Sensing and Monitoring of Epileptical Seizure Under IoT Platform

Introduction

Epilepsy is a neurological disorder by which a large volume of the population in this world is affected. Epilepsy seriously affects the life of human being with huge impact, which affects not only personal life but also professional and social too (M. Vergara et al., 2017). With the help of the Internet of things (IoT) platform sensing seizures and patient monitoring is reached at a great level of accuracy. IoT platform has basically consist of two different part which includes data sensing /collection with the help of wearable sensors and another one is the detection of epilepsy, with following various methods. At very first, the important thing is to a collection of data of epileptic patient before and after the seizure. Further using various features, analysis and detection seizure can be done. Initially, it was a very time-consuming task of prediction seizure by just visualizing the signals and it is getting tougher with a very small amount of information regarding the seizure. During the past few years, numbers of the algorithm have been proposed for signal classification /seizure detection.

Figure 1.

The basic structure of IoT-enabled epilepsy data management and monitoring system

IoT has a verity of the structure of interconnected networks of various types of sensors usually has limited capability of storing and processing the data. With clouds, IoT has a great range of applications which makes smart health system feasible (Yin et al., 2016). The requirement of cost optimization with the high quality of data transmission ability helps to grow IoT. The emergence of IoT with cloud technology is the result of this requirement, which makes real-time data processing possible for the remote patient too (Chen et al., 2018; Hou et al., 2016). Sometimes for the epileptic patient, it tends to impossible to move, in such cases a smart patient monitoring system is very much required to access the medical information between user end and hospital. In such an IoT network, smart sensors are very much required for biomedical (EEG) data. Smart sensors used to process the user-centric data to the cloud framework. This large volume of processed data is too complex to handle at the server end.

Besides providing medical information from patients, IoT industries help to grow a nation. Smart health service/IoT-cloud-based network is able to provide accurate service with the minimum service cost. With the amalgamation of IoT and cloud, real-time data processing of signals like EEG, ECG, etc becomes possible. For that, a standard and intelligent framework have been designed which one is able to make a proper decision. A particular framework based on cognitive computing has already been proposed which can also be treated as brain powered cognitive IoT (M Chen, Herrera, & Hwang, 2018). In this system, data with huge complexity like EEG or any health-related data, etc. can be processed with intelligence. In this cognitive IoT enabled cloud-based system; the patient is directly linked with special sensors or devices which are always with him. In this system, devices with patient body collect information and monitor signals like EEG. Cognitive IoT system is able to analyze the information and make a decision about patients, which are helpful for hospital /practitioner. IoT enabled cloud-based service with smart sensors along with real-time data processing for health service in a smart city is presented in (M Shamim Hossain & Muhammad, 2016b; Zanella, Bui, Castellani, Vangelista, & Zorzi, 2014). On the other hand, the IoT based health system incorporated with edge computing cognitive technology presented in (Min Chen, Li, Hao, Qian, & Humar, 2018). Such kind of system makes possible of transmission of sensor data in real-time. The patient’s external expression and voice can be monitored under the IoT platform (M Shamim Hossain, 2016).