Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches
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ORIGINAL RESEARCH
Brain epilepsy seizure detection using bio‑inspired krill herd and artificial alga optimized neural network approaches Ahed Abugabah1 · Ahmad Ali AlZubi2 · Mohammed Al‑Maitah2 · Abdulaziz Alarifi2 Received: 8 July 2020 / Accepted: 4 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Nowadays, Epilepsy is one of the chronic severe neurological diseases; it has been identified with the help of brain signal analysis. The brain signals are recorded with the help of electrocorticography (ECoG), Electroencephalogram (EEG). From the brain signal, the abnormal brain functions are a more challenging task. The traditional systems are consuming more time to predict unusual brain patterns. Therefore, in this paper, effective bio-inspired machine learning techniques are utilized to predict the epilepsy seizure from the EEG signal with maximum recognition accuracy. Initially, patient brain images are collected by placing the electrodes on their scalp. From the brain signal, different features are extracted that are analyzed with the help of the Krill Herd algorithm for selecting the best features. The selected features are processed using an artificial alga optimized general Adversarial Networks. The network recognizes the intricate and abnormal seizure patterns. Then the discussed state-of-art methods are examined simulation results. Keywords Brain informatics · Epilepsy · Electroencephalogram (EEG) · Krill herd algorithm · Artificial alga optimized general adversarial networks
1 Introduction Epilepsy (Yan et al. 2015) is one of the neurological disorders that have unique characteristics and founded in Babylonian medicine text. The epilepsy disorder not only identified in human beings; it also founded in all species like rats, cats, and dogs. Epilepsy is spread all over the world because of abnormal or disturbed electrical activities (Andrzejak et al. 2001) in the brain. The brain signal disturb happened due to the low blood sugar level, shortage of oxygen at the time of childbirth and malformations. Around 50 million peoples are affected by this disease, and 100 million peoples are changed at least once in their lifespan. Therefore, this disease is most dangerous, and burden and the prevalence rate is nearly 0.5–1% (Chaudhary et al. 2011). The seizure disease is identified by several symptoms such as momentary consciousness loss, patient behavior, and sensation. According to the * Ahmad Ali AlZubi [email protected] 1
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia
2
symptoms, Epilepsy is divided into partial and generalized types (Fisher et al. 2017). The Partial seizure is named as a focal seizure that is occurred when the cerebral hemisphere is affected. It has been divided into simple and complex partial seizures. The complex epilepsy seizure patients are getting confused about recognizing the surrounding activities and behave abn
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