Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

doi:10.22266/ijies2023.0228.40

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…In [13], a low-cost, portable EEG headband is utilized to find the absence of seizures in epilepsy patients-ML with Tensorflow to forecast its confidence level and the SVM technique for separating the initial limit. Next, to see its measurement divergence, the author tried to test the technique on two other patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence With Deep Learning Based Automated Ear Infection Detection

Mehedi,

Hanif,

Bilal

et al. 2024

IEEE Access

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Artificial intelligence (AI) related to intelligent control in healthcare denotes using AI techniques to enhance the management and control of healthcare processes and systems. Damage to the inner and middle ear caused by accidents and diseases even causes hearing impairment in the ear that has been harmed or injured. Traditional otoscopy devices were utilized to check the tympanic membrane (TM) to identify OM in medical practice, and a conclusion is drawn depending on the outcomes of the examination. While developing a computer-aided method to support the OM diagnosis, it is possible to focus on methods like feature extraction, image pre-processing, classification, and image segmentation. The existing methodology of detecting the ear infection experiences a reduction of accuracy due to the influence of the noise in the input ear image. This presence of noise affects the feature extraction process, directly influences the accuracy in detection process. To overcome this issue, in this manuscript, a Deep learning (DL) is utilized to find biomedical ear infections by examining images of the eardrum and ear canal. The process includes training a DL method with a large dataset of ear images, where the images were labeled as either not infected or infected. With this motivation, this article emphasizes the design of Bayesian optimization with a deep learning-based automated ear infection detection and classification (BODL-AEIDC) model. The BODL-AEIDC technique exploits the DL model with a metaheuristic optimization algorithm for the ear infection classification process. The BODL-AEIDC technique employs a Wiener filtering (WF) based noise removal process to eliminate the noise data. In addition, the BODL-AEIDC technique exploits W-Net-based segmentation and the EfficientNet model for feature extraction purposes. Moreover, the BODL-AEIDC technique employs a fuzzy Restricted Boltzmann machine (FRBM) model for ear infection detection. Furthermore, the BO algorithm is utilized to adjust the FRBM technique's hyperparameter values effectively. The BODL-AEIDC technique's experimental outcomes occur using the medical dataset. The comprehensive comparative study stated the enhanced performance of the BODL-AEIDC approach over other existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence With Deep Learning Based Automated Ear Infection Detection

Mehedi,

Hanif,

Bilal

et al. 2024

IEEE Access

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“…Terefore, feature selection scenarios are critical because they afect accuracy. SVM was chosen because it has an excellent ability in medical applications, one of which is to classify EEG signals, as reported in research [1,24,25].…”

Section: Evaluation With Support Vector Machine (Svm) Dan N-fold Cros...mentioning

confidence: 99%

Multiscale Hjorth Descriptor on Epileptic EEG Classification

Rizal,

Hadiyoso,

Aulia

et al. 2023

Journal of Electrical and Computer Engineering

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The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. Automatic epileptic EEG recognition is an area of interest on which much research focuses. The complexity approach to EEG signal analysis is interesting to be used as feature extraction, referring to the nonlinear characteristics of the signal. This study proposed an automatic epileptic EEG classification method based on the multiscale Hjorth descriptor measurement. EEG signals consisting of normal, interictal, and seizure (ictal) were simulated. The signal is scaled into new signals using the coarse-grained procedure on a scale of 1–20. Then, the Hjorth parameter which consists of activity, mobility, and complexity is calculated on the new signal. This process produces a feature vector that is used in the classification stage. Support vector machine (SVM) is used to evaluate the proposed feature extraction method. Simulation results showed that the Hjorth parameter on a scale of 1–15 yields 99.5% accuracy. The proposed method is expected to be applied to digital EEG for seizure detection and prediction.

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Design of Wheelchair Using Body Position Tracking Designed For Paralyzed Patients

Nasution,

Samosir,

Fahmi

2024

2024 14th International Conference on Electrical Engineering (ICEENG)

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Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

Cited by 4 publications

References 28 publications

Artificial Intelligence With Deep Learning Based Automated Ear Infection Detection

Artificial Intelligence With Deep Learning Based Automated Ear Infection Detection

Multiscale Hjorth Descriptor on Epileptic EEG Classification

Design of Wheelchair Using Body Position Tracking Designed For Paralyzed Patients

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