Automated Sleep Staging by a Hybrid System Comprising Neural Network and Fuzzy Rule-based Reasoning

Tian, Junrui; Liu, J.Q.

doi:10.1109/iembs.2005.1615368

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…Recent studies have adopted bioelectrical signals (i.e., EEG, ECG, EMG, and EOG signals), which allow subjects to operate at home in order to develop sleep stage scoring methods, while attempting to obtain results similar to those of experts involved in visual scoring (Park et al, 2000 ; Anderer et al, 2005 ; Tian and Liu, 2005 ; Berthomier et al, 2007 ; Doroshenkov et al, 2007 ; Virkkala et al, 2007 ; Wang et al, 2009 ; Güneş et al, 2010 ; Jo et al, 2010 ; Yιlmaz et al, 2010 ; Eiseman et al, 2011 ). The classification structure of most of sleep stage classifications consists of feature extraction and classification schemes.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

Huang

Lin

et al. 2014

Front. Neurosci.

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Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare.

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Section: Introductionmentioning

confidence: 99%

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

Huang

Lin

et al. 2014

Front. Neurosci.

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“…The SMO have been applied in automatic sleep stage detection [84], [85], and in the classification of patterns of k-complexes during sleep [86].…”

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%

A Systematic Review of Literature on Automated Sleep Scoring

Alsolai

Qureshi

Iqbal³

et al. 2022

IEEE Access

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Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

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“…3) SVM-OVA: It has ability to minimize both basic and empirical risk provoking better speculation for new data request. The essential favourable position of SVM is the settlement of non-linear classification issues and having no requirement of speed clasping for narrowing model [5,9]. To solve the inner product calculation, kernel function is used.…”

Section: Technology Used 1) Annmentioning

confidence: 99%

“…The back propagation (BP) algorithm is mostly utilized as a part of ANN learning strategy which has been used by numerous specialists as a part of the identification and characterization of sleep apnea/hypopnea occasions, recognition of sleep wake stages [6,7], recognition of REM sleep, sleep spindles [7], detection of sleep disordered breathing [8] and Classification of Obstructive sleep apnea [5] by achieving 88% accuracy. ANN has a couple focal points over knowledge based system in showing a reciprocal way to deal with rule based reasoning regarding learning representation that need quite a while to build such a framework from rule based methodology [9,25]. ANN has an extremely alluring property for automated recognition which does not need any intricate grouping principles or complex area learning [10].…”

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confidence: 99%

A Novel 3-Layer Soft Computing Integrated Technique ANFUS Based On Symptomatic Guidelines For Early Detection Of Sleep Disorders

Garg¹,

Bansal²

2016

IJET

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Sleep assumes an indispensable part in life of human being to be casual and stress free from consistent day errands. However, some time, sleep gets disturbed by unordinary exercises and practices, because of which numerous sleep disorders emerge like Sleep Apnea, Insomnia, Parasomnia and Snoring. The different applications have been created in artificial intelligence domain for the finding of gawky practices which begins the interference in sleep nearby the backing of further new sleep disorders. This paper is based on a novel 3-layer soft computing method ANFUS which has been formulated for an early detection of the above said sleep disorders. In this methodology, artificial neural network (ANN) and fuzzy C-means (FCM) are integrated with support vector machine-one versus all (SVM-OVA). At layer-1, ANN is used to discover the relationship between collected patient records and disability factors of sleep disorders and the outcome is passed to FCM at layer-2 to define the boundaries of all overlapped disability factors. The result provided by FCM is utilized by SVM-OVA present at layer-3 to classify the sleep disorders. The outcome demonstrates that ANFUS integrated techniques provide an early detection of sleep disorders to help the patients to upgrade themselves from the various bad effects arising due to disrupted sleep, also known as sleep disorders. Keyword-3-Layer, Early detection, Sleep Disorders, Soft Computing I. INTRODUCTION "THE BEST WAY TO ESCAPE FROM REALITY IS TO SLEEP, THE BEST WAY TO IMAGINE SOMETHING IS TO SLEEP, AND THE BEST WAY TO WAKE UP IS TO SLEEP" Dalai Lama i.e., One of the famous head monks of Gelug School says that "Sleep is the best meditation" that is also a demand of every individual to cover up them to start the regular activities. It is a state where a person has lost its total consideration against the outer environment [3]. It is a stage, where all muscles are in calm state along with other sensual movements. An individual moves through many phases/stages at the time of its sleeping hours such as Wake, Non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM). On the other hand, at some point this sleep is hindered because of some humiliating practices, mental conditions related with it, otherwise called sleep disorders that further chop down the nature of sleep including dozing hours. The greater part of the parents is new to the characteristic of sleep disorders exasperating a ton of youngsters at the worldwide level. Sometime parents are unable to discriminate the sleeping arrangements of their children which become the major cause of suffering children from many side effects. It is finished up by the Toronto Star that the quantity of kids having lower evaluations in study are 6 to 9 times more noteworthy than others which are experiencing some sort of problems related to sleep [4]. Many intelligent techniques and systems have been adopted by researchers to detect sleep and the sleeping patterns including sleep disorders linked with them which produces some kind of disturbances...

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Automated Sleep Staging by a Hybrid System Comprising Neural Network and Fuzzy Rule-based Reasoning

Cited by 14 publications

References 14 publications

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

A Systematic Review of Literature on Automated Sleep Scoring

A Novel 3-Layer Soft Computing Integrated Technique ANFUS Based On Symptomatic Guidelines For Early Detection Of Sleep Disorders

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