Prediction of Depth of Sedation from Biological Signals Using Continuous Restricted Boltzmann Machine
Joint Authors
Chen, Pei-Jarn
Chen, Yeou-Jiunn
Chen, Shih-Chung
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-11
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Proper anesthesia is very important for patients to get through surgery without pain and then avoid some other problems.
By monitoring the depth of sedation for a patient, it could help a clinician to provide a suitable amount of anesthetic and other clinical treatment.
In hospital, a patient is usually monitored by different types of biological systems.
To predict the depth of sedation from biological signals is able to ease patient monitoring services.
In this study, continuous restricted Boltzmann machines based neural network is proposed to predict the depth of sedation.
The biological signals including heart rate, blood pressure, peripheral capillary oxygen saturation, and body weight are selected as analytic features.
To improve the accuracy, the signals related to the state of anesthesia including fractional anesthetic concentration, end-tidal carbon dioxide, fraction inspiration carbon dioxide, and minimum alveolar concentration are also adopted in this study.
Using minimizing contrastive divergence, a continuous restricted Boltzmann machine is trained and then used to predict the depth of sedation.
The experimental results showed that the proposed approach outperforms feed-forward neural network and modular neural network.
Besides, it would be able to ease patient monitoring services by using biological systems and promote healthcare quality.
American Psychological Association (APA)
Chen, Yeou-Jiunn& Chen, Shih-Chung& Chen, Pei-Jarn. 2014. Prediction of Depth of Sedation from Biological Signals Using Continuous Restricted Boltzmann Machine. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1044070
Modern Language Association (MLA)
Chen, Yeou-Jiunn…[et al.]. Prediction of Depth of Sedation from Biological Signals Using Continuous Restricted Boltzmann Machine. Mathematical Problems in Engineering No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1044070
American Medical Association (AMA)
Chen, Yeou-Jiunn& Chen, Shih-Chung& Chen, Pei-Jarn. Prediction of Depth of Sedation from Biological Signals Using Continuous Restricted Boltzmann Machine. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1044070
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044070