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Image Search Results


Examples of sleep recordings and hypnograms from the ( a ) EEG, and ( b ) ECG datasets.

Journal: Sensors (Basel, Switzerland)

Article Title: Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models

doi: 10.3390/s23052458

Figure Lengend Snippet: Examples of sleep recordings and hypnograms from the ( a ) EEG, and ( b ) ECG datasets.

Article Snippet: ECG signals were similarly preprocessed in two steps using MATLAB R2019a v9.6.0.

Techniques:

Basic procedure for the sleep staging classification in the ( a ) ECG model, ( b ) EEG model, and ( c ) EEG–ECG transfer learning model.

Journal: Sensors (Basel, Switzerland)

Article Title: Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models

doi: 10.3390/s23052458

Figure Lengend Snippet: Basic procedure for the sleep staging classification in the ( a ) ECG model, ( b ) EEG model, and ( c ) EEG–ECG transfer learning model.

Article Snippet: ECG signals were similarly preprocessed in two steps using MATLAB R2019a v9.6.0.

Techniques:

Classification accuracy, Cohen’s kappa, and F1 score (mean ± standard deviation) of the EEG model, ECG model, and the EEG–ECG transfer learning model.

Journal: Sensors (Basel, Switzerland)

Article Title: Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models

doi: 10.3390/s23052458

Figure Lengend Snippet: Classification accuracy, Cohen’s kappa, and F1 score (mean ± standard deviation) of the EEG model, ECG model, and the EEG–ECG transfer learning model.

Article Snippet: ECG signals were similarly preprocessed in two steps using MATLAB R2019a v9.6.0.

Techniques: Standard Deviation, Blocking Assay

Accuracy (upper panel) and loss (lower panel) functions of the ( a ) EEG model, ( b ) ECG model, and ( c ) EEG–ECG model (frozen block_1).

Journal: Sensors (Basel, Switzerland)

Article Title: Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models

doi: 10.3390/s23052458

Figure Lengend Snippet: Accuracy (upper panel) and loss (lower panel) functions of the ( a ) EEG model, ( b ) ECG model, and ( c ) EEG–ECG model (frozen block_1).

Article Snippet: ECG signals were similarly preprocessed in two steps using MATLAB R2019a v9.6.0.

Techniques: Blocking Assay

Confusion matrix of the ( a ) EEG, ( b ) ECG, and ( c ) EEG–ECG model (frozen block_1).

Journal: Sensors (Basel, Switzerland)

Article Title: Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models

doi: 10.3390/s23052458

Figure Lengend Snippet: Confusion matrix of the ( a ) EEG, ( b ) ECG, and ( c ) EEG–ECG model (frozen block_1).

Article Snippet: ECG signals were similarly preprocessed in two steps using MATLAB R2019a v9.6.0.

Techniques: Blocking Assay

Performance of different sleep staging systems based on CNNs with ECG signals.

Journal: Sensors (Basel, Switzerland)

Article Title: Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models

doi: 10.3390/s23052458

Figure Lengend Snippet: Performance of different sleep staging systems based on CNNs with ECG signals.

Article Snippet: ECG signals were similarly preprocessed in two steps using MATLAB R2019a v9.6.0.

Techniques: