Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
A comprehensive survey on heart sound analysis in the deep learning era
Heart sound auscultation has been applied in clinical usage for early screening of
cardiovascular diseases. Due to the high demand for auscultation expertise, automatic …
cardiovascular diseases. Due to the high demand for auscultation expertise, automatic …
An optimal approach for heart sound classification using grid search in hyperparameter optimization of machine learning
Heart-sound auscultation is one of the most widely used approaches for detecting
cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope …
cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope …
[HTML][HTML] Major advances in geostationary fire radiative power (FRP) retrieval over Asia and Australia stemming from use of Himarawi-8 AHI
Characterising the highly variable temporal dynamics of landscape-scale fire activity is best
achieved using geostationary satellites, and the Himawari-8 Advanced Himawari Imager …
achieved using geostationary satellites, and the Himawari-8 Advanced Himawari Imager …
Applications of self-supervised learning to biomedical signals: A survey
Over the last decade, deep learning applications in biomedical research have exploded,
demonstrating their ability to often outperform previous machine learning approaches in …
demonstrating their ability to often outperform previous machine learning approaches in …
Detection of heart murmurs in phonocardiograms with parallel hidden semi-markov models
A McDonald, MJF Gales… - 2022 Computing in …, 2022 - ieeexplore.ieee.org
We describe a recurrent neural network and hidden semi Markov model (HSMM) approach
to detect heart murmurs in phonocardiogram recordings. This model forms the winning' …
to detect heart murmurs in phonocardiogram recordings. This model forms the winning' …
A lightweight robust approach for automatic heart murmurs and clinical outcomes classification from phonocardiogram recordings
H Lu, JB Yip, T Steigleder… - 2022 Computing in …, 2022 - ieeexplore.ieee.org
Cardiac auscultation provides an efficient and cost-effective way for cardiac disease pre-
screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs …
screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs …
Hierarchical multi-scale convolutional network for murmurs detection on pcg signals
Computer-aided analysis is helpful in improving heart sound classification. PhysioNet
Challenge 2022 provides a platform for researchers to evaluate their proposed classification …
Challenge 2022 provides a platform for researchers to evaluate their proposed classification …
Listen2yourheart: A self-supervised approach for detecting murmur in heart-beat sounds
A Ballas, V Papapanagiotou… - 2022 Computing in …, 2022 - ieeexplore.ieee.org
Heart murmurs are abnormal sounds present in heartbeats, caused by turbulent blood flow
through the heart. The PhysioNet 2022 challenge targets automatic detection of murmur …
through the heart. The PhysioNet 2022 challenge targets automatic detection of murmur …