Literature DB >> 8858492

Past and future of computer-assisted sleep analysis and drowsiness assessment.

J Hasan1.   

Abstract

The development of computerized sleep analysis has been very much technology-driven by both mathematical tools and available hardware but, additionally and unfortunately, by the almost-30-year-old standard used for manual sleep stage scoring of paper recordings. There are no technical restrictions in terms of computing power, storage space, and costs anymore. However, the standards of visual sleep stage scoring have proven insufficient and ambiguous, and their utilization evidently provides misleading and erroneous information. The low temporal resolution provided by the one-page epoch, the crude division of the sleep processes into a few discrete stages, and the total ignorance of spatial information are the major drawbacks. It is meaningless to try to improve the computerised systems if the algorithms are based on erroneous concepts. Instead, the focus should be changed to studies dealing with the identification and modelling of true biological sleep-related processes. This work cannot be performed without the successful application of computerized methods, some of which have been used in related fields but have not yet been applied to sleep studies. It is extremely important that basic findings are confirmed with a wide variety of methods in several laboratories. The use of predetermined, fixed criteria for methods, waveforms, and states too early is scientifically erroneous and hazardous. Instead standards should describe the minimum requirements for the recording and analysis of the signals in terms of sampling rate, dynamic range, linearity, and documentation of the methods used. With the development of better technology, these standards ought to be constantly reevaluated and modified. The development toward more open commercial digital systems, including standardized programming methods and data formats, would have great positive impact to the field. These trends have long been established in many other fields of industry.

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Mesh:

Year:  1996        PMID: 8858492     DOI: 10.1097/00004691-199607000-00004

Source DB:  PubMed          Journal:  J Clin Neurophysiol        ISSN: 0736-0258            Impact factor:   2.177


  8 in total

1.  Autoassociative MLP in sleep spindle detection.

Authors:  E Huupponen; A Värri; S L Himanen; J Hasan; M Lehtokangas; J Saarinen
Journal:  J Med Syst       Date:  2000-06       Impact factor: 4.460

2.  Automatic analysis of electro-encephalogram sleep spindle frequency throughout the night.

Authors:  E Huupponen; S L Himanen; J Hasan; A Värri
Journal:  Med Biol Eng Comput       Date:  2003-11       Impact factor: 2.602

3.  Identification of deep sleep and awake with computational EEG measures.

Authors:  Eero Huupponen; Antti Kulkas; Antti Saastamoinen; Mirja Tenhunen; Sari-Leena Himanen
Journal:  J Med Syst       Date:  2010-01-06       Impact factor: 4.460

4.  Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states.

Authors:  Rakesh Kumar Sinha
Journal:  J Med Syst       Date:  2008-08       Impact factor: 4.460

Review 5.  A new mHealth communication framework for use in wearable WBANs and mobile technologies.

Authors:  Sana Tmar-Ben Hamida; Elyes Ben Hamida; Beena Ahmed
Journal:  Sensors (Basel)       Date:  2015-02-03       Impact factor: 3.576

6.  Anteroposterior difference in EEG sleep depth measure is reduced in apnea patients.

Authors:  Eero Huupponen; Antti Saastamoinen; Atte Joutsen; Jussi Virkkala; Jarmo Alametsä; Joel Hasan; Alpo Värri; Sari-Leena Himanen
Journal:  J Med Syst       Date:  2005-10       Impact factor: 4.460

7.  Integrating the Divided Nasal Cannula Into Routine Polysomnography to Assess Nasal Cycle: Feasibility and Effect on Outcomes.

Authors:  Marcelo Scapuccin; Logan Schneider; Nur Rashid; Soroush Zaghi; Talita Rosa; Yung-An Tsou; Stanley Liu; Paulo Lazarini; Robson Capasso; Chad Ruoff
Journal:  J Clin Sleep Med       Date:  2018-04-15       Impact factor: 4.062

8.  Automatic analysis of single-channel sleep EEG: validation in healthy individuals.

Authors:  Christian Berthomier; Xavier Drouot; Maria Herman-Stoïca; Pierre Berthomier; Jacques Prado; Djibril Bokar-Thire; Odile Benoit; Jérémie Mattout; Marie-Pia d'Ortho
Journal:  Sleep       Date:  2007-11       Impact factor: 5.849

  8 in total

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