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CNSystems Medizintechnik
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Norav Medical Ltd
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Data Sciences International
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Lepu Medical Technology Co Ltd
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Bittium Corporation
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Siemens AG
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Cambridge Electronic Design
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BIOPAC
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Shanghai Yuanye Biochemicals
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Image Search Results
Journal: PLoS ONE
Article Title: Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder
doi: 10.1371/journal.pone.0260612
Figure Lengend Snippet: The filter size is 19 until the sixth residual block, after which it is reduced to nine. At the end of encoder, the extracted data are flattened and converted into the values of mean and standard deviation with a size of 60. The latent variable is sampled from the values of mean and standard deviation. The hyperparameter of the decoder is set symmetrically so that the output size is the same as that of the encoder. Average pooling is applied to the outputs of the decoder. The mean values were used as the CVAE feature vector of electrocardiogram (ECG).
Article Snippet: We used 596,000
Techniques: Blocking Assay, Standard Deviation, Plasmid Preparation
Journal: PLoS ONE
Article Title: Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder
doi: 10.1371/journal.pone.0260612
Figure Lengend Snippet: Baseline characteristics of each dataset.
Article Snippet: We used 596,000
Techniques:
Journal: PLoS ONE
Article Title: Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder
doi: 10.1371/journal.pone.0260612
Figure Lengend Snippet: Visualization of (a) t-SNE and (b) LLE in two-dimensional space for ECG from ICU and Healthy test. ECGs from ICU are shown as blue clusters while ECGs from medical-checkup are shown as red clusters. It shows that feature vectors can represent the differences between healthy and unhealthy people although the model was trained without any label.
Article Snippet: We used 596,000
Techniques:
Journal: PLoS ONE
Article Title: Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder
doi: 10.1371/journal.pone.0260612
Figure Lengend Snippet: (a) Examples of the reconstruction output of a normal electrocardiogram (ECG) are shown. Original ECG data (blue) and reconstructed ECG data (yellow) are not significantly different. (b) Examples of latent space exploration. A single blue line represents the reconstruction output of the sampled origin feature vector in the first ECG. In the remaining three ECGs, the blue line represents the reconstruction output of the feature vector that subtracts 2 from the Nth feature variable. The yellow lines represent the reconstruction output of the feature vector that adds 2 to the Nth feature variable.
Article Snippet: We used 596,000
Techniques: Plasmid Preparation