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检索条件"任意字段=Conference on Computing in Cardiology"

共 1032 条记录，以下是41-50 订阅

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A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning

A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ...

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computing in cardiology conference (CinC)

作者： Kirodiwal, Akash Srivastava, Apoorva Dash, Ashutosh Saha, Ayantika Penaganti, Gopi Vamsi Pratiher, Sawon Alam, Sazedul Patra, Amit Ghosh, Nirmalya Banerjee, Nilanjan IIT Kharagpur Dept Elect Engn Kharagpur W Bengal India Univ Maryland Baltimore Cty CSEE Baltimore MD 21228 USA

ISBN: (纸本)9781728173825

Automated cardiac abnormality detection from an ever-expanding number of electrocardiogram (ECG) records has been widely used to assist physicians in the clinical diagnosis of a variety of cardiovascular diseases. Over the last few years, deep learning (DL) architectures have achieved state-of-the-art performances in various biomedical applications. In this work, we propose a bio-toolkit based on the DL framework comprising stacked convolutional and long short term memory neural network blocks for multi-label ECG signal classification. Our team participated under the name "Cardio-Challengers" in the "PhysioNet/computing in cardiology Challenge 2020" and obtained a challenge metric score of 0.337 in the validation data set and 0.258 in the full test data, placing us 16th out of 41 teams in the official ranking.

关键词： cardiology

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Automated Classification of Normal and Abnormal Heart Sounds using Support Vector Machines 43

Automated Classification of Normal and Abnormal Heart Sounds...

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43rd computing in cardiology conference (CinC)

作者： Bouril, Anthony Aleinikava, Darya Guillem, Maria S. Mirsky, Grace M. STATS LLC Chicago IL USA Benedictine Univ Lisle IL USA Univ Politecn Valencia ITACA Valencia Spain

ISBN: (纸本)9781509008957

While a number of researchers have attempted to develop algorithms for automated classification of heart sounds over the last five decades, these studies have been inadequate in terms of clinical utility due to a number of important limitations. The PhysioNet/computing in cardiology 2016 Challenge seeks to facilitate the development of highly robust algorithms to perform automatic classification of heart sounds in a manner which overcomes the limitations of previous studies. The dataset consists of over 3000 phonocardiogram recordings, taken from several locations on the body, from both healthy and pathological adults and children. The classification task requires the algorithm to determine if a recording is normal, abnormal or cannot be scored (due to excessive noise/corruption of the signal). An implementation of a state-of-the-art segmentation algorithm has been provided by the Challenge organizers, leaving the primary focus of the Challenge on the classification task. For this task, we selected a number of features in both the time and frequency domains. For Phase I, our best overall score for the hidden test set was 0.78 (Sensitivity = 0.70, Specificity = 0.87). For Phase II, our best score was 0.7864 (Sensitivity = 0.733, Specificity = 0.8398).

关键词： cardiology

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Exploration of Neural Radiance Fields, A Review 2nd

Exploration of Neural Radiance Fields, A Review

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2nd International conference on Applied Intelligence, ICAI 2024

作者： Xing, Meng Qu, Shaoyang Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology NJ Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Medicine Tongji University Shanghai200123 China Department of Artificial Intelligence Institute Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9789819619030

In recent years, Neural Radiance Fields (NeRF) have garnered widespread attention in the domains of deep learning and 3D scene rendering, marking a significant advancement in computer vision and graphics. Given the increasing prominence of NeRF and the surge in related research, a thorough summary and analysis of recent progress are warranted. In this paper, we provide a comprehensive examination of the technological evolution, key challenges, and recent developments associated with NeRF. We begin with an introduction to the theoretical foundations of NeRF, followed by an overview of its various adaptations addressing different application scenarios, such as virtual reality, augmented reality, filmmaking, and game development, as well as the practical limitations it faces, including high computational demands and slow rendering speeds. Subsequently, we review publicly available datasets pertinent to NeRF methods and summarize the characteristics of different variants based on experimental results. Finally, we highlight potential research directions and future developments, indicating the extensive application potential of optimized NeRF and its variants across diverse fields. Our objective is to provide both academia and industry with a detailed understanding of the future research and application prospects of NeRF in graphics and scene analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks

Automatic Concurrent Arrhythmia Classification Using Deep Re...

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computing in cardiology conference (CinC)

作者： Nankani, Deepankar Saikia, Pallabi Baruah, Rashmi Dutta Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati 781039 Assam India

ISBN: (纸本)9781728173825

This paper addresses the PhysioNet/computing in cardiology Challenge 2020. The challenge presents a problem to classify 26 types of arrhythmias and normal sinus rhythm using 12-lead electrocardiogram data. We were able to successfully perform the classification task using an eight layer deep residual neural network (ResNet). The skip connections present in the ResNet allowed the model to train faster and produce better challenge score. We also investigated sixteen other models that included convolution and recurrent neural network based models along with interpretability based attention mechanism as all of them are well suited for time series classification problems. The results depicted that the 8 layer ResNet model outperformed other models in terms of challenge score consuming significantly less time during the training phase. We preferred batch wise training to avoid having all the data in memory during training thereby alleviating the problem of memory choking. Our team, deepzx987, obtained a challenge score of 0.305 on validation data, -0.035 on the full test set, and ranked 35th in this year's challenge.

关键词： cardiology

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Prediction of Apnoea and Non-apnoea Arousals From the Polysomnogram Using a Neural Network Classifier

Prediction of Apnoea and Non-apnoea Arousals From the Polyso...

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computing in cardiology conference (CinC)

作者： de Chazal, Philip Du, John Sadr, Nadi Univ Sydney Charles Perkins Ctr Sydney NSW 2006 Australia Univ Sydney Sch Biomed Engn Sydney NSW 2006 Australia

ISBN: (纸本)9781728173825

In this study we present a system for automated processing of signals from the polysomnogram (PSG) for the detection of apnoea and non-apnoea arousals. The PSG signals were divided into 15 second epochs and 59 time- and frequency-domain features were derived for each epoch. Features from adjacent 4 epochs were combined and processed with a bank of ten feed-forward neural networks each with a single hidden layer of 20 units. The system outputs a 200 Hz annotation signal containing probability estimates that each sample was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet computing in cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using three class and two class metrics. With the system classifying three classes, the volume under the receiver operator characteristic (ROC) surface was 0.75 with an optimal specificity of 0.72, a sensitivity of 0.76 for the apnoea arousals, and a sensitivity of 0.69 for the non-apnoea arousals. When the two arousal classes were combined into one arousal class, the area under the precision recall curve was 0.74, the area under the ROC curve was 0.91, with an optimal specificity and sensitivity of 0.85.

关键词： cardiology

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Classification of AF and Other Arrhythmias from a Short Segment of ECG Using Dynamic Time Warping 44

Classification of AF and Other Arrhythmias from a Short Segm...

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44th computing in cardiology conference (CinC)

作者： Tziakouri, Maria Pitris, Costas Orphanidou, Christina Univ Cyprus Dept Elect & Comp Engn 1 Panepistimiou Ave2109 AglanziaPOB 20537 CY-1678 Nicosia Cyprus Univ Cyprus Kios Res & Innovat Ctr Excellence Nicosia Cyprus Oxygen Res Ltd Limassol Cyprus

ISBN: (纸本)9781538645550;9781538666302

Atrial Fibrillation(AF) is a major public health risk but its identification is challenging because it may be episodic and non-symptomatic. Automatically identifying episodes of AF from a short segment of ECG would, thus, be beneficial. As a response to the Physionet/computing in cardiology Challenge 2017 we have implemented a three-stage classifier which can classify segments of ECG into Noisy, Normal, AF or Other Rhythm. We employ a state-of-the-art SQI for identifying noisy segments and then learn two different Support Vector Machine (SVM) classifiers using features extracted from the ECG. The features used are derived using a template matching approach via Dynamic Time Warping and using the statistical characteristics of the R-R intervals. Our average F1 score on the validation set was 0.66.

关键词： Electrocardiography Rhythm Feature extraction Support vector machines Classification algorithms cardiology Noise measurement Electrocardiography Support Vector Network Noise measurement dynamic time warping Sorting algorithm electrochemical grinding Rhythms cardiology Feature extraction air feed equipment TEST SETS

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Epicardial Fibrosis explains Increased Transmural Conduction in a Computer Model of Atrial Fibrillation 43

Epicardial Fibrosis explains Increased Transmural Conduction...

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43rd computing in cardiology conference (CinC)

作者： Gharaviri, Ali Potse, Mark Verheule, Sander Krause, Rolf Auricchio, Angelo Schotten, Ulrich Univ Svizzera Italiana Ctr Computat Med Cardiol Lugano Switzerland Inria Bordeaux Bordeaux France Maastricht Univ Dept Physiol Maastricht Netherlands Cardioctr Ticino Lugano Switzerland

ISBN: (纸本)9781509008957

Recent work has shown that the transition from persistent to permanent AF in goats coincides with an increase in fibrosis in the outer millimeter of the atrial wall. Macroscopically this leads to reduced electrical conductivity orthogonal to the dominant fiber orientation. We constructed a detailed geometry of the human atria including epicardial layer and all major endocardial bundle structures. The model also includes realistic one to three layers of fiber orientations, corresponding to their location in the atrium. The numbers of waves, phase singularities, and breakthroughs (BTs) were quantified at different degrees of fibrotic tissue. Increase in the "fibrotic" volume from zero (Control) to moderate (50% Fibrotic), and severe (70% Fibrotic) increased both the number of waves and the number of phase singularities. Along with the increase in fibrosis, the endo-epicardial electrical activity dyssynchrony increased.

关键词： cardiology

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Discrimination of Normal and At-Risk Populations from Fetal Heart Rate Variability 41

Discrimination of Normal and At-Risk Populations from Fetal ...

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41st computing in cardiology conference (CinC)

作者： Warrick, Philip A. Hamilton, Emily F. PeriGen Inc Montreal PQ Canada McGill Univ Dept Obstet & Gynecol Montreal PQ H3A 2T5 Canada

ISBN: (纸本)9781479943463

Using clinically measured intrapartum cardiotocography (CTG) data, the objective of this study was to compare the discrimination of fetal heart rate variability in the low frequency (LF, 30-150 mHz) movement frequency (MF, 150-500mHz) and high frequency (HF, 500-1 000mHz) bands for two at-risk groups: fetuses experiencing either neonatal depression or metabolic acidosis. These parameters had statistically significant differences when comparing normal fetus and these indexed groups.

关键词： cardiology medical signal processing obstetrics statistical analysis CTG data at-risk populations clinically measured intrapartum cardiotocography data fetal heart rate variability fetuses metabolic acidosis neonatal depression Abstracts Pediatrics Standards Fetal Heart Rate Variability Statistical analysis Statistical methods Population at Risk medical signal processing Metabolic Acidosis Fetus Gynecology cardiology Pediatrics Data analysis

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Time-Frequency Analysis of Phonocardiogram for Classifying Heart Disease 43

Time-Frequency Analysis of Phonocardiogram for Classifying H...

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43rd computing in cardiology conference (CinC)

作者： Banerjee, Rohan Biswas, Swagata Banerjee, Snehasis Choudhury, Anirban Dutta Chattopadhyay, Tanushyam Pal, Arpan Deshpande, Parijat Mandana, Kayapanda M. Tata Consultancy Serv Ltd Res & Innovat Kolkata India Fortis Hosp Kolkata India

ISBN: (纸本)9781509008957

Analysis of heart sounds is a popular research area for non invasive identification of several heart diseases. This paper proposes a set of 88 time-frequency features along with five different methodologies for classifying normal and abnormal heart sounds. State of the art approach was applied for segregating the fundamental heart sounds. Apart from a baseline two class classifier, separate classifiers for long and short heart sounds were also explored in order to get rid of the dependency of features on the duration of the recordings. Finally, a three class classifier was explored to deal with the noisy data present in the dataset. Both balanced and unbalanced sets were considered for crating of the training models. A comparative analysis showed that, out of all the methodologies, the three class classifier based approach produces the most optimum performance by simultaneously yielding high values of both sensitivity and specificity.

关键词： cardiology

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Mechano-Electric Feedbacks in a New Model of the Excitation-Contraction Coupling in Human Cardiomyocytes 45

Mechano-Electric Feedbacks in a New Model of the Excitation-...

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45th computing in cardiology conference (CinC)

作者： Balakina-Vikulova, Nathalie Solovyova, Olga Panfilov, Alexander Katsnelson, Leonid Russian Acad Sci Inst Immunol & Physiol Ural Branch Pervomayskaya St 106 Ekaterinburg 620041 Russia Ural Fed Univ Ekaterinburg Russia Univ Ghent Ghent Belgium

ISBN: (纸本)9781728109589

The study is aimed to develop a new human cardiomyocyte model, which describes electromechanical coupling and mechano-electric feedbacks. The combined electromechanical model (TP+M) links the TP06 elecfrophysiological model of the human cardiomyocyte with our earlier developed model of the myocardium mechanical activity and its calcium regulation. In the TP+M model, we tried to maintain principal features of calcium transients and action potentials during the twitches typical for the human cardiomyocytes. The developed TP+M model allows simulating several basic classic phenomena such as load-dependent relaxation and length-dependence of isometric twitches and respective changes in action potential duration. We have also simulated some age-dependent changes in the electrical and mechanical activity in the human cardiomyocytes.

关键词： cardiology

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