A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review
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.
A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review, International Journal of Sensors and Sensor Networks.
Vol. 5, No. 3,
2017, pp. 43-47.
ZT. Yeh, RP. Chiang, SC. Kang, CH. Chiang “Development of the Insomnia Screening Scale based on ICSD-II.”, Int J Psychiatry Clin Pract, 16(4), pp. 259-267, Oct. 2012.
HJ. Park, JS. Oh, DU. Jeong, KS. Park “Automated Sleep Stage ScoringUsing Hybrid Rule- and Case-Based Reasoning.”, Computers andBiomedical Research, 33, pp. 330-349, 2000.
David T. Krausman, Bel Air, Richard P. Allen, “Sleep Scoring apparatus and method. Google Patents,” 12 April 2005.
Park, H. J., Oh, J. S., Jeong, D. U., Park, K. S., “Automated Sleep Stage Scoring using hybrid rule and case based reasoning”, Computers and Biomedical Research, 2000, 33(5), pp.330-349.
Vani Rao, M. D., Alyssa Bergey, B. A., Hugh Hill, M. D., David Efron, M. D., and Una McCann, M. D. Clinical Research Reports, Sleep Disturbance After Mild Traumatic Brain Injury: Indicator of Injury?he Journal of Neuropsychiatry and Clinical Neurosciences. 23: 2; 201-205, 2011.
Penzel, T., Conradt, R., Computer based sleep recording and analysis. Sleep Med. Rev. 4:131-138, 2000.
Moser, D., Anderer, P., Gruber, G., Parapaties, S., Loretz, E., Boeck, M., Danker-Hopfe, H., “Sleep Classification according to AASM and Rechtschaffen and Kales: effects on sleep scoring parameters”, Sleep, 2009, 32(2), pp.139-149.
Silber, M. H., Ancoli-Isreal, S., Bonnet, M. H., Chokroverty, S., Grigg-Damberger, M. M., Hirshkowitz, M., Penzel, T., “The visual Scoring of Sleep in Adults”, J Clin Sleep Med, 2007, 3(2), pp.121-131.
Marzec, M. L., Malow, B. A., “Approaches to staging sleep in polysomnographic studies with epileptic activity”, Sleep Med. Sep 2003; 4(5): 409-17.
Sonali, B., Maind, Priyanka Wankar, “Research paper on basic of Artificial Neural Network”, International journal on recent and innovation trends in computing and communication, Jan 2014; 2321-8169.
Ming Ming Liu, Walter Herzog Hans. H. C. M. Savelberg., “Dynamic muscle force prediction from EMG: an artificial neural network approach”, Journal of Electromyography and Kinesiology, 1999, 9(6), pp.391-400.
Shimada, T., Shiina, T., Saito, Y., “Detection of characteristic waves of Sleep EEG by neural network analysis”, IEEE Transactions on Biomedical Engineering, 2000, 47(3), pp. 369-379.
Tagluk, M. E., Sezgin, N., and Akin, M., “Estimation of Sleep Stages by an Artificial Neural Network employing EEG, EMG, EOG”, Journal of Medical System, 2010, 34(4), pp. 717-725.
Mousmita Sarma, Kandarpa Kumar Sarma, “Fundamentals consideration of ANN”, Phoneme-Based speech segmentation using hybrid soft computing frame work, April 2014; 47-75.
Wang, L., and Buchannan, T. S., “Prediction of Joint Movements using a Neural Network model of Muscle Activation from EMG Signals”, IEEE Transaction on Neural System and Rehabilitation Engineering, 2002, 10(1), pp. 30-37.
Kulkarni, P., Ade R., “Incremental learning from unbalanced data with concept drift and missing features: a review”, International journal of data Min knowledge management process, 2014; 15-29.
Mehmet Akin, Muhammed B., Kurt, “Estimating vigilance level by using EEG and EMG signals”, Neural computing and applicatons, 2008, pp. 227-236.
Subasi, A., Yilmaz, M., and Ozcalik, H. R., “Classification of EMG Signals using Wavelet Neural Network”, Journal of Neuroscience Methods, 2006, 156(1), pp. 360-367.
Claude Roberta, Christian Guilpinb, Ayne Linuogea, “Review of Neural Network Applications in sleep research”, Journal of Neuroscience Method, 1998, pp. 187-193.
Enrique Dominguez Merino, Jose Munoz-Perez, “An efficient Neural Network Algorithm for p-medium problem”, Advances in Artificial Intelligence, 2002, pp. 460-469.
Gevins A. L., Morgan N. H., “Applications of neural networks signal processing in brain research”, IEEE Transcations on Acoustics, speech and signal processing, 1998; 36(7): 1152-1161.
Grozinger M., Freisleben B., Roschke, “Comparison of back propagation neural network and a non parametric discriminant analysis in the evaluation of sleep EEG data”, Pro of the world of congress on NN, 1994; 1: 462-466.
Roschke J., Kloppel B., “Automatic recognition of REM sleep by ANN”, Sleep Research, 1995; 4: 86-91.
Hasan, J., “Automatic analysis of sleep recordings: A critical review”, Annals of clinical research, 1985; 17: 280-287.
Hertz J., Kroch A., Palmer R., “Introduction to the theory of neural computation”, Wesley publication company, 1991; 326.
Kemsley D. H., Martinez T. R., Campbell D. M., “A survey of neural network research and fielded applications”, International journal of neural networks, 1991; 2: 123-132.
Miler A. S., Blott B. H., Hames T. K., “Review of neural network applications in medical imaging and signal processing” Med and Bio Eng and computation 1992; 30: 449-464.
Principe J. C., Tome A. M. P., “Performance and training strategies in feed forward NN: an application to sleep scoring, Proceeding of the IJCNN’89; 1: 341-346.
Eric A. Nofzinger, Jeffrey J., Damian F., “Apparatus and method for modulating sleep. Google Patents”, 2015.
Conor Heneghan, Conor Hanley, Niall Fox, “Apparatus, System and method of monitoring physiological signs. Google Patents”, 2013.
Park H. J., J. S. Oh, D. U. Jeong, K. S. Park “Automated Sleep Stage ScoringUsing Hybrid Rule- and Case-Based Reasoning.”, Computers andBiomedical Research, 33, pp. 330-349, 2000.
Grigg-Damberger, M. M., “The AASM Scoring Manual four years later”, J clin Sleep Med 2012; 8(3): 323-332.
Luana Novelli, Raffaele Ferri, Oliviero Bruni, “Sleep classification according to AASM and R and K: effects on sleep scoring parameters of adults”, Journal of sleep research, 2010, pp. 238-247.
Doris Moser, Peter Anderer, Georg Gruber, Georg Dorffner, “Sleep classification according to AASM and Rechtschaffen and Kales”, Sleep, March 2009, 32. 2. 139.
Silvia Miano, Maria Chaira Paolino, Rosa Castaldo, Maria Piavilla”, Visual Scoring of sleep: A comparison between R and K criteria and AASM criteria”, Clinical Neurophysiology, 2010, pp. 39-42.
Yanfenz H., Jacek M. Z., W. Karwowski, William S. M., “A hybrid Neuro-Fuzzy approach for spinal force evaluation”, Proceedings of first Internal conference on Advances in Natural Computation, 2005, pp. 1216-1225.
Abdulhamit Subasi, Mustafa Yimaz, Hasan Riza Ozcalik, “Classification of EMG Signals using Wavelet Neural Network”, Journal of Neuroscience Methods, 2006, pp. 360-367.
Lin Wang, T. S. Buchanan, “Prediction of joint moments using NN model of muscle activation from EMG signals”, IEEE Transaction on Neural system and Rehabilitation Engineering, 2002, pp. 30-37.