Time domain analysis of EEG signals for detection of epileptic seizure

Gill, Ammama Furrukh; Fatima, Syeda Alishbah; Nawaz, Aafreen; Nasir, Ayesha; Akram, M. Usman; Khawaja, Sajid Gul; Ejaz, Saqib

doi:10.1109/isiea.2014.8049867

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…We have used correlation technique to provide insight in the best EEG electrode locations for our study. Binaural studies described in literature commonly use time domain analysis [59,110,124]. A difference between previous studies and our method is the area calculation, indicating a higher energy for the response to antiphase signals, which could indicate binaural interaction in the human brain.…”

Section: Discussionmentioning

confidence: 86%

Study of Correlation Between EEG Electrodes for the Analysis of Cortical Responses Related to Binaural Hearing

et al. 2021

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Binaural hearing is the ability of the human auditory system to integrate information received from both ears simultaneously. Binaural hearing is fundamental in understanding speech in noisy backgrounds. Any disfunction in one or both ears could cause a disruption in the processing mechanism. Auditory evoked potentials (AEPs) are electrical potentials evoked by externally presented auditory stimuli from any part of the auditory system. A non-invasive technology, electroencephalography (EEG) is used for the monitoring of AEPs. The research aims to identify the best suited electrode positions through correlation analysis and analyse the AEP signals from the selected electrodes in order to detect binaural sensitivity of the human brain. The study evaluates the time-averaged EEG responses of normal hearing subjects to auditory stimuli. The stimuli used for the study are 500 Hz Blackman windowed pure tones, presented in either homophasic (the same phase in both ears) or antiphasic (180-degree phase difference between the two ears) conditions. The study focuses on understanding the effect of phase reversal of auditory stimuli, an under interaural time difference (ITD) cue, on the middle latency response (MLR) region of the AEPs. A correlation analysis was carried out between the eight different locations and as a result, Cz and Pz electrode positions were selected as the best suited positions for further analysis. The selected electrode signals were further processed in the time domain and frequency domain analysis. In the time domain analysis, it was found that Cz electrode for eight subjects out of nine and Pz electrode for seven subjects out of nine, had the larger area under signal curve obtained in the antiphasic condition than in the homophasic signals. Frequency domain analysis showed that the frequency bands 20 to 25Hz and 25 to 30Hz had the most energy when elicited by antiphasic stimuli than by homophasic stimuli. The findings of this study can be further utilised for the detection of binaural processing in a human brain.

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Section: Discussionmentioning

confidence: 86%

Study of Correlation Between EEG Electrodes for the Analysis of Cortical Responses Related to Binaural Hearing

et al. 2021

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“…There is a wealth of well-established methods in the ML literature that can effectively decrease feature size, thereby relaxing computational complexity and reducing feature redundancy. Common methods include dimensionality reduction [414], [414]- [421] and feature ranking and selection [8], [125], [414], [422]- [434]. In DL-based feature extraction methods, the substantial size of the resultant deep-learned features necessitates dimensionality reduction since these features constitute a multi-dimensional latent space.…”

Section: Computational Complexitymentioning

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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EEG seizure detection: concepts, techniques, challenges, and future trends

Dessouky

El‐Sayed

Hemdan

2023

Multimed Tools Appl

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A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and timeconsuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart Multimedia Tools and Applications

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Time domain analysis of EEG signals for detection of epileptic seizure

Cited by 4 publications

References 11 publications

Study of Correlation Between EEG Electrodes for the Analysis of Cortical Responses Related to Binaural Hearing

Study of Correlation Between EEG Electrodes for the Analysis of Cortical Responses Related to Binaural Hearing

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

EEG seizure detection: concepts, techniques, challenges, and future trends

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