Adaptive SSA Based Muscle Artifact Removal from Single Channel EEG Using Neural Network Regressor

Dora, Chinmayee; Patro, Ram Narayana; Rout, Susanta Kumar; Biswal, Pradyut Kumar; Biswal, Birendra

doi:10.1016/j.irbm.2020.08.002

Cited by 17 publications

(9 citation statements)

References 21 publications

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“…In order to identify a correlation between them, ML algorithm is adopted and implemented. Although ML algorithms are extensively applied for regression and classification purposes such as fiscal analysis 7 , remote sensing 8,9 , health related like diabetes prediction 10,11 . While, their applications for body weight prediction of child are sparse in literature.…”

Section: Based Body Weight Predictionmentioning

confidence: 99%

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

Saishree Sahu,

Prasanna Kumar Dixit,

Chinmayee Dora

2023

IJPHRD

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Background:The maternal education and socio-economic status of the mother greatly affect the growth of the baby. Nutritional deficiency is the major cause of motor and physiological disorders in children. Hence, predicting body weight could be used to monitor the growth of the babies. Although the prediction of weights is standardized by World Health Organization (WHO), for babies based on their gender and age; generalizing it for all locations and socio-economic variations is not possible Method: Hence, in this paper, an ANN-based predictive approach to establish the relationship between the factors like socioeconomic status, age, and prior weights to predict the baby's weight using a locally acquired dataset. This could be helpful for the health workers to properly diagnose the disease a time ahead. Conclusion:The proposed work suggests predicting the baby's growth rate using Machine Learning (ML) techniques is both an efficient and feasible approach.

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Section: Based Body Weight Predictionmentioning

confidence: 99%

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

Saishree Sahu,

Prasanna Kumar Dixit,

Chinmayee Dora

2023

IJPHRD

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“…This method was robust since ARX model selects the optimal model and shows the better performance. Dora et al [104] proposed hybrid method with SSA and neural network regressor (NNR) to remove muscle artifacts from single channel EEG. All these methods when combined with machine learning methods have shown improved performance.…”

Section: Performance Metricmentioning

confidence: 99%

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

2022

IJATEE

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The artifacts may affect the signal in spectral, temporal and in few cases it affects spatial domain as well which makes it difficult to process. In this case, simple filtering is not sufficient to completely remove the artifacts during pre-processing. Hence, many hybrid methods were developed but still there is no single method to detect and remove all types of artifacts [2].In this context, this paper provides a systematic literature review on types of artifacts, existing methods and comparative analysis on available methods. Further, performance evaluation metrics and available open-source tools for artifact removal are also discussed. The preferred reporting items for

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“…In 2017, Barua et al [18] have proposed an algorithm which is known as automated artifact handling in EEG (ARTE). It was employed as a pre-processing step in the driver monitoring application, and functioned based on the wavelets, hierarchical clustering, and independent component analysis (ICA In 2020, Dora et al [27] have introduced an adaptive SSA algorithm for muscle artifact removal. The mobility threshold was decided in an adaptive manner by neural network regressor (NNR).…”

Section: Related Workmentioning

confidence: 99%

“…Electroencephalogram (EEG) is a voltage test recording the electrical activity of the neurons in the brain. EEG is obtained in various frequencies including delta (1-4 Hz), theta (4-8 Hz), alpha , beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (greater than 30 Hz) [1]. EEG brain rhythms with different frequency ranges are shown in Table 1 [2].…”

Section: Introductionmentioning

confidence: 99%

Artifact Removal Methods in EEG Recordings: A Review

Mathe

Padmaja

Krishna

2021

Proc. eng. technol. innov.

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To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

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Adaptive SSA Based Muscle Artifact Removal from Single Channel EEG Using Neural Network Regressor

Cited by 17 publications

References 21 publications

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

Artifact Removal Methods in EEG Recordings: A Review

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