An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks

Abstract

Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %.

An IoT Platform for Epilepsy Monitoring and Supervising

“…Ergonomic issues also include factors like energy efficiency and long battery life: the lack of them forces frequent charging cycles of the devices and some other annoyances. Besides, some examples of devices without lacking in ergonomic issues are found in the main part of the solutions; they are efficient for their purpose, but their use is uncomfortable though: using sensing caps [7,8,31,32], wrongly sized WD [36,41], too many WDs [8], and so on.…”

“…A very detailed explanation of the requirements and of the hardware decisions is included. [31,32] An EEG cap linked to a Smartphone is arranged to send the gathered data to CC services. Whenever a seizure is detected on the cloud, GPS locations are shared through the notification system.…”

“…In EpiCare, a home care platform based on Mobile Cloud Computing to assist epilepsy diagnosis [7], portable EEG are used to detect the seizures in controlled environments. Sareen gathers data from EEC, using MCC and CC for storage and for sending notifications to the relatives and medical staff, including location information [31,32]. The main difference between Sareen and Bajwa is that the latter proposes CC only.…”

“…On the other hand, there are solutions where the gathered data is stored for data analytics [39,40] or even for future use [41][42][43][44]. Finally, complete CC solutions have been also tackled in the literature, including not only the data storage and visualization but also the modelling and classification of the current state of affairs [7,31,32,[45][46][47][48].…”

On-line EEG Denoising and Cleaning Using Correlated Sparse Signal Recovery and Active Learning

“…Automatic detection of times of decreased performance might be useful in safety sensitive environments, for drivers [3] and for workers during night shifts [4]. While there are several studies of EEG-based systems predicting attention lapses [5–7] or detecting/predicting seizures [8, 9], those analyses assumed that the EEG had been cleaned of artifacts. The dominating presence of artifacts in real EEG data make predictions from these algorithms unreliable [10].…”