Deep Learning for Detection of Fetal ECG from Multi-Channel Abdominal Leads

Fang Wen La, Pei Yun Tsai

研究成果: 書貢獻/報告類型會議論文篇章同行評審

16 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose to use a CNN-based approach for fetal ECG detection from the abdominal ECG recording. Our work flow contains a pre-processing phase and a classification phase. In the pre-processing phase, abdominal ECG waveform is normalized and segmented. Then, short-time Fourier transform is applied to obtain time-frequency representation. The 2D representation is sent to 2D convolutional neural network for classification. Two convolutional layers, two pooling layers, one fully-connected layer are used. The softmax activation function is used at the output layer to compute the probabilities of four events. The classified results from multiple channels are fused to derive the final detection according to the respective detection accuracies. Compared to the K-nearest neighbor algorithm, the CNN-based classifier has better detection accuracy.

原文???core.languages.en_GB???
主出版物標題2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1397-1401
頁數5
ISBN(電子)9789881476852
DOIs
出版狀態已出版 - 2 7月 2018
事件10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
持續時間: 12 11月 201815 11月 2018

出版系列

名字2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

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???event.eventtypes.event.conference???10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
國家/地區United States
城市Honolulu
期間12/11/1815/11/18

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