Automatic sleep scoring using statistical features in the EMD domain and ensemble methods

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

doi:10.1016/j.bbe.2015.11.001

Cited by 202 publications

(82 citation statements)

References 26 publications

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“…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]. In this study, two new statistical features are proposed and derived by applying an assumption that depends on a utilized epoch.…”

Section: New Feature Extractionmentioning

confidence: 99%

“…The dataset used in this study is publicly available from the Sleep-EDF database (expanded) on the Physionet website (https://physionet.org/physiobank/database/sleep-edfx/) and has been widely used in the literature [2,7,11,12,16,18,24,33,36,38,48,49,57,87,91,105,106,108,109,114]. The database is a collection of 61 PSGs obtained from 1987-2002 including the older Sleep EDF database recordings prior to 1991.…”

Section: Input Eeg Signalmentioning

confidence: 99%

“…Sleep is the primary function of the brain and plays an essential role in an individual's performance, learning ability and physical movement [1][2][3][4][5][6][7][8][9]. Sleep is a reversible state in which the eyes are closed and several nervous system centers are inactive.…”

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

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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Section: New Feature Extractionmentioning

confidence: 99%

Section: Input Eeg Signalmentioning

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

124

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“…Recently EMD has been used for analysis and classification of physiological signal [12][13][14]. IMFs generated by EMD process on healthy, myopathy and neuropathy EMG signal are shown in Fig.…”

Section: B Empirical Mode Decompositionmentioning

confidence: 99%

Discrimination of Myopathy, Neuropathy and Healthy EMG Signals

Singh¹,

Agrawal²,

Gupta³

2017

International Journal of Advanced Research in Computer and Comm

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Abstract:In this paper, method for discrimination of EMG signals is proposed. EMG signal is decomposed into set of narrow band signal using empirical mode decomposition (EMD) process known as intrinsic mode functions (IMFs). The features namely mean, standard deviation, variance and Entropy of the intrinsic mode functions (IMFs) generated by EMD process is used to discrimination of myopathy, neuropathy, and healthyEMG signals. The statistical features of IMFs have provided better discrimination performance.

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“…When solving classification problems, statistical moments based features are commonly used. The authors in [23] used some statistical central moments based features such as the mean, variance, skewness, and kurtosis in order to provide an automatic sleep scoring method based on the use of single channel electroencephalograms. The research in [24] made use of statistical moments to extract features for a Multilayer Neural Network for the prediction of certain membrane proteins.…”

Section: Introductionmentioning

confidence: 99%

Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

Muñoz-Organero

Blázquez

2017

Journal of Advanced Transportation

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The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a -NN approach is used for assigning a class to each unlabelled element.

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Automatic sleep scoring using statistical features in the EMD domain and ensemble methods

Cited by 202 publications

References 26 publications

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

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

Discrimination of Myopathy, Neuropathy and Healthy EMG Signals

Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

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