Signal processing techniques applied to human sleep EEG signals—A review

Motamedi-Fakhr, Shayan; Moshrefi-Torbati, M.; Hill, Martyn; Hill, Catherine M.; White, P.R.

doi:10.1016/j.bspc.2013.12.003

Cited by 205 publications

(120 citation statements)

References 105 publications

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“…The main idea of artefact identification is to determine what frequencies dominate in the recorded signal. If the signal frequencies deviate significantly from the frequency of the EEG signal, the classifier selects this fragment as the artefact [6,12,13].…”

Section: Methods Of Artefact Correctionmentioning

confidence: 99%

Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Kaczorowska

2017

ITM Web Conf.

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Abstract. The present paper covers the review of artefacts of EEG recording, as well as methods to eliminate them, i.e. PCA and ICA. The reviewed artefacts were of various origins: biological (muscle, eye blinking, eyeball moving), as well as technological artefacts caused mainly by the power network with the frequency of 50 Hz. The research has been conducted on a group consisting of 8 students. The research was carried out with the use of two defined models of electroencephalographs: stationary and mobile, then results have been compared. The comparison of two amplifiers combined with various artefact elimination methods has been presented. Presented research has shown that the record performed by the mobile amplifier contains the artefacts of significantly lower amplitude.

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Section: Methods Of Artefact Correctionmentioning

confidence: 99%

Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Kaczorowska

2017

ITM Web Conf.

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“…Selective digital filters (low-pass, high-pass, band-pass and band-stop filters) have been widely used in artifact detection and removal [129]. These filters are powerful and significant tools to minimize artifacts and to eliminate selected EEG signals from unwanted frequencies [128].…”

Section: Signal Pre-processingmentioning

confidence: 99%

“…The statistical feature method is one of the most commonly used time-domain features for discriminating different input EEG classes [129]. The statistical moments are well recognized for their ability to interpret the underlying statistics of the data [7].…”

Section: New Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

260

124

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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

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“…In this study, linear discriminative classifier realizes simple classification using only covariance matrices. Obtained model forms a multivariate normal density to each group derived from training set and estimates testing samples' labels with calculated covariance with estimations [31]. Basic linear classification is tested in order to demonstrate effects of a simple linear model on the sleep stage classification besides complex methods.…”

Section: Linear Discriminative Classifier (Ldc)mentioning

confidence: 99%

Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals

İlhan

Bilgin

2017

ijisae

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Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for more robust and efficient diagnosis systems. Several traditional machine-learning and pattern recognition algorithms are deployed on the modern computer aided diagnosis systems. However, current results are not as satisfactory as expected. In the last two decade, a new concept has emerged with 'ensemble learning' title. It has attracted the attention of many researchers from various disciplines. In this study, several ensemble-learning methods are utilized and inspected on EEG signals for sleep-stage classification. Conventional machine-learning methods are also performed in same testing phase to report comparative results. Additionally, methods are evaluated in two different scenarios; subject specific and independent. Study proves that combination of DTs and SVMs in bagging theorem surpasses all of the conventional methods used in the experiments. Moreover, test trials reveal that both conventional and ensemble models need to be improved for subject independent scenario which is more essential case in the development of patient independent computer based diagnosis systems.

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Signal processing techniques applied to human sleep EEG signals—A review

Cited by 205 publications

References 105 publications

Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals

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