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Deep Learning for Detection of Fetal ECG from Multi-Channel Abdominal Leads
Fang Wen La,
Pei Yun Tsai
電機工程學系
研究成果
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書貢獻/報告類型
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會議論文篇章
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同行評審
16
引文 斯高帕斯（Scopus）
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指紋
深入研究「Deep Learning for Detection of Fetal ECG from Multi-Channel Abdominal Leads」主題。共同形成了獨特的指紋。
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Keyphrases
Preprocessing Techniques
100%
Multi-channel
100%
Deep Learning
100%
Detection Accuracy
100%
Ambulatory ECG
100%
Abdominal
100%
Fetal ECG
100%
K-nearest
50%
Short-time Fourier Transform
50%
Time-frequency Representation
50%
CNN-based
50%
ECG Recording
50%
Convolutional Layer
50%
2D Convolutional Neural Network
50%
Fully Connected Layer
50%
Classification Phase
50%
Output Layer
50%
Pooling Layer
50%
CNN Classifier
50%
ECG Detection
50%
Layer Two
50%
ECG Waveform
50%
Softmax Activation Function
50%
2D Representation
50%
Engineering
Multichannel
100%
Deep Learning
100%
Preprocessing Phase
100%
Short-Time Fourier Transform
50%
Frequency Representation
50%
Convolutional Neural Network
50%
Nearest Neighbor
50%
Convolutional Layer
50%
Activation Function
50%
Phase Classification
50%
Output Layer
50%
Flow Work
50%
Computer Science
Convolutional Neural Network
100%
Deep Learning
100%
Detection Accuracy
66%
Preprocessing Phase
66%
time-frequency representation
33%
Multiple Channel
33%
Convolutional Layer
33%
Activation Function
33%
Workflow
33%
Time Fourier Transform
33%
Immunology and Microbiology
Electrocardiogram
100%
K Nearest Neighbor
25%