Bi-directional Long Short-Term Memory using Quantized data of Deep Belief Networks for Sleep Stage Classification

Abstract Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

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Combining Deep Belief Networks and Bidirectional Long Short-Term Memory

Proceeding of the Electrical Engineering Computer Science and Informatics ◽

10.11591/eecsi.v4.1051 ◽

2017 ◽

Vol 4 (1) ◽

Author(s):

Intan Nurma Yulita ◽

Mohamad Ivan Fanany ◽

Aniati Murni Arymurthy

Keyword(s):

Short Term Memory ◽

Belief Networks ◽

Deep Belief Networks ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Investigating the use of uni-directional and bi-directional long short-term memory models for automatic sleep stage scoring

Informatics in Medicine Unlocked ◽

10.1016/j.imu.2020.100370 ◽

2020 ◽

Vol 20 ◽

pp. 100370

Author(s):

Luay Fraiwan ◽

Mohanad Alkhodari

Keyword(s):

Short Term Memory ◽

Sleep Stage ◽

Memory Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Sleep Stage Scoring

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A COMBINATION DEEP BELIEF NETWORKS AND SHALLOW CLASSIFIER FOR SLEEP STAGE CLASSIFICATION

Kursor ◽

10.28961/kursor.v8i4.97 ◽

2017 ◽

pp. 197

Author(s):

Intan Nurma Yulita ◽

Rudi Rosadi ◽

Sri Purwani ◽

Rolly Maulana Awangga

Keyword(s):

Naive Bayes ◽

Sleep Stage ◽

Naïve Bayes ◽

Support Vector ◽

Belief Networks ◽

Deep Belief Networks ◽

K Nearest Neighbor ◽

Stage Classification ◽

Sleep Stage Classification ◽

High Level

In this research, it is proposed to use Deep Belief Networks (DBN) in shallow classifier for the automatic sleep stage classification. The automatic classification is required to minimize Polysomnography examination time because it needs more than two days for analysis manually. Thus the automatic mechanism is required. The Shallow classifier used in this research includes Naïve Bayes (NB), Bayesian Networks (BN), Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN). The results obtained that many methods of the shallow classifier are increasing precision, recall, and F-Measure if they use DBN output as input for classification. Experiments that have been done indicate a significant increase of Naive Bayes after being combined with DBN. The high-level features generated by DBN are proven to be useful in helping Naive Bayes' performance. On the other hand, the combination of KNN with DBN shows a decrease because high-level features of DBN make it harder to find neighbors that optimize the performance of KNN.

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