Multivariate Sliding-Mode Singular Spectrum Analysis for the Decomposition of Multisensor Time Series

Jain, Sahil; Panda, Rohan; Tripathy, Rajesh Kumar

doi:10.1109/lsens.2020.2996096

Cited by 20 publications

(7 citation statements)

References 16 publications

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“…The existing approaches have used various uni-variate signal processing techniques for the classification of sleep stage classes with EEG signals. In recent years, various multivariate signal decomposition-based methods have been used for the analysis of different multi-channel physiological signals [ 1 , 25 , 26 ]. These methods considered all channel information of the physiological signals simultaneously for the decomposition.…”

Section: Introductionmentioning

confidence: 99%

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Tripathy

Ghosh

Gajbhiye

et al. 2020

Entropy

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The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.

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Section: Introductionmentioning

confidence: 99%

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Tripathy

Ghosh

Gajbhiye

et al. 2020

Entropy

Self Cite

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“…After signal filtering, a sliding window method [30] is applied to decompose EEG trials into equal size segments of 0.5 seconds. Since the sampling frequency was 250 Hz, we obtained 125 instances of given MI sequences for further processing.…”

Section: ) Signal Segmentationmentioning

confidence: 99%

“…Here, we first performed EEG preprocessing to reduce the noise and outliers from the raw signals. The preprocessed EEG signals are decomposed into equal size chunks using the sliding window method [30] and later the Multivariate Empirical Mode Decomposition (MEMD) [24] is adopted for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

Tiwari

Chaturvedi

2021

IEEE Access

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Brain-Computer Interface (BCI) provides a direct communicating pathway between the human brain and the external environment. In the BCI systems, electroencephalography (EEG) signals are used to represent different cognitive patterns corresponding to various limb movements or motor imagery (MI) activities. However, EEG signals are multichannel in nature that require explicit information processing to alleviate the computational complexity of the BCI system. This paper represents a novel channel selection method to find effective EEG channels using Dynamic Channel Relevance (DCR) score. The proposed approach explores the minimum redundancy maximum relevance paradigm for selecting pertinent channels from raw ones. Firstly, the EEG signals are preprocessed using the Savitzky-Golay filter, and a sliding window approach is used to decompose them into chunks of fixed length. Further, three priorly known channels are used as candidate solutions for detecting new correlated channels. The selected channels are used to extract spatial-temporal features using the Multivariate Empirical Mode Decomposition (MEMD) method. The extracted features are used to discriminate four MI tasks (left hand, right hand, tongue, and foot) using the Support Vector Machine (SVM). The experiment is validated on three public EEG datasets (BCI Competition IV-2008 -IIA, BCI Competition IV-dataset 1, BCI competition III -dataset IVa). The results show that the proposed method achieved a superior classification accuracy (85.4% on dataset 1, 80.33% on dataset 2, and 85.20% on dataset 3) with a lesser number of channels compared to state-of-the-art methods. In addition, our method significantly reduced the computation time compared to other published results without compromising the classification accuracy. Topographical mapping between the selected channels and the cognitive regions showed that the central, frontal, and parietal lobe contributes to the execution of various MI tasks during physical activities.

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“…In order to minimize the forecast error of electrical loads, scholars have carried out many studies on combined forecasting models based on data preprocessing and electrical load forecasting models. Typically, these scholars use wavelet transform (WT) [23], empirical modal decomposition (EMD) [24], variational modal decomposition (VMD) [25], and singular spectrum analysis [26] for noise reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

et al. 2021

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Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

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Multivariate Sliding-Mode Singular Spectrum Analysis for the Decomposition of Multisensor Time Series

Cited by 20 publications

References 16 publications

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

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