A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

Stalin, Shalini; Roy, Vandana; Shukla, Prashant Kumar; Zaguia, Atef; Khan, Mohammad Monirujjaman; Shukla, Piyush Kumar; Jain, Anurag

doi:10.1155/2021/2942808

Cited by 72 publications

(31 citation statements)

References 37 publications

(48 reference statements)

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“…mRMRe has minimum Redundancy and Maximum Relevance ensemble (mRMRe), which takes advantage of parallel computing to create numerous feature sets rather than a single feature set, in order to overcome this problem [ 24 ].…”

Section: Minimum Redundancy and Maximum Relevance Ensemble (Mrmre) Me...mentioning

confidence: 99%

Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

Sathya¹,

Jeyaselvi

Joshi

et al. 2022

Journal of Healthcare Engineering

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In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper.

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Section: Minimum Redundancy and Maximum Relevance Ensemble (Mrmre) Me...mentioning

confidence: 99%

Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

Sathya¹,

Jeyaselvi

Joshi

et al. 2022

Journal of Healthcare Engineering

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“…Feature Extraction. Characteristics extracted by feature extraction methods are distinguishable features that are not affected by incorrect adjustments to the input [5]. Following the picture segmentation stage, the extraction of features is carried out either at the tissue level or the cellular level to 11 quantify differences.…”

Section: Introductionmentioning

confidence: 99%

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

Reshma

Arya

Ahmad

et al. 2022

BioMed Research International

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Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

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“…In addition, FIR digital sifters are applied. Elliptic IIR strainer with Blackman of FIR filter demonstrated superior consequences in SNR besides power spectral density presentation metrics than the conventional FIR filter [ 22 ]. Consequently, to remove various sounds from ECG data, we developed a cascaded FIR/IIR filter [ 23 , 24 ].…”

Section: Existing Methodologymentioning

confidence: 99%

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Amhia

Wadhwani

2022

Journal of Healthcare Engineering

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Cardiovascular health and training success can be assessed using electrocardiogram (ECG) data. For over a quarter of a century, an individual’s resting heart rate is varying more. As a result, it has become the subject of inquiry and reveals the intricate relationship between the human body and its environment. The autonomic nervous system has impact on blood flow system based on the rate of heartbeats. However, heart rate variation (HRV) characteristics analysis throughout the time period has lack of physical activity information. In the presence of patient movement, ECG signal is suffering from hard artefacts. Time-varying HRV parameters can be derived from low-frequency (LF) and high-frequency (HF) domains of the correct frequency. However, sometimes it is critical to ensuring accurate detection of the R-peak position. The proposed ROIIR (reduced-order IIR) offers 8.8% improvement in peak-to-peak swing than earlier IIR filter. We present an advanced filtering algorithm that is used for R-peak detection.

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A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

Cited by 72 publications

References 37 publications

Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

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