An intelligent system for diagnosing sleep stages using wavelet coefficients

Vatankhah, Maryam; Akbarzadeh-T, Mohammad-R.; Moghimi, Ali

doi:10.1109/ijcnn.2010.5596732

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…The mean standard SE, calculated using the PSG data of 30 subjects, was 85.11 ± 6.48% and the mean estimated SE using the proposed modeling was 73.41 ± 8.18%. The accuracy in predicting sleep efficiency (%) could be calculated by Equations ( 1) and (2). The mean accuracy for estimating sleep efficiency was 86.19 ± 6.07 %.…”

Section: Calculation For Sleep E Ciencymentioning

confidence: 99%

Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy

Kim

Kim²,

2023

Front. Public Health

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IntroductionSleep is a fundamental and essential physiological process for recovering physiological function. Sleep disturbance or deprivation has been known to be a causative factor of various physiological and psychological disorders. Therefore, sleep evaluation is vital for diagnosing or monitoring those disorders. Although PSG (polysomnography) has been the gold standard for assessing sleep quality and classifying sleep stages, PSG has various limitations for common uses. In substitution for PSG, there has been vigorous research using actigraphy.MethodsFor classifying sleep stages automatically, we propose machine learning models with HRV (heart rate variability)-related features and acceleration features, which were processed from the actigraphy (Maxim band) data. Those classification results were transformed into a binary classification for estimating sleep efficiency. With 30 subjects, we conducted PSG, and they slept overnight with wrist-type actigraphy. We assessed the performance of four proposed machine learning models.ResultsWith HRV-related and raw features of actigraphy, Cohen's kappa was 0.974 (p < 0.001) for classifying sleep stages into five stages: wake (W), REM (Rapid Eye Movement) (R), Sleep N1 (Non-Rapid Eye Movement Stage 1, S1), Sleep N2 (Non-Rapid Eye Movement Stage 2, S2), Sleep N3 (Non-Rapid Eye Movement Stage 3, S3). In addition, our machine learning model for the estimation of sleep efficiency showed an accuracy of 0.86.DiscussionOur model demonstrated that automated sleep classification results could perfectly match the PSG results. Since models with acceleration features showed modest performance in differentiating some sleep stages, further research on acceleration features must be done. In addition, the sleep efficiency model demonstrated modest results. However, an investigation into the effects of HRV-derived and acceleration features is required.

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Section: Calculation For Sleep E Ciencymentioning

confidence: 99%

Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy

Kim

Kim²,

2023

Front. Public Health

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“…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%

“…WT uses functions that are localized in both time and frequency scales [8,33,129]. Because of its flexible way to represent the time-frequency domain of a signal, WT is suitable for non-stationary signal analysis [126].…”

Section: Wavelet Transformsmentioning

confidence: 99%

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

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

291

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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“…SVMs are binary classifiers, in which the objective is to locate a separating hyper-plane in the space between the two classes by mapping the data into a higher dimensional space [22]. For multi-class classification SVM, a "one-against-all" approach combined with linear kernel function was used in this work.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages

Aboalayon

Almuhammadi

Faezipour

2015

2015 Long Island Systems, Applications and Technology

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In recent years, the estimation of human sleep disorders from Electroencephalogram (EEG) signals have played an important role in developing automatic detection of sleep stages. A few methods exist in the market presently towards this aim. However, sleep physicians may not have full assurance and consideration in such methods due to concerns associated with systems accuracy, sensitivity and specificity. This paper presents a novel and efficient technique that can be implemented in a microcontroller device to identify sleep stages in an effort to assist physicians in the diagnosis and treatment of related sleep disorders by enhancing the accuracy of the developed algorithm using a single channel of EEG signals. First, the dataset of EEG signal is filtered and decomposed into delta, theta, alpha, beta and gamma subbands using Butterworth band-pass filters. Second, a set of sample statistical discriminating features are derived from each frequency band. Finally, sleep stages consisting of Wakefulness, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM) are classified using several supervised machine learning classifiers including multi-class Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), K-Nearest Neighbors (KNN) and Naive Bayes (NB). This paper combines REM with Stage 1 NREM due to data similarities. Performance is then compared based on single channel EEG signals that were obtained from 20 healthy subjects. The results show that the proposed technique using DT classifier efficiently achieves high accuracy of 97.30% in differentiating sleeps stages. Also, a comparison of our method with some recent available works in the literature reiterates the high classification accuracy performance.

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An intelligent system for diagnosing sleep stages using wavelet coefficients

Cited by 9 publications

References 14 publications

Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy

Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy

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

A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages

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