Prediction and analysis of chronic epilepsy using electroencephalographic signals on medical internet of things platform

Issue title: Machine Learning Based Computational Bioinformatics for Healthcare Big Data

Guest editors: Muhammad Attique Khan, Gaurav Dhiman and Sathishkumar VE

Article type: Research Article

Authors: Hassoon, Noor Hasan^a | Ali, Mohammed Hasan^b | Jaber, Mustafa Musa^{c; d; *} | Abd, Sura Khalil^e | Abosinnee, Ali S.^{f; g} | Kareem, Z.H.^h

Affiliations: [a] Department of Computer, College of Education for Pure Science, University of Diyala, Diyala, Iraq | [b] Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Najaf, Iraq | [c] Medical Instrumentation Techniques Engineering Department, Alfarahidi University, Baghdad, Iraq | [d] Medical Instrumentation Techniques Engineering Department, Al-turath University College, Baghdad, Iraq | [e] Ministry of Higher Education and Scientific Research, Baghdad, Iraq | [f] Department of Computer Technical Engineering, Altoosi University College, Najaf, Iraq | [g] Department of Computer Technical Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq | [h] Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq

Correspondence: [*] Corresponding author: Mustafa Musa Jaber, Medical Instrumentation Techniques Engineering Department, Alfarahidi University, Baghdad 10021, Iraq. E-mail: Mustafa.jaber@turath.edu.iq.

Abstract: Epilepsy patients who are presently refractory may be monitored using a seizure prediction Brain-Computer Interface (BCI), which uses electrodes strategically implanted in the brain to anticipate and regulate the onset and duration of a seizure. Real-time approaches to these technologies have challenges, as seen by seizures’ instantaneous electrographic activity. Electroencephalographic (EEG) signals are inherently non-stationary, which means that the regular and seizure signals differ significantly among people with epilepsy. Due to the restricted number of contacts on electrodes, dynamically processed and collected characteristics cannot be employed in a prediction function without causing significant processing delays. Big data can guarantee secure storage in these situations, and it has the maximum processing capability to identify, record, and analyze time in real-time to conduct the seizure event on the timetable. Seizure prediction and location for huge Scalp EEG recordings have been the focus of this study, which used wearable sensor data and deep learning to use cloud storage to develop the systems. A novel technique is suggested to avoid an epileptic seizure and discover the seizure origin from the utilized wearable sensors. Secondly, deep learning architectures called Clustered Autoencoder with Convolutional Neural Network (CAE-CNN), an expanded optimization methodology is presented based on the Principal Component Analysis (PCA), the Hierarchical Searching Algorithm (HSA), and the Medical Internet of Things (MIoT) has been established to define the suggested frameworks based on the collection of big data storage of the wearable sensors in real-time, automatic computation and storage. According to clinical trials, CAE-CNN outperforms the current wearable sensor-based treatment for unresolved chronic epilepsy patients.

Keywords: Chronic epilepsy, big data, MIoT, wearable sensors, seizure prediction, auto encoder, CNN, PCA, HSA

DOI: 10.3233/IDA-237434

Journal: Intelligent Data Analysis, vol. 27, no. S1, pp. 65-82, 2023