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Relevant bibliographies by topics / Cinc 2020 challenge dataset

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Academic literature on the topic 'Cinc 2020 challenge dataset'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Cinc 2020 challenge dataset"

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Moqurrab, Syed Atif, Hari Mohan Rai, and Joon Yoo. "HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings." Algorithms 17, no. 8 (2024): 364. http://dx.doi.org/10.3390/a17080364.

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Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we

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Alhasani, Ahmed T., Zainab H. Albakaa, Shahad A. Alabidi, Osamah Qasim Abd Zaid Gburi, Ammar Kadi, and Irina Potoroko. "Heartbeat Sound Classification Using Mel-Spectrogram and CNN Optimized by Frilled Lizard Algorithm for Cardiovascular Disease Detection." Mesopotamian Journal of Artificial Intelligence in Healthcare 2025 (May 21, 2025): 96–104. https://doi.org/10.58496/mjaih/2025/010.

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Cardiovascular disease (CVD) continues to be the predominant cause of mortality globally, underscoring the critical necessity for prompt and precise diagnostic techniques. This paper introduces an innovative machine learning framework for categorizing heartbeat sounds into four classifications—normal, murmur, additional heart sound, and artifact—utilizing audio recordings from the PhysioNet/CinC Challenge 2016 dataset. The methodology employs Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, converting raw heart sound data into comprehensive time-frequenc

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Rahman Ahad, Md Atiqur, and Israt Jahan. "A Study of Left Ventricular (LV) Segmentation on Cardiac Cine-MR Images." Jurnal Kejuruteraan 34, no. 3 (2022): 463–73. http://dx.doi.org/10.17576/jkukm-2022-34(3)-13.

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Left ventricular segmentation from cardiac images has high impact to have early diagnosis of various cardiovascular disorders. However, it is really a challenging task to segment left ventricular images from magnetic resonance image (MRI). In this paper, we explore several state-of-the-art segmentation algorithms applied on left ventricular (LV) segmentation on cardiac cine-MR images. Both adaptive and global thresholding algorithms along with region-based segmentation algorithm have been explored. Edge-based segmentation is disregard due to the absence of edge information in the employed data

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Abdessater, Elza, Paniz Balali, Jimmy Pawlowski, et al. "A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease." Sensors 25, no. 11 (2025): 3360. https://doi.org/10.3390/s25113360.

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Severe aortic valve disease (AVD) cause changes in heart sounds, making phonocardiogram (PCG) analyses challenging. This study presents a novel method for segmenting heart sounds without relying on an electrocardiogram (ECG), specifically targeting patients with severe AVD. Our algorithm enhances traditional Hidden Semi-Markov Models by incorporating signal envelope calculations and statistical tests to improve the detection of the first and second heart sounds (S1 and S2). We evaluated the method on the PhysioNet/CinC 2016 Challenge dataset and a newly acquired AVD-specific dataset. The metho

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Arora, Vinay, Rohan Leekha, Raman Singh, and Inderveer Chana. "Heart sound classification using machine learning and phonocardiogram." Modern Physics Letters B 33, no. 26 (2019): 1950321. http://dx.doi.org/10.1142/s0217984919503214.

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This research pertains to classification of the heart sound using digital Phonocardiogram (PCG) signals targeted to screen for heart ailments. In this study, an existing variant of the decision tree, i.e. XgBoost has been used with unsegmented heart sound signal. The dataset provided by PhysioNet Computing in Cardiology (CinC) Challenge 2016 has been used to validate the technique proposed in this research work. The said dataset comprises six databases (A–F) having 3240 heart sound recordings in all with the duration lasting from 5–120 s. The approach proposed in this paper has been compared w

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Alotaiby, Turky N., Nuwayyir A. Alsahle, and Gaseb N. Alotibi. "Abnormal Heart Sound Detection Using Common Spatial Patterns and Random Forests." Electronics 14, no. 8 (2025): 1512. https://doi.org/10.3390/electronics14081512.

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Early and accurate diagnosis of heart conditions is pivotal for effective treatment. Phonocardiography (PCG) has become a standard diagnostic tool for evaluating and detecting cardiac abnormalities. While traditional cardiac auscultation remains widely used, its accuracy is highly dependent on the clinician’s experience and auditory skills. Consequently, there is a growing need for automated, objective methods of heart sound analysis. This study explores the efficacy of the Common Spatial Patterns (CSP) feature extraction algorithm paired with the Random Forest (RF) classifier to distinguish b

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Sridhar, Niranjan, Atiyeh Ghoreyshi, Lance Myers, and Zachary Owens. "247 Automated sleep staging using wrist-worn device and deep neural networks." Sleep 44, Supplement_2 (2021): A100. http://dx.doi.org/10.1093/sleep/zsab072.246.

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Abstract Introduction Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can be performed using only cardiac rhythms, then existing medical and consumer-grade devices that can measure heart rate can enable low-cost sleep evaluations. Methods We trained a neural network which uses dilated convolutional blocks to learn both local and long range features of heart rate extracted from ECG R-wave timing to predict for every non-overlapping 30s epoch of the input the probabilities of the epoch being in one of four classes—wake, light sleep, deep sleep or REM.

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Li, Fan, Hong Tang, Shang Shang, Klaus Mathiak, and Fengyu Cong. "Classification of Heart Sounds Using Convolutional Neural Network." Applied Sciences 10, no. 11 (2020): 3956. http://dx.doi.org/10.3390/app10113956.

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Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connec

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Lee, Hyeonjeong, and Miyoung Shin. "Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs." Sensors 21, no. 13 (2021): 4331. http://dx.doi.org/10.3390/s21134331.

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Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily cardiac health monitoring. In this study, we propose a novel image-based deep learning framework to classify single-lead ECG recordings of short variable length into several different rhythms associated with arrhythmias. By transforming variable-length 1D ECG signals into fixed-size 2D time-morphology representations and feeding them to the beat–interval–texture convolutional neural network (BIT-CNN) model, we

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Orozco-Reyes, Leonel, Miguel A. Alonso-Arévalo, Eloísa García-Canseco, Roilhi F. Ibarra-Hernández, and Roberto Conte-Galván. "A Deep-Learning Approach to Heart Sound Classification Based on Combined Time-Frequency Representations." Technologies 13, no. 4 (2025): 147. https://doi.org/10.3390/technologies13040147.

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Worldwide, heart disease is the leading cause of mortality. Cardiac auscultation, when conducted by a trained professional, is a non-invasive, cost-effective, and readily available method for the initial assessment of cardiac health. Automated heart sound analysis offers a promising and accessible approach to supporting cardiac diagnosis. This work introduces a novel method for classifying heart sounds as normal or abnormal by leveraging time-frequency representations. Our approach combines three distinct time-frequency representations—short-time Fourier transform (STFT), mel-scale spectrogram

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Book chapters on the topic "Cinc 2020 challenge dataset"

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Scharr, Hanno, Benjamin Bruns, Andreas Fischbach, Johanna Roussel, Lukas Scholtes, and Jonas vom Stein. "Germination Detection of Seedlings in Soil: A System, Dataset and Challenge." In Computer Vision – ECCV 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65414-6_25.

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Jiang, Liming, Wayne Wu, Chen Qian, and Chen Change Loy. "DeepFakes Detection: the Dataset and Challenge." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_14.

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AbstractRecent years have witnessed exciting progress in automatic face swapping and editing. Many techniques have been proposed, facilitating the rapid development of creative content creation. The emergence and easy accessibility of such techniques, however, also cause potential unprecedented ethical and moral issues. To this end, academia and industry proposed several effective forgery detection methods. Nonetheless, challenges could still exist. (1) Current face manipulation advances can produce high-fidelity fake videos, rendering forgery detection challenging. (2) The generalization capa

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Liu, Feifei, Yonglian Ren, Shengxiang Xia, et al. "Classification of Heart Sounds Based on Topological Data Analysis Method." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220409.

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Topological data analysis (TDA) method could catch the rich geometric and topologic information of big data and find subtle differences between different signals. TDA method opens up a new way for biomedical data analysis. In this study, we applied TDA method for heart sound signals (PCG) classification. First, the sliding window method was used to build a point cloud. Then, the persistent barcode is extracted from the point cloud by using the topology technology Vietoris-Rips (VR) filtration. At last, GoogLeNet transfer learning model was applied for classifing. The proposed the model did wor

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Sharma, Ashish, and Shivnarayan Patidar. "A Fourier-Bessel Expansion-Based Method for Automated Detection of Atrial Fibrillation From Electrocardiogram Signals." In Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7131-5.ch009.

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This chapter presents a new methodology for detection and identification of cardiovascular diseases from a single-lead electrocardiogram (ECG) signal of short duration. More specifically, this method deals with the detection of the most common cardiac arrhythmia called atrial fibrillation (AF) in noisy and non-clinical environment. The method begins with appropriate pre-processing of ECG signals in order to get the RR-interval and heart rate (HR) signals from them. A set of indirect features are computed from the original and the transformed versions of RR-interval and HR signals along with a

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Conference papers on the topic "Cinc 2020 challenge dataset"

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Siminyu, Kathleen, and Sackey Freshia. "AI4D - African Language Dataset Challenge." In Proceedings of the The Fourth Widening Natural Language Processing Workshop. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.winlp-1.18.

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Alhosseini, Seyed Ali, Raad Bin Tareaf, and Christoph Meinel. "Engaging with Tweets: The Missing Dataset On Social Media." In RecSys Challenge '20: Proceedings of the Recommender Systems Challenge 2020. ACM, 2020. http://dx.doi.org/10.1145/3415959.3415999.

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Reyna, Matthew, Erick Andres Perez Alday, Annie Gu, et al. "Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020." In 2020 Computing in Cardiology Conference. Computing in Cardiology, 2020. http://dx.doi.org/10.22489/cinc.2020.236.

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Anjum, Samreen, and Danna Gurari. "CTMC: Cell Tracking with Mitosis Detection Dataset Challenge." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00499.

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Porta, Alberto, Beatrice Cairo, Beatrice De Maria, and Vlasta Bari. "Complexity of Spontaneous QT Variability Unrelated to RR Variations and Respiration During Graded Orthostatic Challenge." In 2020 Computing in Cardiology Conference. Computing in Cardiology, 2020. http://dx.doi.org/10.22489/cinc.2020.009.

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Padovano, Daniele, Arturo Martinez-Rodrigo, José Manuel Pastor, José J Rieta, and Raul Alcaraz. "Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge." In 2020 Computing in Cardiology Conference. Computing in Cardiology, 2020. http://dx.doi.org/10.22489/cinc.2020.400.

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Yun, Youngsik. "Culturally-aware Image Captioning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/975.

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The primary research challenge lies in mitigating and measuring geographical and demographic biases in generative models, which is crucial for ensuring fairness in AI applications. Existing models trained on web-crawled datasets like LAION-400M often perpetuate harmful stereotypes and biases, especially concerning minority groups or less-represented regions. To address this, I proposed a framework called CIC (Culturally-aware Image Caption) to generate culturally-aware image captions. This framework leverages visual question answering (VQA) to extract cultural visual elements from images. It p

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Clapes, Albert, Julio C. S. Jacques Junior, Carla Morral, and Sergio Escalera. "ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results." In 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). IEEE, 2020. http://dx.doi.org/10.1109/fg47880.2020.00135.

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Abdelhamed, Abdelrahman, Mahmoud Afifi, Radu Timofte, et al. "NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00256.

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Jain, Sarthak, Madeleine van Zuylen, Hannaneh Hajishirzi, and Iz Beltagy. "SciREX: A Challenge Dataset for Document-Level Information Extraction." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.670.

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Reports on the topic "Cinc 2020 challenge dataset"

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Beuermann, Diether, Bridget Hoffmann, Marco Stampini, David Vargas, and Diego A. Vera-Cossio. Shooting a Moving Target: Choosing Targeting Tools for Social Programs. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0005502.

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A key challenge for policymakers is how to design methods to select beneficiaries of social programs when income is volatile and the target population is dynamic. We evaluate a traditional static proxy-means test (PMT) and three policy-relevant alternatives. We use a unique panel dataset of a random sample of households in Colombia's social registry that contains information before, during, and after the 2020 economic crisis. Updating the PMT data does not improve social welfare relative to the static PMT. Relaxing the eligibility threshold reduces the exclusion error, increases the inclusion

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