Automatic A-Phase Detection of Cyclic Alternating Patterns in Sleep Using Dynamic Temporal Information

Hartmann, Simon; Baumert, Mathias

doi:10.1109/tnsre.2019.2934828

Cited by 54 publications

(51 citation statements)

References 26 publications

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“…Most of the methods proposed in the state of the art were developed for the estimation of the CAP A phases by examining specific features from the characteristic EEG bands [24] . These features were then fed to a classifier, typically developed using a machine learning approach such as Linear Discriminant Analysis (LDA), to create models which achieved a performance that ranged from 68% to 86% [25,26] . A brief introduction to some of the details of the features employed by these models is presented in the subsequent list:…”

Section: Discussionmentioning

confidence: 99%

“…• 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%

See 1 more Smart Citation

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

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Section: Discussionmentioning

confidence: 99%

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

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“…Evaluates the variance of the signal's amplitude [23], [26] Lempel-Ziv complexity Metric that evaluates the randomness of a finite sequence [36], [35], [11], [32], [9], [31] Macro-micro structure descriptor (band descriptor)…”

Section: State Of the Artmentioning

confidence: 99%

“…Evaluates the complexity of a signal by estimating the probability for a given value to occur (high probabilities suggest that the signal has less information, leading to a smaller entropy) [26], [36], [35]…”

Section: Sample Entropymentioning

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.

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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.

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“…An explanatory example regarding the classification of an EEG signal into the CAP phases, cycles and sequence is presented in Figure 1 . The A phases can be subdivided into three subtypes (A1, A2 and A3) [ 5 ] that can be examined for a deeper analysis of the sleep process [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation

Mendonça

Mostafa

Morgado‐Dias

et al. 2019

Entropy

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Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.

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Automatic A-Phase Detection of Cyclic Alternating Patterns in Sleep Using Dynamic Temporal Information

Cited by 54 publications

References 26 publications

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

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

A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation

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