Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

doi:10.1016/j.jneumeth.2015.01.022

Cited by 276 publications

(182 citation statements)

References 76 publications

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“…For this reason, numerous attempts have been made to automate this process (See Penzel et al (Penzel et al 2007) and Lajnef et al (Lajnef et al 2015a) for a listing of various automatic systems proposed). Of the several dozen systems tried so far three have shown enough promise to be used in clinical studies and are commercially available (Malhotra et al 2013;Younes et al 2015b;Pittman et al 2004;Anderer et al 2005;Punjabi et al 2015).…”

Section: Comparison Between Manual and Automatic Scoring Of Sleep Accmentioning

confidence: 99%

The case for using digital EEG analysis in clinical sleep medicine

Younes

2017

Sleep Science Practice

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Evaluation of sleep in clinical polysomnograms continues to rely almost exclusively on visual scoring that implements rules proposed by Rechtschaffen and Kales nearly 50 years ago. Apart from its cost and timeconsuming nature, visual scoring has limitations including: A) Sleep depth, which is a continuous variable, is treated as if it changes in a stepwise fashion from light (stage 1), to intermediate (stage 2) to deep (stage 3). B) Even with this limited scale, there is considerable inter-scorer variability, particularly in scoring stages 1 and 3 of non-REM sleep, thereby adding uncertainty to %time spent in these stages as a reliable metric for evaluating sleep depth. C) Limitation in scoring some of EEG features, including 1) arousal intensity, 2) extent of Alpha intrusion and 3) frequency, and characteristics of sleep spindles and K complexes. Digital analysis can solve these problems but producing a reliable system has been a challenge. In this review I begin with recent advances in digital scoring of sleep according to the Rechtschaffen and Kales rules and conclude that this technology has progressed enough to make it possible to obtain reliable, reproducible scoring, comparable in accuracy to scoring by highly experienced technologists, with minimal editing. This is followed by description of several new metrics that can be obtained if digital scoring systems were to be used routinely in clinical studies. The scientific evidence supporting the potential of these metrics to positively impact sleep medicine practice and the wide range of such metrics in patients studied in the sleep laboratory are highlighted.

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Section: Comparison Between Manual and Automatic Scoring Of Sleep Accmentioning

confidence: 99%

The case for using digital EEG analysis in clinical sleep medicine

Younes

2017

Sleep Science Practice

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“…Liang et al (2016) pointed out the importance of health competency for sleep quality because it was necessary to understand the adjustment of pressure, the enhancement of individual psychology, and good living habits being able to prevent diseases, and health competency required medical education. In this case, good health competency required medical education (Lajnef et al, 2015). Based on above inference, the hypothesis is proposed in this study.…”

Section: Quality Of Lifementioning

confidence: 92%

“…Sleep is a normal physiological behavior and the process humans being unconscious of external environment but could awake and recover by environmental stimuli (Lajnef et al, 2015;de Arriba Pérez et al, 2016;Wysocki, Balcerzak, Prus, Niemczyk, Lachowska, 2016;Moon, Kong, Oh, Kim, 2018). Being a primary physiological function to maintain human life, sleep is the fundamental element of life like eating, breathing, and drinking water.…”

Section: Sleep Qualitymentioning

confidence: 99%

To Explore the Impact of Medical Education on Sleep Quality toward Quality of Life

Luo

Xü

2018

EURASIA J MATH SCI T

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Sleep is the basic physiological needs of human beings, while sleep disorder commonly occur in the public. Bad sleep quality would result in bad work efficiency and low quality of life. Insomnia problem is regarded as a disease seriously affecting quality of life in current industrial and commercial societies. Medical education allows students presenting further cognition of self-health concepts and people understanding that proper physical activity could enhance citizens' physical fitness, reduce the occurrence of chronic diseases, and largely help the expenditure of social medical expenses. Apparently, medical education is primary for human health. With experimental design, total 200 students in four classes of the University in Shanghai, as the research object, are proceeded 16-week (3hrs per week for total 48 hours) experimental teaching. The research results reveal the significant correlation between 1.medical education and sleep quality, 2.sleep quality and quality of life, and 3.medical education and quality of life. According to the results and findings, practical suggestions are proposed in this study, expecting to reinforce relevant medical education and draft lifestyles to enhance the sleep quality and quality of life.

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“…The splitting attribute n X or predictor attribute is associated with each internal node. If n X denotes a numerical attribute, then n q which is the splitting predicate holds the form n J represents the splitting subset at the node n [18]. A classification tree is typically built using training data in two phases namely growing phase and pruning phase.…”

Section: Decision Trees For Independent Observationsmentioning

confidence: 99%

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Biju

Prashanth²

2017

Journal of Applied Computer Science Methods

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This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

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Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines

Cited by 276 publications

References 76 publications

The case for using digital EEG analysis in clinical sleep medicine

The case for using digital EEG analysis in clinical sleep medicine

To Explore the Impact of Medical Education on Sleep Quality toward Quality of Life

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

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