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Prediction of Depth of Sedation from Biological Signals Using Continuous Restricted Boltzmann Machine
المؤلفون المشاركون
Chen, Pei-Jarn
Chen, Yeou-Jiunn
Chen, Shih-Chung
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-09-11
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1044070
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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