Enhanced automatic sleep spindle detection: a sliding window-based wavelet analysis and comparison using a proposal assessment method

Zhuang, Xiaobin; Li, Yuanqing; Peng, Nengneng

doi:10.1186/s40535-016-0027-9

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Sleep experts have observed that k-complexes normally appear in EEG signals for 0.5 to 2 s. The sliding window technique was utilized by Siuly et al (2011) for the classification of EEG signals. It was also utilized by Al-Salman et al (2018) and Zhuang et al (2016) to detect sleep spindles in EEG signals. Kam et al (2004) employed the sliding window method to detect k-complexes in their study.…”

Section: Proposed Methodsmentioning

confidence: 99%

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

Al-Salman

Wen

2019

Front. Neuroinform.

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K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to detect k-complexes from EEG signals based on fractal dimension (FD) of time frequency (T-F) images coupled with undirected graph features. Firstly, an EEG signal is partitioned into smaller segments using a sliding window technique. Each EEG segment is passed through a spectrogram of short time Fourier transform (STFT) to obtain the T-F images. Secondly, the box counting method is applied to each T-F image to discover the FDs in EEG signals. A vector of FD features are extracted from each T-F image and then mapped into an undirected graph. The structural properties of the graphs are used as the representative features of the original EEG signals for the input of a least square support vector machine (LS-SVM) classifier. Key graphic features are extracted from the undirected graphs. The extracted graph features are forwarded to the LS-SVM for classification. To investigate the classification ability of the proposed feature extraction combined with the LS-SVM classifier, the extracted features are also forwarded to a k-means classifier for comparison. The proposed method is compared with several existing k-complexes detection methods in which the same datasets were used. The findings of this study shows that the proposed method yields better classification results than other existing methods in the literature. An average accuracy of 97% for the detection of the k-complexes is obtained using the proposed method. The proposed method could lead to an efficient tool for the scoring of automatic sleep stages which could be useful for doctors and neurologists in the diagnosis and treatment of sleep disorders and for sleep research.

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Section: Proposed Methodsmentioning

confidence: 99%

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

Al-Salman

Wen

2019

Front. Neuroinform.

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“…In a semi-automatic application with low sensitivity, it is necessary for a specialist to inspect the markings performed by the classifier, eliminating the excess of false positives. This is the case of the detector of Zhuang et al [44].…”

Section: Comparison To Literature Modelsmentioning

confidence: 99%

“…Tsanas at. el [39] and Zhuang et al [44] proposed continuous wavelet transform (CWT) based approaches and the estimation of the probability of spindles occurrences. Lachner-Piza et al [21] proposed a SVM approach with a feature selection method based on the label-feature and feature-feature correlations for determining the relevance and redundancy of each feature.…”

Section: Comparison To Literature Modelsmentioning

confidence: 99%

Classification of EEG signals using genetic programming for feature construction

Miranda

Aranha

Ladeira

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumors. In this context, one important task is the identification of visible structures in the EEG signal, such as sleep spindles and K-complexes. The identification of these structures is usually performed by visual inspection from human experts, a process that can be error prone and susceptible to biases. Therefore there is interest in developing technologies for the automated analysis of EEG. In this paper, we propose a new Genetic Programming (GP) framework for feature construction and dimensionality reduction from EEG signals. We use these features to automatically identify spindles and K-complexes on data from the DREAMS project. Using 5 different classifiers, the set of attributes produced by GP obtained better AUC scores than those obtained from PCA or the full set of attributes. Also, the results obtained from the proposed framework obtained a better balance of Specificity and Recall than other models recently proposed in the literature. Analysis of the features most used by GP also suggested improvements for data acquisition protocols in future EEG examinations.

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Automatic Detection of Sleep Spindles Using Time Domain Features

Fatima

Farooq

Singh

2020

Advances in Data and Information Sciences

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Enhanced automatic sleep spindle detection: a sliding window-based wavelet analysis and comparison using a proposal assessment method

Cited by 9 publications

References 13 publications

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

Classification of EEG signals using genetic programming for feature construction

Automatic Detection of Sleep Spindles Using Time Domain Features

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