Results 1 to 10 of about 677,853 (245)
Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG [PDF]
Scientific Reports, 2020 Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients ...
Pecchia, Leandro +3 more
core +3 more sources
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation [PDF]
Remote Sensing, 2020 The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias.
Anwar, Syed M. +3 more
core +2 more sources
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL [PDF]
IEEE journal of biomedical and health informatics, 2020 Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by algorithms. The progress in the field of automatic ECG analysis has up to now been hampered by a lack of appropriate ...
Nils Strodthoff +3 more
semanticscholar +1 more source
Frontiers in Physiology, 2023 Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection,
Yaqoob Ansari +3 more
semanticscholar +1 more source
New Insights into the Degradation Path of Deltamethrin
Molecules, 2021 Pyrethroids are among the insecticidal compounds indicated by the World Health Organization for mitigation of vector-borne diseases. Active deltamethrin (with chiral configuration α-S,1-R-cis) is one of the most effective pyrethroids characterized by low
Federica Aiello +3 more
doaj +1 more source
Automatic diagnosis of the 12-lead ECG using a deep neural network [PDF]
Nature Communications, 2019 The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples.
Antônio H. Ribeiro +11 more
semanticscholar +1 more source
Annals of Noninvasive Electrocardiology, 2021 The patient is a 19 years‐old man who often wakes up in dreams with palpitations and fatigue. The ECG shows: 1. Sinus rhythm; 2. Preexcitation syndrome. Transesophageal electrophysiological study (TEEPS) diagnosis:High‐risk accessory pathway.
Chao Qin, Tao He, Shuo Li
doaj +1 more source
Self-Supervised ECG Representation Learning for Emotion Recognition [PDF]
IEEE Transactions on Affective Computing, 2020 We exploit a self-supervised deep multi-task learning framework for electrocardiogram (ECG) -based emotion recognition. The proposed solution consists of two stages of learning a) learning ECG representations and b) learning to classify emotions.
Pritam Sarkar, A. Etemad
semanticscholar +1 more source
Transfer learning for ECG classification
Scientific Reports, 2021 Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity.
Kuba Weimann, T. Conrad
semanticscholar +1 more source
ECG Heartbeat Classification: A Deep Transferable Representation [PDF]
IEEE International Conference on Healthcare Informatics, 2018 Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats.
Mohammad Kachuee +2 more
semanticscholar +1 more source