Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

doi:10.1016/j.bspc.2015.09.002

Cited by 250 publications

(85 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…The characteristics of such features are obtained directly from the EEG epoch X(n) to measure certain tasks, such as central tendency, degree of dispersion, asymmetry, data peaks, troughs and flatness respectively [45]. Examples of statistical moments are shown in previous studies [12,47].…”

Section: Standard Statisticsmentioning

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 a reversible state in which the eyes are closed and several nervous system centers are inactive. Hence, sleep renders the individual either partially or completely unconscious and makes the brain a less complicated network [10][11][12][13]. Humans spend around one-third of their lives sleeping and conditions such as insomnia and Obstructive Sleep Apnea (OSA) are frequent and can severely affect physical health [14][15][16][17].…”

Section: Background and Motivationmentioning

confidence: 99%

See 3 more Smart Citations

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

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

284

124

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Standard Statisticsmentioning

confidence: 99%

Section: Input Eeg Signalmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

See 2 more Smart Citations

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

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

284

124

View full text Add to dashboard Cite

show abstract

“…Therefore, EEG signals have been used to study brain activity of different sleep stages [1] . The traditional classification of sleep stages is the classification criteria developed by experts according to Rechtschaffen and Kales (R & K) [2] . This study used the six-state sleep stages of the R & K standard: Awake (Awa), Stage 1 (S1), Stage 2 (S2), Stage 3 (S3), Stage 4 (S4) and REM [3] .…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection for sleep EEG signal with Mahalanobis-Taguchi-Gram-Schmidt method

Peng¹,

Zheng²,

Liu³

et al. 2018

Proceedings of the 2018 7th International Conference on Energy, Environment and Sustainable Development (ICEESD 2018)

View full text Add to dashboard Cite

Abstract：Considering the tedious steps, the poor accuracy and over-subjectivity of human sleep quality judgment artificially, this paper presents an automatic detection algorithm of sleep quality based on Mahalanobis-Taguchi system method. Based on the modeling and analysis of the human brain dual channel EEG signals, the normalized vector of each channel is obtained under different sleep stages. At the same time, the linear independent vector group is subjected to Gram-Schmidt orthogonalization, and the mean value of the signal-to-noise ratio of each sleep stage is calculated by using the Mahalanobis-Taguchi-Gram-Schmidt method. Through analyzing the waveform of the mean signal-to-noise ratio on different sleep stages, the normal and the abnormal sleep quality can be identified.

show abstract

Machine Learning Techniques for IoT Data Analytics

Afshan

Rout

2021

Big Data Analytics for Internet of Things

View full text Add to dashboard Cite

Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating

Cited by 250 publications

References 35 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

Anomaly detection for sleep EEG signal with Mahalanobis-Taguchi-Gram-Schmidt method

Machine Learning Techniques for IoT Data Analytics

Contact Info

Product

Resources

About