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Supervised ECG wave segmentation using convolutional LSTM
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DOI：10.1016/j.icte.2020.04.004.png)
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摘要
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Electrocardiogram (ECG) is the graphical representation of electrical activity of the heart and is used to detect certain structural and functional heart conditions. Segmenting ECG waveforms and annotating constituent components is required for analysis of ECG and to arrive at a diagnosis. This paper proposes a Convolutional Long Short-Term Memory (ConvLSTM) neural network to segment the ECG waves. It consists of a convolutional layer followed by a Bidirectional LSTM architecture. The segmentation is achieved by adding additional features such as derivative of the ECG wave as well as the smoothened ECG wave and the model outperforms traditional Markov models. (C) 2020 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
Keyword:
ECG segmentation
LSTM
CNN
Supervised learning
ConvLSTM
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An Anatomy of Chinese: Rhythm, Metaphor, Politics, by Perry Link. Cambridge MA: Harvard University Press, 2013. viii + 367 pp. US$39.95/£29.95/€36.00 (hardcover), US$39.95 (eBook).《汉语解剖：节奏、隐喻、政治》，[美] Perry Link 著。剑桥（MA）: 哈佛大学出版社，2013年。vi + 367页。精装本售价39.95美元/29.95英镑/36.00欧元，电子书售价39.95美元。
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