrews, KY16 9AJ, United Kingdom
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Zhang Z, Xiao Q, Luo J. Infant death prediction using machine learning: A population-based retrospective study. Comput Biol Med 2023; 165:107423. [PMID: 37672926 DOI: 10.1016/j.compbiomed.2023.107423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/27/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND Despite declines in infant death rates in recent decades in the United States, the national goal of reducing infant death has not been reached. This study aims to predict infant death using machine-learning approaches. METHODS A population-based retrospective study of live births in the United States between 2016 and 2021 was conducted. Thirty-three factors related to birth facility, prenatal care and pregnancy history, labor and delivery, and newborn characteristics were used to predict infant death. RESULTS XGBoost demonstrated superior performance compared to the other four compared machine learning models. The original imbalanced dataset yielded better results than the balanced datasets created through oversampling procedures. The cross-validation of the XGBoost-based model consistently achieved high performance during both the pre-pandemic (2016-2019) and pandemic (2020-2021) periods. Specifically, the XGBoost-based model performed exceptionally well in predicting neonatal death (AUC: 0.98). The key predictors of infant death were identified as gestational age, birth weight, 5-min APGAR score, and prenatal visits. A simplified model based on these four predictors resulted in slightly inferior yet comparable performance to the all-predictor model (AUC: 0.91 vs. 0.93). Furthermore, the four-factor risk classification system effectively identified infant deaths in 2020 and 2021 for high-risk (88.7%-89.0%), medium-risk (4.6%-5.4%), and low-risk groups (0.1), outperforming the risk screening tool based on accumulated risk factors. CONCLUSIONS XGBoost-based models excel in predicting infant death, providing valuable prognostic information for perinatal care education and counselling. The simplified four-predictor classification system could serve as a practical alternative for infant death risk prediction.
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Affiliation(s)
- Zhihong Zhang
- School of Nursing, University of Rochester, Rochester, NY, USA; Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA.
| | - Qinqin Xiao
- Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA; The Warner School of Education and Human Development, University of Rochester, Rochester, NY, USA
| | - Jiebo Luo
- Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA; Department of Computer Science, University of Rochester, Rochester, NY, USA
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Wijayanto I, Humairani A, Hadiyoso S, Rizal A, Prasanna DL, Tripathi SL. Epileptic seizure detection on a compressed EEG signal using energy measurement. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Kuang S, Woodruff HC, Granzier R, van Nijnatten TJA, Lobbes MBI, Smidt ML, Lambin P, Mehrkanoon S. MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets. Neural Netw 2023; 165:119-134. [PMID: 37285729 DOI: 10.1016/j.neunet.2023.05.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts to better exploit the underlying semantic information of the image at different levels. To resolve the data imbalance problem, we utilize a category-wise cross-domain sampling strategy to sample anchors from target images and build a hybrid memory bank to store samples from source images. We have validated MSCDA with a challenging task of cross-domain breast MRI segmentation between datasets of healthy volunteers and invasive breast cancer patients. Extensive experiments show that MSCDA effectively improves the model's feature alignment capabilities between domains, outperforming state-of-the-art methods. Furthermore, the framework is shown to be label-efficient, achieving good performance with a smaller source dataset. The code is publicly available at https://github.com/ShengKuangCN/MSCDA.
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Affiliation(s)
- Sheng Kuang
- The D-Lab, Department of Precision Medicine, GROW - School or Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School or Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Renee Granzier
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Thiemo J A van Nijnatten
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Marc B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands; Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Marjolein L Smidt
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands; GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School or Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Siamak Mehrkanoon
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.
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Asadi M, Poursalim F, Loni M, Daneshtalab M, Sjödin M, Gharehbaghi A. Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search. Sci Rep 2023; 13:11378. [PMID: 37452165 PMCID: PMC10349064 DOI: 10.1038/s41598-023-38541-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
This paper presents a novel machine learning framework for detecting PxAF, a pathological characteristic of electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a generative adversarial network (GAN) along with a neural architecture search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99.0% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by [Formula: see text] and [Formula: see text].
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Affiliation(s)
- Mehdi Asadi
- Department of Electrical Engineering, Tarbiat Modares University, Tehran, Iran
| | | | - Mohammad Loni
- School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden
| | - Masoud Daneshtalab
- School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden
| | - Mikael Sjödin
- School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden
| | - Arash Gharehbaghi
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
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Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning. Cogn Neurodyn 2023. [DOI: 10.1007/s11571-023-09932-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
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8
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End-to-end fatigue driving EEG signal detection model based on improved temporal-graph convolution network. Comput Biol Med 2023; 152:106431. [PMID: 36543007 DOI: 10.1016/j.compbiomed.2022.106431] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/30/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Fatigue driving is one of the leading causes of traffic accidents, so fatigue driving detection technology plays a crucial role in road safety. The physiological information-based fatigue detection methods have the advantage of objectivity and accuracy. Among many physiological signals, EEG signals are considered to be the most direct and promising ones. Most traditional methods are challenging to train and do not meet real-time requirements. To this end, we propose an end-to-end temporal and graph convolution-based (MATCN-GT) fatigue driving detection algorithm. The MATCN-GT model consists of a multi-scale attentional temporal convolutional neural network block (MATCN block) and a graph convolutional-Transformer block (GT block). Among them, the MATCN block extracts features directly from the original EEG signal without a priori information, and the GT block processes the features of EEG signals between different electrodes. In addition, we design a multi-scale attention module to ensure that valuable information on electrode correlations will not be lost. We add a Transformer module to the graph convolutional network, which can better capture the dependencies between long-distance electrodes. We conduct experiments on the public dataset SEED-VIG, and the accuracy of the MATCN-GT model reached 93.67%, outperforming existing algorithms. Furthermore, compared with the traditional graph convolutional neural network, the GT block has improved the accuracy rate by 3.25%. The accuracy of the MATCN block on different subjects is higher than the existing feature extraction methods.
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Hybrid optimized convolutional neural network for efficient classification of ECG signals in healthcare monitoring. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103731] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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10
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Maiti S, Maji D, Kumar Dhara A, Sarkar G. Automatic detection and segmentation of optic disc using a modified convolution network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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