Wavelet-based Entropy Analysis on EEG Signal for Detecting Seizures

Humairani, Annisa; Rizal, Achmad; Wijayanto, Inung; Hadiyoso, Sugondo; Fuadah, Yunendah Nur

doi:10.1109/icoict55009.2022.9914898

Cited by 5 publications

(1 citation statement)

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“…On the other hand, wavelet transform is a computational method designed to analyze non-stationary signals by decomposing them into various frequency components while maintaining temporal resolution. The wavelet transform employs basic functions called "wavelets", allowing simultaneous frequency and time domain analysis [25,26]. Equation ( 3) is a mathematical expression for the wavelet transform.…”

Section: Continuous Wavelet Transformation (Cwt)mentioning

confidence: 99%

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images

Khan,

Jan,

Koo

2023

Sensors

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Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time–frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time–frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.

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Section: Continuous Wavelet Transformation (Cwt)mentioning

confidence: 99%