Artificial Neural Network Model Using Short-Term Fourier Transform for Epilepsy Seizure Detection

Barneih, Feras; Nasir, Nida; Alshaltone, Omar; Qatmah, Mahmoud; Bonny, Talal; Shabi, Mohammad Al; Al-Shamma’a, Ahmed

doi:10.1109/aset53988.2022.9735094

Cited by 31 publications

(17 citation statements)

References 24 publications

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“…(2021) , diabetic retinopathy detection Nasir et al. (2022b) , epileptic seizure detection Barneih et al. (2022) , sleep apnea detection Qatmh et al.…”

Section: Introductionmentioning

confidence: 99%

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Nasir

Kansal

Barneih

et al. 2023

Intelligent Systems with Applications

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“…(2021) , diabetic retinopathy detection Nasir et al. (2022b) , epileptic seizure detection Barneih et al. (2022) , sleep apnea detection Qatmh et al.…”

Section: Introductionmentioning

confidence: 99%

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Nasir

Kansal

Barneih

et al. 2023

Intelligent Systems with Applications

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“…The characteristics of seizures can differ from person to person and are determined by a number of factors, including the location in the brain where the seizure first began and the amount of time that it had been spreading. 3,4 This illness has been around for a very long time and is known to a lot of people. The majority of people who have epilepsy also struggle with mental health conditions such as anxiety and depression in addition to their physical symptoms (such as fractures and bruising from injuries).…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure detection using transfer learning

Nasir

Barneih

Alshaltone

et al. 2023

Smart Biomedical and Physiological Sensor Technology XX

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The most notable advancement in the 21st Century has been in artificial intelligence (AI). Despite how far AI has progressed, how it applies to healthcare remains a significant challenge for brilliant minds all over the world. A neurological condition known as epilepsy can strike a person at any time in their life. An individual with epilepsy therefore experiences frequent to infrequent seizures, which can occasionally result in death. Electroencephalogram (EEG) signals aid in the diagnosis of this condition. However, lengthy EEG signals frequently take a day or longer to detect this disorder, even for trained neurologists, and may even cause human error. Therefore, it is essential to create a reliable and computationally efficient system. This study aims to classify seizures by creating Convolutional Neural Network (CNN) Inception ResNet V2 and short-time Fourier transform (STFT) to extract the time-frequency plane from time domain signals. This study helped to better classify health and seizures by achieving up to 100% the highest classification accuracy.

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“…Another reason for this contradiction is the diversity in selected the input parameters [12], where any changes in these parameters affect the performance of the model. Moreover, the usage of the machine learning could you help losing weight and improve the life quality [13][14][15][16][17][18][19][20][21][22][23]. Moreover, metaheuristic algorithms, integrated with machine learning techniques [24][25][26][27][28][29][30][31][32][33][34][35][36], can optimize the selection of input parameters for WBV studies on weight loss and quality of life improvements, overcoming challenges related to standardized protocols and diverse parameter settings, and providing more consistent and reliable outcomes [37][38][39][40][41][42][43][44][45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Studying the gender effect on apparent mass during whole-body vibration using ANN

Al-Shabi

Nawayseh

2023

Smart Biomedical and Physiological Sensor Technology XX

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Artificial Neural Network (ANN) is a powerful tool to model a system using only the inputs and outputs of that system. In this paper, ANN is used to model the relation between the subject's gender to its performance while been excited in a whole-body vibration machine (WBV). For training the ANN, 20 male and 20 female subjects were observed during an experimental setup using a WBV at different vibration frequencies in the range of 20 to 45 Hz. The apparent mass was measured for the subjects at different frequencies. The input to the ANN includes body mass index, mass, and gender of the subjects along with and the excitation frequency. The ANN shows a good performance and extract the relationship with a performance that has a root mean squared error of the relative percentage error less than 9%.

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Artificial Neural Network Model Using Short-Term Fourier Transform for Epilepsy Seizure Detection

Cited by 31 publications

References 24 publications

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

Epileptic seizure detection using transfer learning

Studying the gender effect on apparent mass during whole-body vibration using ANN

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