A two-step automatic sleep stage classification method with dubious range detection

Sousa, Teresa; Cruz, Aniana; Khalighi, Sirvan; Pires, Gabriel; Nunes, Urbano

doi:10.1016/j.compbiomed.2015.01.017

Cited by 48 publications

(25 citation statements)

References 31 publications

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“…Various approaches have been proposed, from simple rule-based decision trees (Liang et al, 2012) to supervised classifiers (e.g., support vector machines or neural networks; Pardey et al, 1996; Sousa et al, 2015), and finally unsupervised classifiers such as Hidden Markov Models (HMMs; Flexer et al, 2002, 2005; Pan et al, 2012). Because traditional PSG uses several EEG channels along with electromyogram (EMG) and electrooculogram (EOG) information, many automatic sleep scoring algorithms use much of the same data associated with PSG (Pan et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity

Onton

Kang

Coleman

2016

Front. Hum. Neurosci.

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Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1–1 Hz or 1–3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.

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Section: Introductionmentioning

confidence: 99%

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity

Onton

Kang

Coleman

2016

Front. Hum. Neurosci.

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“…A typical algorithm involves using some kind of signal processing to extract representative features followed by a classifier to assign one of the sleep stages based on these features. Some of the algorithms proposed recently include the use of support vector machines [4]- [6], hidden Markov models [7] and frequency coupling with linear discriminant analysis classification [8]. Other methods have also used artificial neural networks [2], [9], [10] and decision trees [11], [12] for classification with a variety of time, frequency, entropy [13], [14] and wavelet [10] sleep data either recorded by the researchers or using signals available from public sleep databases.…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep staging using state machine-controlled decision trees

Imtiaz¹,

Rodriguez‐Villegas²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.

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“…Hence, to ensure that the algorithm is pragmatic and feasible in the real-world, we performed tests using various types of classifiers and determined the best one that covered the widest variety of data. Moreover, based on our survey, a few ASSC schemes have considered testing different classifiers and achieved performance improvements [9,10,12,32,41,50]. The chosen classifiers applied to the proposed method in this study include SVM, NN, KNN, NB, SVM and DT, which are among the most commonly used ones in ASSC, based on our survey.…”

Section: Resultsmentioning

confidence: 99%

“…In biomedical signal processing, it is crucial to determine the noise, artifacts and any trends present in the raw signals so that their influence in the feature extraction stage can be minimized [29,30,46,49,50]. EEG recordings have a wide variety of artifacts, some having a technical origin and others having a physiological origin mixed together with the brain signal [41,45,57,108,129].…”

Section: Signal Pre-processingmentioning

confidence: 99%

“…The purpose of this component is to reduce the number of features and to generate low-dimensional features that are derived from the input features. A wide range of machine learning-based methods such as Linear Discriminant Analysis (LDA) [50,56], Artificial Neural Networks (ANN) [57,58], Support Vector Machine (SVM) [4,34,53], K-Nearest Neighbor (KNN) [32,37] and Decision Trees (DT) [15,17,43] have been proposed for classification problems, which have also been widely used for sleep stage classification.…”

Section: Background and Motivationmentioning

confidence: 99%

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

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

260

120

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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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A two-step automatic sleep stage classification method with dubious range detection

Abstract: This approach provides reliable sleep staging results for non-dubious epochs.

Cited by 48 publications

References 31 publications

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity

Automatic sleep staging using state machine-controlled decision trees

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

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