Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging

Charbonnier, Sylvie; Zoubek, Lukáš; Lesecq, Suzanne; Chapotot, Florian

doi:10.1016/j.compbiomed.2011.04.001

Cited by 72 publications

(46 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. It is generally difficult to compare the performance of algorithms that have been evaluated using different 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)

View full text Add to dashboard Cite

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.

show abstract

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)

View full text Add to dashboard Cite

show abstract

“…In order to determine the direction of the anomaly, we first classify all the variables as the larger the better type and the smaller the better type category [10][11][12] . If the jth is abnormal and MD is above the user-defined threshold, good anomaly can be identified based on the following rules.…”

Section: Mahalanobis-taguchi-gram-schmidt Methodsmentioning

confidence: 99%

“…The experimental data used in this study comes from the Sleep-EDF database, which is part of the Physionet database [12] . This article uses two sets of data records from sc4001e0 and st7022j0.…”

Section: Experimental Datamentioning

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

“…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]. Therefore, the use of pre-processing methods before feature extraction is useful for EEG signal analysis without losing relevant information [44,73]. Many of the sleep stage detection schemes reported in this study employ pre-processing techniques before extracting the features from the signal; 50% use frequency-selective-filtering and another 15% employ filtering, such as Discrete Wavelet Transform (DWT).…”

Section: Signal Pre-processingmentioning

confidence: 99%

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

Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging

Cited by 72 publications

References 19 publications

Automatic sleep staging using state machine-controlled decision trees

Automatic sleep staging using state machine-controlled decision trees

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

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

Contact Info

Product

Resources

About