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Gould Instruments System Inc
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BioSignal Group
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BIOPAC
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DATAQ Instruments
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BIOPAC
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CamNtech Ltd
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Hellige GMBH
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TOSHIBA Medical
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Microstar Laboratories
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Image Search Results
Journal: Healthcare Informatics Research
Article Title: Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model
doi: 10.4258/hir.2019.25.3.201
Figure Lengend Snippet: Acceptable and non-acceptable electrocardiogram (ECG) examples. Non-acceptable ECGs have global (A) or local (B) noise, or signals from ECG equipment rather than patients (C). (D) and (E) are examples of acceptable ECGs.
Article Snippet: From September 1, 2016 to September 30, 2018, the
Techniques:
Journal: Healthcare Informatics Research
Article Title: Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model
doi: 10.4258/hir.2019.25.3.201
Figure Lengend Snippet: Study process flow. From 15,400 electrocardiograms (ECGs) reviewed by nonexperts, 2,400 and 300 ECGs were confirmed by a medical expert and used as training and validation dataset. Three hundred ECGs independently gathered from different periods were reviewed by the expert and used to evaluate the performance of the developed deep learning-based model.
Article Snippet: From September 1, 2016 to September 30, 2018, the
Techniques: Biomarker Discovery
Journal: Healthcare Informatics Research
Article Title: Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model
doi: 10.4258/hir.2019.25.3.201
Figure Lengend Snippet: Web-based labeling tool. The tool presented 10-second electrocardiogram waveforms and asked evaluators if they were acceptable or unacceptable. All answers were recorded and managed in the backend database.
Article Snippet: From September 1, 2016 to September 30, 2018, the
Techniques: Labeling