Removal of muscle artifacts from EEG based on ensemble empirical mode decomposition and classification of seizure using machine learning techniques

Dutta, Kusumika Krori; Venugopal, K R; Swamy, Sunny Arokia

doi:10.1109/icici.2017.8365259

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…Extended Empirical Mode Decomposition (EEMD) has also used for EEG artifact removal [3]. Empirical mode decomposition methods can be used as filters but are not strictly in the same category.…”

Section: A Signal Decomposition Methodsmentioning

confidence: 99%

Artifact Detection and Correction in EEG data: A Review

Sadiya,

Alhanai,

Ghassemi

2021

Preprint

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Electroencephalography (EEG) has countless applications across many of fields. However, EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts contribute to the noisiness of EEG, and many techniques have been proposed to detect and correct these artifacts. These techniques range from simply detecting and rejecting artifact ridden segments, to extracting the noise component from the EEG signal. In this paper we review a variety of recent and classical techniques for EEG data artifact detection and correction with a focus on the last half-decade. We compare the strengths and weaknesses of the approaches and conclude with proposed future directions for the field.

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Section: A Signal Decomposition Methodsmentioning

confidence: 99%

Artifact Detection and Correction in EEG data: A Review

Sadiya,

Alhanai,

Ghassemi

2021

Preprint

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“…Using convolutional neural networks (CNN) and transfer learning for lung cancer detection [16], algorithm for identifying lung nodules based on deep feature fusion [17], Classification of Lung Disease Using a Deep Learning Algorithm Based on Voting [18] etc. are carried out with different pre-processing techniques [19]. Even some of the wearable devices are developed based on AI techniques to aid diagnosis procedure [20].…”

Section: Literature Surveymentioning

confidence: 99%

“…[19] [23] AICECS-2023 Journal of Physics: Conference Series 2571 (2023) 012004 IOP Publishing doi:10.1088/1742-6596/2571/1/012004 4 that determine performance of a model These metrics help identify the usefulness of the models developed. Some of the parameter which were determine in lung cancer detection are listed below.…”

mentioning

confidence: 99%

Supervised learning techniques for detection of Lung Carcinoma

Jalall,

Harsha,

Dutta

et al. 2023

J. Phys.: Conf. Ser.

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Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer

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“…The proposed solution overcomes the problems associated with the conventional method, such as source opacity, disordered independent components and a large number of independent components. Kusumika Krori Dutta [5] used ensemble empirical mode decomposition with optimization via deep learning proficiency to denoise EEG signals. Deep learning attempted to realize and tune the optimization process without the need to pretreat the EEG signals.…”

Section: Introductionmentioning

confidence: 99%

“…However, in real EEG data, the ISR measure is not applicable because no information is available about the original sources. The EEG system is designed to measure the electrical waves that show the biological functions of the human brain by placing sensing equipment on the head surface [5]. Figure 1 shows that EEG signals are present in an unsystematic susceptible range compared with the artefacts.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

Ahmed

AlShemmary

2020

IEEE Access

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Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (ℎlong, ℎshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (ℎlong, ℎshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.

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Removal of muscle artifacts from EEG based on ensemble empirical mode decomposition and classification of seizure using machine learning techniques

Cited by 12 publications

References 9 publications

Artifact Detection and Correction in EEG data: A Review

Artifact Detection and Correction in EEG data: A Review

Supervised learning techniques for detection of Lung Carcinoma

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

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