Advanced Fuzzy Set: An Application to Flat Electroencephalography Image

Zenian, Suzelawati; Ahmad, Tahir; Idris, Amidora

doi:10.1007/978-981-15-0058-9_62

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…EEG signals are typically obtained by placing electrodes on the scalp to measure the differential voltage fluctuations resulting from ionic current flows within the neurons of the brain. EEG signals can detect changes over milliseconds [448], thereby providing insights into the rapid dynamics of brain activity…”

Section: B Epilepsy Detection Using Eeg Brain Signalsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Section: B Epilepsy Detection Using Eeg Brain Signalsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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“…It has been successfully digitized into grid by using Voronoi digitization. The digital Flat EEG is transformed into image by using fuzzy approach [8][9][10]. There are three main steps that are involved in the transformation of Flat EEG into image as follows:…”

Section: Digital Flat Eegmentioning

confidence: 99%

Flat EEG Image Segmentation by Fuzzy Entropy-Based Multi-Level Thresholding

Zenian,

Ahmad,

Hamzah

2022

MATEMATIKA

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Thresholding is a type of image segmentation that deals with the conversion of an image with many gray levels into another image with fewer gray levels. It classifies grayscale pixels into two categories which creates a binary image. However, the output image is not alwayssatisfying due to several factors such as inherent image vagueness as uncertainty arises within the gray values of an image. In this paper, a multi-level image thresholding based on fuzzy entropy is applied on Flat Electroencephalography (Flat EEG) image. The outcomes are compared visually with global thresholding.

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“…In the same year, Yang et al, [8] applied a new intensifier operator to enhance fuzzy images. An application on contrast improvement by using advanced fuzzy technique on Flat EEG images was presented by Zenian et al, [9]. In addition, Yogish et al, [10] focuses on improving the quality and clarity of microscopic medical images through the use of fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Contrast Enhancement Techniques Using Rule-Based System and Intensification Operator

Suzelawati Zenian,

Tan Jia Xuan,

Siti Aisyah Tumiran

2024

ARASET

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Contrast enhancement is a crucial step in digital image processing to ensure the production of high-quality images. This paper presents two distinct fuzzy techniques designed to address the nuanced concept of contrast. that utilize rule-based system and intensification operator, respectively, to handle the inherent vagueness associated with the concept of contrast. The fuzzy rule-based system calculates the intensity value of the output image based on the contrast measure (standard deviation) of the input image, thereby enabling effective contrast enhancement for low contrast images. Meanwhile, the fuzzy intensification operator aims to accentuate the differences in intensity levels within the image, thereby enhancing the contrast between various features and details. The experimental results show better image contrast is obtained using both methods. However, fuzzy rule-based approach shows more images with better quality compared to the intensification operator approach. The performance of these techniques is evaluated both visually and quantitatively.

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Advanced Fuzzy Set: An Application to Flat Electroencephalography Image

Cited by 8 publications

References 7 publications

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Flat EEG Image Segmentation by Fuzzy Entropy-Based Multi-Level Thresholding

Fuzzy Contrast Enhancement Techniques Using Rule-Based System and Intensification Operator

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