Automatic identification of Cyclic Alternating Pattern (CAP) sequences based on the Teager Energy Operator

Machado, Fátima; Sales, Francisco; Bento, Carlos; Dourado, António; Teixeira, César

doi:10.1109/embc.2015.7319617

Cited by 10 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 61% of the subjects are men (66 people), and 38% are women (42 people). Most of the studies conducted using the CAP sleep database are on cyclic phase detection [ 30 , 31 , 32 , 33 , 34 , 35 ]. There are many studies on sleep stage detection using other datasets but there is no study available in literature on sleep stage classification using the CAP sleep database.…”

Section: Materials Usedmentioning

confidence: 99%

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Sharma

Tiwari

Acharya

2021

IJERPH

View full text Add to dashboard Cite

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

show abstract

Section: Materials Usedmentioning

confidence: 99%

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Sharma

Tiwari

Acharya

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…• EEG band descriptors [25][26][27][28][29][30][31][32][33][34][35] : describes how much the amplitude of the activity, in the selected frequency band, differs from its background; • Differential variance [26,29,[32][33][34][35] : alteration of the variance between the current and the previous epoch; • Detrended fluctuation analysis [36] : characterizes the correlation structure of non-stationary time series; • Hjorth descriptors [26,29,[32][33][34][35] : the activity, mobility, and complexity parameters that are respectively estimated by the variance of the signal, the variance of the slopes that were normalized by the variance of the amplitude distribution and the ratio of the mobility from the first derivative of the signal to the mobility of the signal; • Power spectral density of the band [25,28,[37][38][39][40] : distribution of power into frequency components that compose the signal; • Moving average ratio [41] : activity index determined by the ratio of a short moving average to a long moving average; • Teager energy operator [25][26][27][28]38,40] : nonlinear metric that can be interpreted as an instantaneous measure of energy; • Lempel-Ziv Complexity [25,28,37] : ...…”

Section: Discussionmentioning

confidence: 99%

Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring

Mendonça

Mostafa

Morgado‐Dias

et al. 2020

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

“…Statistical based characteristics such as mode, kurtosis, skewness, and standard deviation [11], [9] Teager energy operator Non-linear estimation of the instantaneous energy [26], [36], [35], [34] Tsallis entropy Metric that is a one-parameter generalization of the Shannon entropy [11], [32], [9] Variance Measurement of the signal's spread [23], [26], [36], [35] Zero-crossing ratio Number of baseline crossings and can provide information about the dominant frequency [36], [35] The second approach employs a machine learning algorithm to learn the patterns from the features (or the EEG signal) and perform the classification. This methodology was used by Machado et al [35] [36], evaluating the MMSD, TEO, zero-crossing rate, Lempel-Ziv Complexity (LZC), discrete time short time Fourier transform, empirical mode decomposition, Shannon Entropy (SE), Fractal Dimension (FD), and variance.…”

Section: Statistical Featuresmentioning

confidence: 99%

On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes

Mendonça¹,

Mostafa²,

Morgado‐Dias³

et al. 2021

J. Neural Eng.

View full text Add to dashboard Cite

The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods). It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.

show abstract

Automatic identification of Cyclic Alternating Pattern (CAP) sequences based on the Teager Energy Operator

Cited by 10 publications

References 20 publications

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring

On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes

Contact Info

Product

Resources

About