Using Lempel-Ziv complexity as effective classification tool of the sleep-related breathing disorders

Pręgowska, Agnieszka; Proniewska, Klaudia; Dam, Peter van; Szczepański, Janusz

doi:10.1016/j.cmpb.2019.105052

Cited by 10 publications

(6 citation statements)

References 44 publications

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“…EEG is essentially a multi-dimensional time series signal; thus, it is meaningful to measure EEG signals with the complexity indexes of information theory. Complexity measures of time series, such as entropy and Lempel–Ziv complexity, have been employed to quantify the dynamical changes of EEG [ 19 , 20 , 21 , 22 , 23 , 24 ]. Based on concepts from information theory, MI measures the interdependence between two variables, and it quantifies the amount of information that can be obtained about a random variable by observing another.…”

Section: Methodsmentioning

confidence: 99%

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification

Tian

et al. 2022

Brain Sciences

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EEG-based human identification has gained a wide range of attention due to the further increase in demand for security. How to improve the accuracy of the human identification system is an issue worthy of attention. Using more features in the human identification system is a potential solution. However, too many features may cause overfitting, resulting in the decline of system accuracy. In this work, the graph convolutional neural network (GCN) was adopted for classification. Multiple features were combined and utilized as the structure matrix of the GCN. Because of the constant signal matrix, the training parameters would not increase as the structure matrix grows. We evaluated the classification accuracy on a classic public dataset. The results showed that utilizing multiple features of functional connectivity (FC) can improve the accuracy of the identity authentication system, the best results of which are at 98.56%. In addition, our methods showed less sensitivity to channel reduction. The method proposed in this paper combines different FCs and reaches high classification accuracy for unpreprocessed data, which inspires reducing the system cost in the actual human identification system.

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Section: Methodsmentioning

confidence: 99%

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification

Tian

et al. 2022

Brain Sciences

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“…Next, the Lempel–Ziv complexity (LZC) and maximum and minimum distance (MMD) are extracted. LZC is utilised to evaluate the complexity of EEG signals [ 37 ]. MMD calculates the distance among the maximum and minimum signal samples in every subwindow, where

and

are the x-axis and y-axis deviations of the maximum as well as minimum signal samples in the m th window, respectively [ 38 ].…”

Section: Proposed Techniquementioning

confidence: 99%

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment

Radhakrishnan,

Ezra,

Jebadurai

et al. 2024

Sensors

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Autonomous sleep tracking at home has become inevitable in today’s fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO–XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model’s performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time–frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.

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“…Lempel-Ziv complexity (1976) is defined as the number of different sub-strings encountered as the binary sequence is viewed as a stream (from left to right) [11]. LZ complexity has been used extensively in a wide spectrum of domains from linguistics, phylogenetics and neural-spike train analysis to mechanical fault identification and siezure detection [25,26,27,28,29]. We demonstrate the usage of LZ complexity in the penalty model, where CCM L (y|G x ) can be directly obtained by concatenation of the two sequences as xy.…”

Section: Lz-basedmentioning

confidence: 99%

Causal discovery using compression-complexity measures

Nagaraj

2021

Journal of Biomedical Informatics

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Using Lempel-Ziv complexity as effective classification tool of the sleep-related breathing disorders

Cited by 10 publications

References 44 publications

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment

Causal discovery using compression-complexity measures

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