Using off-the-shelf lossy compression for wireless home sleep staging

Lan, Kun; Chang, Da; Kuo, Chih‐En; Wei, Ming; Li, Yu Hung; Shaw, Fu-Zen; Liang, Sheng

doi:10.1016/j.jneumeth.2015.03.013

Cited by 21 publications

(15 citation statements)

References 31 publications

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“…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]. According to a survey, 50-70 million people suffer from sleep disorders in the United States [18,19].…”

Section: Background and Motivationmentioning

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%

“…Although some studies on ASSC have considered using a single EEG channel, some epoch ambiguity problems remain unsolved. For example, the characteristics of the EEG signal in S1 and REM stages are very similar [17,42,59], which has resulted in a low classification performance.…”

Section: Background and Motivationmentioning

confidence: 99%

“…The performance for the automatically discriminated data achieved an optimal classification accuracy of up to 80%. Another study from [17] extracted many spectral features based on Fast Fourier Transform (FFT) of multichannel PSG data to classify the sleep stages using a rule-based DT classifier and achieved an accuracy of 84%.…”

Section: State Of the Artmentioning

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

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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: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: State Of the Artmentioning

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

284

124

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“…The main scope of this step is to reduce the dimension of the estimated features. A wide range of machine learning-based classification methods such as Linear Discriminant Analysis (LDA) (Sousa et al, 2015 ;Weiss et al, 2011), Artificial Neural Networks (ANN) (Liu et al, 2010 ;Dursun et al, 2012), Support Vector Machine (SVM) (Huang et al, 2013 ;Brignol et al, 2012 ;Yu et al, 2012 ;Lainef et al, 2015), K-Nearest Neighbor (KNN) (Kuo and Liang, 2011 ;Liu et al, 2010), Decision Trees (DT) (Schaltenbrand et al, 1996 ;Pan et al, 2012) and SVMs-DT (Lan et al, 2015) have been adopted for sleep stage classification.…”

Section: Introductionmentioning

confidence: 99%

A Novel, Fast and Efficient Single-Sensor Automatic Sleep-Stage Classification Based on Complementary Cross-Frequency Coupling Estimates

Dimitriadis

Salis

Linden

2017

Preprint

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ObjectiveLimitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels, have long been recognised. Manual staging is resource-intensive and time-consuming and considerable efforts have to be spent to ensure inter-rater reliability. There is thus great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC).MethodsIn this paper, we present a single EEG-sensor ASSC technique based on dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database using 20 healthy young adults with repeat recordings.ResultsWe achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively and high mean F1-score (92%, range 90–94%) when multi-class Bayes Naive classifier was applied.ConclusionsOur method outperformed the accuracy of previous studies on different datasets but also on the same database.SignificanceSingle-sensor ASSC makes the whole methodology appropriate for longitudinal monitoring using wearable EEG in real world and lab-oriented environments.

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