Sleep is an essential element for an individual’s well-being and is considered vital for the overall mental and physical heath of a person. Sleep can be considered as a virtual detachment of an individual from his environment. In normal humans, about 30% of their life-time is spent for sleep. Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Sleep scoring is under taken by the examination and visual inspection of polysomnograms (PSG) done by sleep specialist. PSG is specialty test, the conduction of which includes the recording of various physiological signals. The signals obtained are processed using digital processing tools so as to extract information. Soft computing techniques are used to analyze the signals. ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparations of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present. The high performances observed with systems based onneural networks highlight that these tools may be act new tools in the field of sleep research. In this scenario we are surmised the review regarding the computer assisted automatic scoring of sleep and soft computing technique Artificial Neural Network.
| Published in | International Journal of Sensors and Sensor Networks (Volume 5, Issue 3) |
| DOI | 10.11648/j.ijssn.20170503.12 |
| Page(s) | 43-47 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Sleep Scoring System, Polysomnograms (PSG), Artificial Neural Network (ANN)
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APA Style
Hemu Farooq, Anuj Jain. (2017). A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review. International Journal of Sensors and Sensor Networks, 5(3), 43-47. https://doi.org/10.11648/j.ijssn.20170503.12
ACS Style
Hemu Farooq; Anuj Jain. A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review. Int. J. Sens. Sens. Netw. 2017, 5(3), 43-47. doi: 10.11648/j.ijssn.20170503.12
AMA Style
Hemu Farooq, Anuj Jain. A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review. Int J Sens Sens Netw. 2017;5(3):43-47. doi: 10.11648/j.ijssn.20170503.12
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Download@article{10.11648/j.ijssn.20170503.12,
author = {Hemu Farooq and Anuj Jain},
title = {A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review},
journal = {International Journal of Sensors and Sensor Networks},
volume = {5},
number = {3},
pages = {43-47},
doi = {10.11648/j.ijssn.20170503.12},
url = {https://doi.org/10.11648/j.ijssn.20170503.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20170503.12},
abstract = {Sleep is an essential element for an individual’s well-being and is considered vital for the overall mental and physical heath of a person. Sleep can be considered as a virtual detachment of an individual from his environment. In normal humans, about 30% of their life-time is spent for sleep. Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Sleep scoring is under taken by the examination and visual inspection of polysomnograms (PSG) done by sleep specialist. PSG is specialty test, the conduction of which includes the recording of various physiological signals. The signals obtained are processed using digital processing tools so as to extract information. Soft computing techniques are used to analyze the signals. ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparations of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present. The high performances observed with systems based onneural networks highlight that these tools may be act new tools in the field of sleep research. In this scenario we are surmised the review regarding the computer assisted automatic scoring of sleep and soft computing technique Artificial Neural Network.},
year = {2017}
}
TY - JOUR T1 - A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review AU - Hemu Farooq AU - Anuj Jain Y1 - 2017/11/02 PY - 2017 N1 - https://doi.org/10.11648/j.ijssn.20170503.12 DO - 10.11648/j.ijssn.20170503.12 T2 - International Journal of Sensors and Sensor Networks JF - International Journal of Sensors and Sensor Networks JO - International Journal of Sensors and Sensor Networks SP - 43 EP - 47 PB - Science Publishing Group SN - 2329-1788 UR - https://doi.org/10.11648/j.ijssn.20170503.12 AB - Sleep is an essential element for an individual’s well-being and is considered vital for the overall mental and physical heath of a person. Sleep can be considered as a virtual detachment of an individual from his environment. In normal humans, about 30% of their life-time is spent for sleep. Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Sleep scoring is under taken by the examination and visual inspection of polysomnograms (PSG) done by sleep specialist. PSG is specialty test, the conduction of which includes the recording of various physiological signals. The signals obtained are processed using digital processing tools so as to extract information. Soft computing techniques are used to analyze the signals. ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparations of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present. The high performances observed with systems based onneural networks highlight that these tools may be act new tools in the field of sleep research. In this scenario we are surmised the review regarding the computer assisted automatic scoring of sleep and soft computing technique Artificial Neural Network. VL - 5 IS - 3 ER -
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