Journal
DISCOVERY AND INNOVATION OF COMPUTER SCIENCE TECHNOLOGY IN ARTIFICIAL INTELLIGENCE ERA
Volume 116, Issue -, Pages 530-538Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2017.10.042
Keywords
Long Short-Term Memory; Bi-directional Long Short-Term Memory; Deep Belief Networks; Sleep Stage Classification
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The study examines the use of quantization to be applied to Bi-directional Long Short-Term Memory (Bi-LSTM), a combination of the two called qBi-LSTM. Quantization used comes from Deep Belief Networks (DBN). It selected DBN for its superiority as a generative model of Deep Learning in producing an optimal artificial feature. Development of qBi-LSTM is expected to improve the performance of Bi-LSTM and also provide efficient time. The qBi-LSTM test is applied for sleep stage classification on St. Vincent's University Hospital / University College Dublin's Sleep Apnea Database. The result shows that qBi-LSTM has the highest performance compared to Bi-LSTM and DBN with precision, recall and F-measure values of 86.00%, 72.10%, and 75.27%. The best qBi-LSTM performance is to classify Stage 2 but still fails to classify the stage of REM (Rapid Eye Movement). (C) 2017 The Authors. Published by Elsevier B.V.
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