FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3160994088> ?p ?o ?g. }

• W3160994088 endingPage "78" @default.
• W3160994088 startingPage "71" @default.
• W3160994088 abstract "Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versus time traces over a period. Deep learning technology, a robust artificial intelligence algorithm, can imitate the data processing patterns of the human brain, and it has experienced remarkable success in disease screening, diagnosis, and prediction. Compared with traditional machine learning, deep learning algorithms possess more powerful learning capabilities and can automatically extract features without extensive data pre-processing or hand-crafted feature extraction, which makes it a suitable tool to analyze complex structures of high-dimensional data. With the advances in computing power and digitized data availability, deep learning provides us an opportunity to improve ECG data interpretation with higher efficacy and accuracy and, more importantly, expand the original functions of ECG. The application of deep learning has led us to stand at the edge of ECG innovation and will potentially change the current clinical monitoring and management strategies. In this review, we introduce deep learning technology and summarize its advantages compared with traditional machine learning algorithms. Moreover, we provide an overview on the current application of deep learning in ECGs, with a focus on arrhythmia (especially atrial fibrillation during normal sinus rhythm), cardiac dysfunction, electrolyte imbalance, and sleep apnea. Last but not least, we discuss the current challenges and prospect directions for the following studies." @default.
• W3160994088 created "2021-05-24" @default.
• W3160994088 creator A5002208932 @default.
• W3160994088 creator A5010974487 @default.
• W3160994088 creator A5035123411 @default.
• W3160994088 creator A5049990969 @default.
• W3160994088 creator A5061752926 @default.
• W3160994088 date "2021-08-01" @default.
• W3160994088 modified "2023-10-02" @default.
• W3160994088 title "The application of deep learning in electrocardiogram: Where we came from and where we should go?" @default.
• W3160994088 cites W1962132018 @default.
• W3160994088 cites W1976172310 @default.
• W3160994088 cites W2031815174 @default.
• W3160994088 cites W2033989169 @default.
• W3160994088 cites W2047549866 @default.
• W3160994088 cites W2062675620 @default.
• W3160994088 cites W2065028337 @default.
• W3160994088 cites W2072331803 @default.
• W3160994088 cites W2076008670 @default.
• W3160994088 cites W2108199928 @default.
• W3160994088 cites W2129040169 @default.
• W3160994088 cites W2132350210 @default.
• W3160994088 cites W2136228583 @default.
• W3160994088 cites W2137434301 @default.
• W3160994088 cites W2152271789 @default.
• W3160994088 cites W2162800060 @default.
• W3160994088 cites W2163784611 @default.
• W3160994088 cites W2462346793 @default.
• W3160994088 cites W2547342533 @default.
• W3160994088 cites W2747849569 @default.
• W3160994088 cites W2767583913 @default.
• W3160994088 cites W2774566807 @default.
• W3160994088 cites W2784094750 @default.
• W3160994088 cites W2803427969 @default.
• W3160994088 cites W2891342985 @default.
• W3160994088 cites W2900614987 @default.
• W3160994088 cites W2901226889 @default.
• W3160994088 cites W2902644322 @default.
• W3160994088 cites W2906699286 @default.
• W3160994088 cites W2916326551 @default.
• W3160994088 cites W2919098904 @default.
• W3160994088 cites W2919115771 @default.
• W3160994088 cites W2921335396 @default.
• W3160994088 cites W2928542791 @default.
• W3160994088 cites W2932995131 @default.
• W3160994088 cites W2941386706 @default.
• W3160994088 cites W2942975361 @default.
• W3160994088 cites W2946078250 @default.
• W3160994088 cites W2946751363 @default.
• W3160994088 cites W2953616918 @default.
• W3160994088 cites W2965520043 @default.
• W3160994088 cites W2966096559 @default.
• W3160994088 cites W2966674791 @default.
• W3160994088 cites W2966850485 @default.
• W3160994088 cites W2980240687 @default.
• W3160994088 cites W2981624778 @default.
• W3160994088 cites W2990156705 @default.
• W3160994088 cites W2999069903 @default.
• W3160994088 cites W3000250023 @default.
• W3160994088 cites W3001733392 @default.
• W3160994088 cites W3002596471 @default.
• W3160994088 cites W3006254974 @default.
• W3160994088 cites W3006389498 @default.
• W3160994088 cites W3008117014 @default.
• W3160994088 cites W3013966144 @default.
• W3160994088 cites W3016291932 @default.
• W3160994088 cites W3016963826 @default.
• W3160994088 cites W3022945091 @default.
• W3160994088 cites W3032777011 @default.
• W3160994088 cites W3033523981 @default.
• W3160994088 cites W3043833357 @default.
• W3160994088 cites W3083389951 @default.
• W3160994088 cites W3084228385 @default.
• W3160994088 cites W3084949122 @default.
• W3160994088 cites W3087200483 @default.
• W3160994088 cites W3096743003 @default.
• W3160994088 cites W3099085560 @default.
• W3160994088 cites W3104075323 @default.
• W3160994088 cites W3109414960 @default.
• W3160994088 cites W3127307335 @default.
• W3160994088 cites W3130855098 @default.
• W3160994088 cites W3134395396 @default.
• W3160994088 cites W3136766298 @default.
• W3160994088 cites W3215602515 @default.
• W3160994088 doi "https://doi.org/10.1016/j.ijcard.2021.05.017" @default.
• W3160994088 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34000355" @default.
• W3160994088 hasPublicationYear "2021" @default.
• W3160994088 type Work @default.
• W3160994088 sameAs 3160994088 @default.
• W3160994088 citedByCount "9" @default.
• W3160994088 countsByYear W31609940882021 @default.
• W3160994088 countsByYear W31609940882022 @default.
• W3160994088 countsByYear W31609940882023 @default.
• W3160994088 crossrefType "journal-article" @default.
• W3160994088 hasAuthorship W3160994088A5002208932 @default.
• W3160994088 hasAuthorship W3160994088A5010974487 @default.
• W3160994088 hasAuthorship W3160994088A5035123411 @default.
• W3160994088 hasAuthorship W3160994088A5049990969 @default.

• next