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Proceedings ArticleDOI

A study on sleep EEG Using sample entropy and power spectrum analysis

23 Sep 2011-pp 1-4
TL;DR: Sample entropy, and the power spectrum of the harmonic parameters using infinite impulse response filters and wavelet transform were used to extract features from the data taken from Physionet database, and a prototype for the sleep stage classification system was implemented.
Abstract: Research on the automation of sleep stage classification, particularly single channel EEG, has been a challenge for many years. The research aims to look into the analysis and evaluation of feature extraction techniques and classification methods that are important to properly classify sleep stages with limited channels. Sample entropy, and the power spectrum of the harmonic parameters using infinite impulse response filters and wavelet transform were used to extract features from the data taken from Physionet database. A total of 13 features were initially extracted and used for the training and testing of the sleep stage classification system. Analysis of the training data showed a distinct combination patterns between the sample entropy and harmonic parameters with a change in the sleep stage. In addition, a prototype for the sleep stage classification system was implemented. Support Vector Machine (SVM) was utilized for the classification system. While the training data were extracted from several database. Further refinement of the data and the program could be useful for a test the sleep stage classification on other database or data.
Citations
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Journal ArticleDOI
23 Aug 2016-Entropy
TL;DR: 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 is presented.
Abstract: 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.

243 citations

Journal ArticleDOI
TL;DR: The proposed classification method has the potential for identifying the epileptogenic zones, which is an important step prior to resective surgery usually performed on patients with low responsiveness to anti-epileptic medications.

185 citations

Journal ArticleDOI
TL;DR: Comparative analysis of the classification performance under different sleep stage patterns with prior works has been carried out to show the significant improvements over state-of-the-art solutions, and suggest that the proposed scheme is suitable for long-term sleep monitoring.
Abstract: Sleep stage estimation is crucial to the evaluation of sleep quality and is a proven biometric in diagnosing cardiovascular diseases. In this paper, we design a continuous wave (CW) Doppler radar to accurately measure sleep-related signals, including respiration, heartbeat, and body movement. Body movement index, respiration per minute (RPM), variance of RPM, amplitude difference accumulation (ADA) of respiration, rapid eye movement parameter, sample entropy, heartbeat per minute (HPM), variance of HPM, ADA of heartbeat, deep parameter, and time feature have been extracted and fed into different machine learning classifiers. A total of 11 all night polysomnography recordings from 13 healthy examinees were used to validate the proposed CW Doppler radar system and the ability to detect sleep stage information from it. Comparative studies and statistical results have shown that the subspace K-nearest neighbor algorithm outperforms the other classifiers with the highest accuracy of up to 86.6%. With the Relief F algorithm, features have been ranked, and the selected feature subsets have been preliminary tested to identify the optimal feature subset. Meanwhile, comparative analysis of our classification performance under different sleep stage patterns with prior works has been carried out to show the significant improvements over state-of-the-art solutions. These results suggest that the proposed scheme is suitable for long-term sleep monitoring.

67 citations


Cites methods from "A study on sleep EEG Using sample e..."

  • ...6) Sample Entropy: The sample entropy [36] is usually used to measure the complexity and regularity of a signal....

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Proceedings ArticleDOI
02 May 2014
TL;DR: An efficient technique to identify the sleep stages from a publicly available EEG signal dataset by using a feasible set of features, easily implementable filters in any microcontroller device, and an efficient classification method is proposed.
Abstract: Currently, sleep disorders are considered as one of the major human life issues. There are several stable physiological stages that the human brain goes through during sleep. Nowadays, many biomedical signals such as EEG, ECG, EMG, and EOG offer useful details for clinical setups that are used in identifying sleep disorders. In this work, we propose an efficient technique that could be implemented in hardware to differentiate sleep stages which will assist physicians in the diagnosis and treatment of related sleep disorders. This study depends on different EEG datasets from PhysioNet using the Sleep-EDF [Expanded] Database that were acquired and described by scientists for the analysis and diagnosis of sleep stages. Generally, the EEG signal is decomposed into five bands: delta, theta, alpha, beta, and gamma to define the change in brain state. In this work, Butterworth band-pass filters are designed to filter and decompose EEG into the above frequency sub-bands. In addition, various discriminating features including energy, standard deviation and entropy are computed and extracted from each δ, □, α, β and γ sub-band. Furthermore, the extracted features are then fed to a supervised learning classifier; support vector machine (SVM) to be able to recognize the sleep stages state and identify if the acquired signal is corresponding to wake or stage 1 of sleep, according to the purpose of this research. The key novelty of this work is to identify the sleep stages from a publicly available EEG signal dataset by using a feasible set of features, easily implementable filters in any microcontroller device, and an efficient classification method. Therefore, physicians can track these sleep stages to identify certain patterns such as detecting fatigue, drowsiness, and/or various sleep disorders such as sleep apnea. The experimental results on a variety of subjects verify 92.5% of classification accuracy of the proposed work.

59 citations


Cites methods from "A study on sleep EEG Using sample e..."

  • ...Whereas, classification of sleep stages of NREM and REM is mainly done by Rechtschaffen and Kales (R&K) rules to determine sleep characteristics [7]....

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  • ...For the sleep stage classification, a Support Vector Machine (SVM) is used as the classification tool [7]....

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Proceedings ArticleDOI
01 May 2015
TL;DR: An efficient methodology that could be implemented in hardware to differentiate OSA patients from normal controls, based on the Electroencephalogram (EEG) signals is introduced.
Abstract: Nowadays, analyzing EEG signals has made it easy to diagnose many sleep-related breathing disorders such as Obstructive Sleep Apnea (OSA), which is a potentially serious sleep disorder that affects the quality of human life This paper introduces an efficient methodology that could be implemented in hardware to differentiate OSA patients from normal controls, based on the Electroencephalogram (EEG) signals For this purpose, first, the EEG recorded datasets that were obtained from the Phsyionet website are filtered and decomposed into delta, theta alpha, beta and gamma sub-bands using Infinite Impulse Response (IIR) Butterworth band-pass filters Second, descriptive features such as energy and variance are extracted from each frequency band that are used as input parameters for classification Finally, several machine learning algorithms including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA) and Naive Bayes (NB) are employed in order to identify if the OSA exists or not, according to the objective of this study The results that are obtained from these classifiers are then compared in terms of accuracy, sensitivity and specificity The experimental results show that the SVM attained the best classification accuracy of 9714% as compared to the others

57 citations

References
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Journal ArticleDOI
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...

11,407 citations

Journal ArticleDOI
TL;DR: Techniques of recording, scoring, and doubtful records are carefully considered, and Recommendations for abbreviations, types of pictorial representation, order of polygraphic tracings are suggested.
Abstract: With the vast research interest in sleep and dreams that has developed in the past 15 years, there is increasing evidence of noncomparibility of scoring of nocturnal electroencephalograph-electroculograph records from different laboratories. In 1967 a special session on scoring criteria was held at the seventh annual meeting of the Association for the Psychophysiological Study of Sleep. Under the auspices of the UCLA Brain Information, an ad hoc committee composed of some of the most active current researchers was formed in 1967 to develop a terminology and scoring system for universal use. It is the results of the labors of this group that is now published under the imprimatur of the National Institutes of Health. The presentation is beautifully clear. Techniques of recording, scoring, and doubtful records are carefully considered. Recommendations for abbreviations, types of pictorial representation, order of polygraphic tracings are suggested.

8,001 citations


"A study on sleep EEG Using sample e..." refers methods in this paper

  • ...NREM sleep was divided into four stages namely: transitional sleep (stage 1), light sleep (stage 2), slow wave sleep (stages 3 and 4) and paradoxical sleep also known as the REM sleep [4-5]....

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Journal ArticleDOI
TL;DR: Support vector machines are becoming popular in a wide variety of biological applications, but how do they work and what are their most promising applications in the life sciences?
Abstract: Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?

3,801 citations


"A study on sleep EEG Using sample e..." refers background in this paper

  • ...It also makes use of soft margin to specify trade-off between hyperplane variations and the size of the margin [11]....

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Journal ArticleDOI
01 Jan 2009
TL;DR: The results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG and demonstrate considerable potential in applying SVM in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.
Abstract: Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS- ) and subjects with OSAS (OSAS+), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.

327 citations


"A study on sleep EEG Using sample e..." refers background in this paper

  • ...Currently there is no analytical or empirical study that has made a conclusion on which a kernel function is the better than other kernels, therefore SVM performance varies with the choice of kernel and the task on hand [12]....

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Further refinement of the data and the program could be useful for a test the sleep stage classification on other database or data.